Method and related apparatuses for analysing a target region beneath a surface of a bed of a body of water using generated noise

Abstract

A method for determining one or more ground properties of a target region beneath a surface of a bed of a body of water, the method comprising: generating a noise signal; outputting noise by a noise source located within the body of water based on the noise signal; receiving a data set, the data set comprising: a first response signal, the first response signal indicative of the generated noise measured at or near the surface by a first receiver arranged at a first location; processing the first response signal; cross-correlating or deconvolving the processed first response signal; and using the cross-correlated or deconvolved first response signal to generate a two-dimensional '2D' or three- dimensional '3D' model of the target region in terms of the one or more ground properties based on the cross-correlated response signals. Unlocking insights from Geo-Data, the present invention relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.

Description

METHOD AND RELATED APPARATUSES FOR ANALYSING A TARGET REGION BENEATH A SURFACE OF A BED OF A BODY OF WATERUSING GENERATED NOISE FIELD[1] The disclosure relates to methods and systems for analysing a target region beneath a surface of a bed of a body of water using generated noise. More particularly, the disclosure relates to a method and system for determining one or more ground properties of a target region beneath a surface of a bed of a body of water based on generated noise output by a noise source at or near the surface based on a generated noise signal. Unlocking insights from Geo-Data, the present invention further relates to improvements in sustainability and environmental developments: together we create a safe and liveable world.  BACKGROUND [2] There is a general and ongoing need for systems and methods for determining subsurface ground parameters. In particular, there is a need for systems and methods that can be used to model the properties of a target volume beneath a surface a bed of a body of water to provide the information useful for infrastructure planning, such as but not limited to offshore foundation design, offshore infrastructure and offshore storage (for example, carbon storage) as well as infrastructure related to monitoring for carbon storage. Determination of subsurface ground properties during the early planning phase of construction projects reduces uncertainty during the location determination, foundation design, and construction phases of a project. This in turn reduces delays, overspend, and unnecessary use of material resources (e.g., concrete) during construction and an asset’s lifecycle.[3] One key parameter for the determination of ground characteristics in a volume of interest is shear-modulus and shear-velocity Vs. The shear-velocity Vs is the velocity at which a shear wave moves through the material and is controlled by the shear-modulus of the material. The relationship between shear-velocity and shear-modulus G is defined by Vs = √G / ρ, where ρ is the density of the material. Measurement of Vs therefore provides a valuable insight to the ground properties of a subsurface ground region. Small-strain shear modulus (Gmax) is also important in foundation design, wherein Gmax = ρ.Vs2.[4] Spectral analysis of surface waves (SASW) and multi-channel analysis of surface waves (MASW) are both examples of techniques for gathering surface wave information that can be used in the determination of ground properties in a subsurface volume. For completeness, surface waves are waves occurring at or near the earth’s surface. In both SASW and MASW, surface-level vibrations are measured, either from an ambient source (vibrations in the surface as a result of background sources of noise) or an active source (e.g., a weight drop on bed of a body of water on an airgun within the body of water near the bed), and the dispersion of the resulting surface waves is studied. ReMi (Refraction Microtremor) is another surface-level technique that uses ambient noise and surface waves to infer ground properties of a subsurface region based on the observation of ambient noise at the surface.[5] To obtain shear wave information in an offshore setting, interface or surface waves (used interchangeably herein, with examples including Scholte waves or Leaky Rayleigh waves) and pressure-to-shear converted waves (P-S) can be generated using an impulsive source, such as an airgun, located within a body of water, with the resulting interface, surface and / or shear waves induced in the bed of the body of water being recorded at the bed of the body of water or very close to it. [6] Impulsive sources like airguns are used to generate such interface or surface waves, for example, by towing them behind a vessel and triggering them within a body of water to induce interface, surface and / or shear waves in the bed of the body of water. Impulsive sources release acoustic energy within the body of water in a very short amount of time. They have limited control in terms of frequency content, repeatability, and the released pressure levels. They can also be detrimental to the marine environment, e.g., cause disturbance to the local fauna and contribute to a negative environmental footprint. Airguns and other impulsive sources can lead to devasting effects like injury, hearing loss, and behavioural changes to species in the surrounding marine environment.  SUMMARY [7] This summary introduces concepts that are described in more detail in the detailed description. It should not be used to identify essential features of the claimed subject matter, nor to limit the scope of the claimed subject matter. [8] According to a first aspect of the present disclosure, there is provided a method for determining one or more ground properties of a target region beneath a surface of a bed of a body of water. In some implementations, the method further comprises generating a noise signal. The step of generating a noise signal may be understood as forming a noise signal or creating a noise signal. The noise signal which is generated / formed / created is a representation of noise on the basis of which actual noise can be generated by a noise source, such as a speaker. In some implementations, the method further comprises outputting generated noise, by a noise source within the body of water based on the noise signal. As such, the noise signal, which is generated or formed or created, is used to output generated noise. That is, the generated noise, or outputted noise, or outputted generated noise (i.e., actual sound waves) are produced by a noise source on the basis of the noise signal. A noise source is configured to output generated noise based on a noise signal. The generated noise output of the noise source is referred to herein as “generated noise”. In some implementations, the method further comprises receiving a data set, the data set comprising: a first response signal, the first response signal indicative of the generated noise measured at or near the surface by a first receiver arranged at a first location. In some implementations, the method further comprises processing the first response signal. In some implementations, the method further comprises cross-correlating or deconvolving the processed first response signal. In some implementations, the method further comprises using the cross-correlated or deconvolved first response signal to generate a two-dimensional ‘2D’ or three-dimensional ‘3D’ model of the target region in terms of the one or more ground properties. [9] In some implementations, the data set further comprises a second response signal, the second response signal indicative of the generated noise measured at or near the surface by a second receiver arranged at a second location, wherein the first and second locations are different. In some implementations, the method further comprises processing the second response signal. In some implementations, the method further comprises cross-correlating or deconvolving the processed second response signal. In some implementations, the method further comprises performing an inversion using the cross-correlated or deconvolved first and second response signals to generate the two-dimensional ‘2D’ or three-dimensional ‘3D’ model of the target region in terms of the one or more ground properties.

[10] Advantageously, a non-invasive technique for measuring ground properties of a target region beneath a surface of a bed of a body of wate – such as a possible site for a new offshore construction – is thus provided. The advantages of being able to generate a 2D or 3D model of the ground properties of such a target region in a non-invasive way and without the use of impulsive noise sources such as airguns are numerous. It is simpler, faster, cheaper, less energy intensive, and less impactful on local environment and wildlife than existing intrusive methods for site analysis and methods that make use of impulsive noise sources. This is because less or no drilling and no impulsive noise stimulation is required. Additionally, and crucially, the disclosed method is both accurate and reliable, making it a practical and technically attractive technique. The disclosed method is especially well suited to use in offshore environments due to its low impact characteristics.

[11] The disclosed method is particular advantageous for providing targeted follow-on investigations. While some intrusive measurements may still be needed for calibration and ground-truth purposes, the method according to the present invention allows for detecting ground anomalies as well as targeting areas where ground anomalies are predicted instead of randomly determining ground properties by drilling across the site. This significantly reduces the number of intrusive investigations needed, which can be particularly challenging in an offshore context. The list of benefits according to the present invention thus include a faster delivery of the desired insights or properties of the target area and so: reduces the time required; reduces capital expenditure compared to conventional investigations beneath a surface of a bed of a body of water; allows the use of less heavy machinery and equipment through lighter and more sustainable engineering, and thereby improves safety of operations and minimizes environmental exposure risks too. In conclusion therefore, globally, the method according to the present invention allows for better risk management and / or risk transfer than conventional investigation methods for determining one or more ground properties of a target region beneath a surface of a bed of a body of water.

[12] Advantageously, the use of a generated noise output by a noise source can enable improved accuracy and precision of the determination of ground properties over the use of impulsive noise sources, such as airguns. Impulsive sources release acoustic energy in a very short amount of time, and they have limited control in terms of frequency content, repeatability, and the released pressure levels. These problems are addressed by the use of a generated noise output by a noise source.

[13] Furthermore, including a generated noise output, also referred to as a generated noise signal output, or a generated noise output, or a noise output, by a noise source, based on a noise signal, can enable further improvements to the resolving of ground properties of the subsurface of a target region. As will be discussed further herein, surface wave propagation is influenced by the physical properties of the subsurface. Surface waves at low frequencies are affected by physical properties deeper below the subsurface than surface waves at high frequencies. Noise from impulsive sources may be limited in frequency range and / or content at specific frequencies. As a result, this may negatively affect the quality of retrieved surface wave information and therefore the determination of ground properties at specific depths. By using a generated noise output by a noise source, the frequency content of the generated noise can be controlled meaning ground properties can be determined at a range of depths of interest and with improved efficiency. Additionally, using a generated noise output by a noise source can enable improved quality (e.g., accuracy and precision) of the determination of the ground properties by providing control over the intensity, location, frequency profile, duration, and noise profile of the noise that is output by the noise source, as will be discussed further herein. Finally, using a generated noise output by a noise source enables greater control over frequency, intensity and sound pressure levels used in geotechnical imaging. This facilitates signal generation at lower amplitude than impulsive sources, such as airguns, lowering the environmental impact of geotechnical imaging processes.

[14] In some implementations, the target region may span from 0 to 100m below the surface; 0 to 45m below the surface; or 50 to 100m below the surface. Further depths may also become target regions as the method according to the present invention presents little technical limits. Advantageously, the described approach is thus suitable for analysing a range of target region depths and profiles. This makes it very versatile.

[15] In some implementations, the target region may be a possible site for a new construction, or a tunnel. In some implementations, ground risk management framework decisions may be taken based on the 2D or 3D model. At an early stage of the construction project, the method according to the present invention provides important information and insights into the ground properties and thus provides the best opportunity to influence the outcome of a project at minimum cost of change. For example, plans for a subsea structure in or near the target region may be adapted based on the 2D or 3D model. The method may be carried out as part of a feasibility study – for example, early site screening – for building in or near the target region. By placing more emphasis on early site screening, uncertainty in building projects is reduced. This translates to significant material, time, and cost savings. For example, reduced uncertainty leads to better risk management, which in turn leads to the avoidance of excessive over engineering in projects. This can help reduce material usage, such as concrete and steel usage, which has significant environmental benefits. Furthermore, early site screening helps inform further analysis of the target region so that any further analysis, which may be intrusive in character, can focus only on those regions allegedly identified as problematic or only on certain regions of particular interest.

[16] In some implementations, the noise source may be a vibratory noise source. Using a vibratory noise source enables greater control over frequency, intensity and sound pressure levels used in geotechnical imaging. This facilitates signal generation at lower amplitude than impulsive sources, such as airguns, lowering the environmental impact of geotechnical imaging processes. Examples of vibratory noise sources that can be used in the systems and methods described herein include transducers and low frequency electrodynamic sound projectors.

[17] In some implementations, the step of cross-correlating the processed first response signal and / or the processed second response signal comprises cross-correlating the processed first response signal with the generated noise and / or cross-correlating the processed second response signal with the generated noise.

[18] In some implementations, the first response signal further is indicative of ambient noise (in addition to the generated noise) measured at or near the surface of the bed of the body of water by a first receiver arranged at the first location. In some implementations, the second response signal further is indicative of ambient noise (in addition to the generated noise) measured at or near the surface of the bed of the body of water by a second receiver arranged at the second location. In some implementations, the two-dimensional ‘2D’ or three-dimensional ‘3D’ model of the target region in terms of the one or more ground properties is generated by performing an inversion using the cross-correlated response signals. In some implementations, the step of cross-correlating the processed first response signal and / or the processed second response signal comprises cross-correlating the processed first response signal with the processed second response signal.

[19] In some implementations, the inversion is one of a tomographic inversion and a full-waveform inversion.

[20] Ambient noise, or seismic ambient noise, may be generated by one or more ambient sources. The ambient sources may be natural (that is, naturally occurring vibrations) or cultural (that is, vibrations from human activity). For example, the ambient sources may include ocean(s) (for example, tidal or wave noise), wind, industry, industrial machinery, vehicles such as cars or trains, and human noise (for example, footsteps). Analysing one or more ground properties of the target region based on ambient noise is a practical, convenient, energy efficient and low-cost approach.

[21] The present method includes a noise source that generates noise which may be similar to, the same as, or shares characteristics with the ambient noise generated by the ambient sources. In particular, such a noise source may be included as part of the disclosed methods to generate noise to be received / measured by the receivers. As will be discussed further herein, by generating noise that is similar to ambient noise, there is less impact on local environment and wildlife compared to more intrusive methods and methods that make use of impulsive noise sources, such as an airgun. At the same time, such noise sources can be used to supplement the existing ambient noise, which enables the determination of the one or more ground properties of a target region beneath a surface of the earth to be improved. A noise source is configured to output generated noise based on a noise signal. The generated noise output of the noise source is referred to herein as “generated noise”.

[22] In some implementations, the receivers used in the present methods may be geophones, accelerometers (such as vertical or triaxial accelerometers), particle velocity sensors, fibre-optic based sensors, seismometers, vibration sensors, pressure sensors, vibration sensors, hydrophones and / or transducers or an array of any of these types of receivers. In some implementations, the receivers may collect data over a significant period of time. For example, the ambient noise and / or generated noise may be measured consecutively over a period of five days. This longer recording time results in the adequate retrieval of surface wave information from the noise recorded at or near the surface of the target region. The recording time required to obtain adequate retrieval of surface wave information is dependent on the quality and quantity of the noise measured by the receivers. In scenarios where surface wave information is insufficient, a longer recording time may be used to compensate for this by summing surface wave information together to produce a stronger signal. Advantageously, the use of generated noise output by a noise source can provide the adequate surface wave information instead of requiring recording time to be prolonged. In fact, incorporating use of generated noise output by a noise source can in most cases reduce the reduce the time necessary to obtain adequate retrieval of surface wave information. As such, improved operational efficiency, i.e., more effective use of time, human resources, and hardware resources are enabled, while minimising disruption / impact on local environment, wildlife, and communities.

[23] In turn, and as described in more detail below, the (processed) surface wave information can be used in an inversion, such as a tomographic inversion, to obtain a shear wave velocity model of the subsurface.

[24] As will be apparent to the skilled person, the noise source used in the present methods may be any device capable of generating vibrations. In some implementations, the noise source can output generated noise for an extended period of time.

[25] Each response signal may be indicative of the Scholte, Rayleigh waves and / or Love waves caused by the generated noise (and, in some implementations, ambient noise).

[26] Each of the first and second response signals may be indicative of the vertical and / or horizontal component of the generated noise (and, in some implementations, ambient noise) measured at or near the surface by the first and second receiver, respectively.

[27] In implementations where the first response signal and the second response signal are indicative of both ambient noise and the generated noise, while the ambient noise and the generated noise originate from various sources, they may be considered / received together by the first and the second receiver as a single response signal, respectively. In other words, the first and the second receiver may not be aware of the source of the noise, whether it is by an ambient source or by a noise source.

[28] In some implementations, the method may comprise, before the step of receiving the data set, determining the locations for the first and second receivers. The locations may be locations on or near the surface. The locations may be based on the minimum and / or maximum depth of the target region and / or the desired resolution of the waves in the target region caused by the generated noise (and, in some implementations, ambient noise). By determining the locations for the receivers based on the minimum and / or maximum depth of the target region and / or the desired resolution of the noise waves in the target region, the first and second receivers are best placed to measure the noise waves at their most sensitive. This contributes to the accuracy of the response signals and thus the resulting 2D or 3D model.

[29] In some implementations, there may be more than two receivers. The receivers may be arranged in an array (line or grid), or otherwise arranged to cover the target region of interest.

[30] In some implementations, the method may comprise, before the step of receiving the data set, selecting a recording frequency for the first and second receivers. The recording frequency may be selected based on the depth of the target region and / or the expected wavelength of the noise waves in the target region. The recording frequency may be called a target recording frequency and may be a range.

[31] In some implementations, one or both of the first receiver and second receiver are coupled to a tow cable of a vessel on or in the body of water. The vessel is able to control the location of the first receiver and second receiver relative to the surface of the bed of the body of water, for example, to optimise the response signals obtained by the receivers.

[32] In some implementations, the noise source is coupled to a tow cable of a vessel on or in the body of water. The vessel is able to control the location of noise source relative to the surface of the body of water, for example, to optimise the generation of Scholte waves, Rayleigh waves and / or shear waves in the target region.

[33] In some implementations, the noise source is coupled to a structure on the surface of the bed of the body of water.

[34] In some implementations, the vessel is a ship, an uncrewed surface vessel, USV, or a remotely operated vehicles ROV.

[35] In some implementations, one or both of the first receiver and second receiver are arranged on the surface of the bed of the body of water.

[36] In some implementations, the noise source is coupled to a tow cable of a further vessel on or in the body of water.

[37] In some implementations, the further vessel is a ship, an uncrewed surface vessel, USV, or a remotely operated vehicles ROV.

[38] In some implementations, the noise source is coupled to a tow cable of the same vessel as one or both of the first receiver and second receiver.

[39] In some implementations, one or both of the first receiver and second receiver are at least one of pressure sensors, geophones, hydrophones, accelerometers (such as vertical or triaxial accelerometers), particle velocity sensors, fibre-optic based sensors, seismometers, vibration sensors, and / or transducers, or an array of any of these types of receivers.

[40] In some implementations, the step of outputting generated noise by the noise source located within the body of water based on the noise signal comprises generating a pressure wave in the body of water incident on the surface of the bed of the body of water.

[41] In some implementations, the pressure wave is incident on the surface of the bed of the body of water at an angle within the range of 10 to 40 degrees, optionally, at an angle of 30 degrees. This angle has been found to optimise the generation of Scholte, Rayleigh, P- and / or S-waves at the surface and in the target region.

[42] In some implementations, the noise source comprises an array of noise sources configured to form a beam of noise incident on the surface of the bed of the body of water. This enhances the generation of Scholte, Rayleigh, P- and / or S-waves at the surface and in the target region.

[43] In some implementations, the beam of noise is incident on the surface of the bed of the body of water at an angle within the range of 10 to 40 degrees, optionally, at an angle of 30 degrees. This angle has been found to optimise the generation of Scholte, Rayleigh, P- and / or S-waves at the surface and in the target region.

[44] In some implementations, the first response signal and / or second response signal comprise Scholte waves, Rayleigh waves and / or shear waves generated in the target region by the generated noise.

[45] In some implementations, the noise source, the first receiver, and / or the second receiver move relative to the surface of the bed of the body of water.

[46] In some implementations, processing the first and / or second response signals comprises applying a motion correction operation to account for the movement of the noise source, the first receiver, and / or the second receiver relative to the surface of the bed of the body of water. Using motion correction to account for relative movement of noise source / receivers, improves the accuracy of the method.

[47] In some implementations, the method may comprise, before the step of receiving the data set, receiving an initial data set, the initial data set comprising an initial first response signal indicative of the ambient noise measured at or near the surface by the first receiver and / or an initial second response signal indicative of the ambient noise measured at or near the surface by the second receiver. In some implementations, the method may further comprise determining an ambient noise profile of the target region indicative of the direction and intensity of the ambient noise received at the target region based on a comparison between the initial first response signal and the initial second response signal. In some implementations, the initial first and second response signals measured at or near the surface respectively by the first and / or second receivers are indicative of both ambient noise and generated noise. In some implementations, the initial first and / or second response signals measured at or near the surface respectively by the first and second receivers are indicative of only ambient noise (from the natural or cultural sources). In some implementations, the method comprises determining the ambient noise profile of the target region using beamforming.

[48] Advantageously, by determining the direction and intensity of the ambient noise, locations for arranging the noise sources can be identified, which enables improved operational efficiency and efficient use of hardware resources. Specifically, taking into consideration only ambient noise can enable determination of initial locations to arrange the noise source. Similarly, taking into consideration both ambient noise and generated noise, the placement of the noise source can refined / rearranged accordingly. In this way, utilising the first receiver and the second receiver (or in some implementations an array of receivers) enables utilisation of the existing receivers to identify the ambient noise profile of a target region.

[49] In some implementations, the noise signal is generated or formed using a pseudorandom binary sequence. Advantageously, pseudorandom binary sequences are deterministic and may be generated efficiently using simple underlying hardware implementation. Additionally, uncorrelated signals can be efficiently / easily generated from pseudorandom binary sequences, which enables determining of Green’s functions efficiently / faster especially in the case of noise that is output simultaneously by one or more noise sources, as will be described further herein. Likewise, the use of pseudorandom binary sequences can enable data acquisition time to be shortened, thereby providing effective use of time and hardware resources. Moreover, pseudorandom binary sequences have similar acoustic properties to white noise, which has the advantage of reducing impact / disruption to the environment and wildlife.

[50] In some implementations, the pseudorandom binary sequence is at least one of a maximum length sequence, Gold sequence, or Kasami sequence. In some implementations, any pseudorandom binary sequence can be used, which shares characteristics with noise like properties, e.g., white noise, or other colours of noise. In some implementations, any combination of the aforementioned pseudorandom binary sequences may be used to generate a noise signal. Advantageously, maximum length sequences, Gold sequences, and Kasami sequences are sequences that each respectively have low cross-correlation with other sequences in the same set, which enables efficient determination of Green’s functions and can reduce data acquisition time.

[51] In some implementations, the noise signal is generated to contain frequency content with a specific frequency range of 2-120 Hz. Advantageously, this ensures the noise signal includes the frequency content required to generate Scholte waves, P-Waves and / or S-Waves for geotechnical imaging at the surface and / or within the target region.

[52] In some implementations, the noise signal is modulated to a specific frequency range. Advantageously, by modulating the noise signal to a specific frequency and / or frequency ranges, specific depth(s) of the target region can be targeted. In some implementations, the specific frequency range is 2-120 Hz or, optionally, 5-100 Hz.

[53] In some implementations, the noise signal is generated using a random number generator. By generating a noise signal using different seeds for the random number generation, noise based on one noise signal can be generated to be uncorrelated with noise based on another signal. This enables improved determination of the Green’s functions and shortens data acquisition time, especially in the case of noise that is output simultaneously by one or more noise sources.

[54] In some implementations, the noise signal is filtered to attenuate frequencies outside a specific frequency range. By filtering the noise signal to attenuate frequencies outside a specific frequency range, similar to the aforementioned modulation of a noise signal, the noise signal can be controlled to increase the energy / intensity of the noise at specific frequencies or frequency ranges. In some implementations, the specific frequency range is 2-120 Hz or, optionally, 5-100 Hz. Advantageously, this ensures the noise signal includes the frequency content required to generate Scholte waves, P-Waves and / or S-Waves for geotechnical imaging at the surface and / or within the target region.

[55] In some implementations, the noise signal is output at an intensity which is based on, matches, is equal to, or is audibly equal to the average intensity of ambient noise received at the first receiver and / or second receiver. This enables a noise source to output generated noise at an intensity that minimises disruption / impact on local environment and wildlife.

[56] In some implementations, the noise signal output by the noise source is limited to a threshold output level. The threshold output level can, in some implementations, be the average intensity of ambient noise across all directions in the ambient noise profile, or, in some implementations, be the aforementioned average intensity of ambient noise received at the first receiver and / or second receiver. However, as will be discussed further herein, the threshold output level can be any suitable threshold, such as the highest ambient noise recorded, based on an environmental standard / regulation, and so forth. In some implementations, the threshold output level can comprise one or more threshold output sublevels according to one or more frequency values or frequency ranges of the generated noise / noise signal.

[57] In some implementations, the noise source can be moved to different locations during the method and the step of outputting noise by a noise source located within the body of water based on the noise signal may comprise outputting noise by a noise source at multiple locations within the body of water.

[58] In some implementations, the noise source comprises a plurality of noise sources. In some implementations, a second noise source can be provided at a different location to the first. In some implementations, the second noise source comprises a plurality of noise sources. Advantageously, by including a noise source that comprises a plurality of noise sources, the plurality of noise sources can further improve the accuracy and precision of the determination of ground properties, as will be further discussed herein.

[59] In some implementations, the plurality of noise sources is configured to output the noise signal sequentially. By having the plurality of noise sources output the noise signal sequentially (i.e., one at the time following a predefined or in some implementations random order), this ensures the generated noise signals are not correlated with each other as they do not overlap in time. This enables efficient determination of the Green’s functions and can shorten data acquisition time. As such, even if the noise signal output by each of the plurality of noise sources are identical, there is no correlation between the noise sources.

[60] In some implementations, the noise signal output at each noise source of the plurality of noise sources are different. That is, the noise signal output by each of the plurality of noise sources are not identical to each other. Advantageously, having each of the plurality of noise sources output generated noise based on a different noise signal can provide output of noise that is similar, mimics, or shares characteristics with the ambient noise.

[61] In some implementations, the different noise signal output at each noise source of the plurality of noise sources are not correlated with each other. Advantageously, using uncorrelated noise signals for output by each of the plurality of noise sources can enable efficient determination of the Green’s functions and shorten data acquisition time.

[62] In some implementations, the one or more ground properties may comprise one or more elastic properties of the target region, such as the shear-velocity, Vs. As explained above, retrieving the shear-velocity of the target region provides a valuable insight to the ground properties of the target region, such as the small-strain shear modulus of the target region. This allows engineers to identify areas of weakness in the target region beneath a surface of a bed of a body of water, say, or lateral variations in the geology. For the reasons provided above, the earlier on in the project lifecycle that such characteristics can be identified, the better.

[63] In some implementations, the step of processing may comprise processing the first and / or second response signals to accentuate the representation of the received noise. Put another way, to accentuate the broad band characteristics of the received noise. For example, by removing the instrument response and / or by filtering out large amplitudes. Such large amplitudes may be caused by (undesired) signals from earthquakes. Advantageously, this prevents large amplitude events from overpowering the generated noise (and ambient noise, if present) response that is of interest.

[64] Additionally, or alternatively, the step of processing may, in some implementations, comprise one or more of: splitting each response signal into segments; trimming each (optionally, segmented) response signal to the nearest second; applying a low-pass filter to each response signal; and / or downsampling each response signal. In some implementations, the method may comprise downsampling each response signal by a factor that is an integer number. For example, the method may comprise downsampling the at least two response signals by a factor of around 10 (for example, by a factor of 10). The advantage of segmenting and trimming the data is that this allows response signals measured by different receivers to be cross-correlated and stacked to obtain an estimate of the Green’s function between the two receivers. The advantage of applying a low-pass filter is that this reduces the frequency content of each response signal to only those frequencies which can be retrieved unaliased after the subsequent cross-correlation step. Aliasing occurs when the signal is not sampled properly enough to reconstruct the waveform for certain frequencies The advantage of downsampling is that this reduces the computational cost of the method and the total time required to generate the model.

[65] In some implementations, the step of cross-correlating the processed response signals may comprise estimating the Green’s function between the first and second receivers. In other words, the step of cross-correlating the at least two processed response signals may comprise simulating the wavefield that would be recorded at one of the first and second receivers if the other of the first and second receivers were a virtual source. For completeness, the step of cross-correlating may be applied to the segmented and / or downsampled and / or trimmed response signals. Advantageously, by cross-correlating the response signals, the similarities between the response signals are analysed. This is an important step towards generating the model.

[66] In some implementations, the step of performing a tomographic inversion may comprise using only fundamental mode surface waves in the inversion. Advantageously, using only the fundamental mode surface waves results in a good level of accuracy for the model.

[67] In some implementations, the inversion may be performed in the time domain or the frequency domain. Usefully, having the choice between the time domain or the frequency domain means that the domain resulting in the shorter processing time can be selected.

[68] In some implementations, the step of performing tomographic inversion may comprise using a combination of tomography and inversion.

[69] In some implementations, the step of performing tomographic inversion may follow a one-step approach or a two-step approach.

[70] In some implementations, the step of performing tomographic inversion may comprise providing an initial model of the one or more ground properties of the target region; providing a noise input for the initial model; calculating, using the initial model, the travel time of the response signal that would be measured at the first and second receivers; comparing the travel time calculated using the initial model to the cross-correlated first and second response signals; and based on the comparison, updating the initial model to generate the 2D or 3D model.

[71] In some implementations, the step of providing the initial model may comprise determining the phase velocity dispersion profile between a virtual source and one of the first and second receivers.

[72] In some implementations, the step of providing the initial model may comprise determining the (for example, 2D or 3D) structure of the initial model by computed tomography of two or more phase velocity dispersion profiles selected between different virtual sources and different receivers.

[73] In some implementations, the 2D model may be a 2D expression of the shear-velocity of the target region. The 3D model may be a 3D expression of the shear-velocity of the target region.

[74] In some implementations, the method may be a computer-implemented method.

[75] In some implementations, the method may be used in combination with one or more further techniques for analysing surface waves, such as SASW or MASW. Usefully this allows for analysing the subsurface over a larger depth range.

[76] According to another aspect, there is provided a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method of the first aspect or any method described herein.

[77] According to a further aspect, there is provided apparatus configured to perform the method of the first aspect or any method described herein.

[78] In some implementations, the apparatus may be a system comprising: one or more processors; one or more memories having stored thereon computer readable instructions configured to cause the one or more processors to perform operations comprising the steps of the first aspect. According to still another aspect, there is provided computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of the first aspect or any method described herein.

[79] According to another aspect, there is provided a system comprising: a noise source, a first receiver (optionally, a second receiver), one or more processors; and one or more memories having stored thereon computer readable instructions configured to cause the one or more processors to perform operations to control the system to perform the steps of the first aspect or any method described herein.

[80] According to another aspect, there is provided a system comprising: a noise source, a first receiver (optionally, a second receiver), a vessel, wherein the first receiver and / or second receiver are coupled to a the vessel. In some implementations, the noise source is coupled to a further vessel on or in the body of water. In some implementations, the noise source is coupled to the same vessel as first receiver and / or second receiver. In some implementations, vessel and / or the further vessel are a ship, an uncrewed surface vessel, USV, or a remotely operated vehicles ROV. In some implementations, the system further comprises one or more processors; and one or more memories having stored thereon computer readable instructions configured to cause the one or more processors to perform operations to control the system to perform the steps of the first aspect or any method described herein.  BRIEF DESCRIPTION OF THE DRAWINGS 

[81] In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary implementations of the disclosure and are therefore not to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:

[82] Figure 1 shows an example system for collecting data indicative of generated noise at a surface of a bed of a body of water;

[83] Figure 2 shows an alternative example system for collecting data indicative of generated noise at a surface of a bed of the body of water;

[84] Figure 3 shows a cross-sectional view of a subsurface target region;

[85] Figure 4 shows a plurality of geophones arranged in a 2D array at a surface above a subsurface target region;

[86] Figure 5 is a flow chart showing an example method for analysing one or more ground properties of a target region;

[87] Figure 6 is a flow chart showing an example method for performing tomographic inversion that can optionally be used in the method of Figure 5;

[88] Figure 7 is a perspective view of a shear wave velocity model;

[89] Figure 8 is an example of a shear wave velocity model resulting from the methods described herein; and

[90] Figure 9 shows a block diagram of one implementation of a computing device which can be used to perform the methods described herein.

[91] Throughout the description and drawings, like reference numerals refer to like features.  DETAILED DESCRIPTION 

[92] The following is a description of certain embodiments of the invention, given by way of example only and with reference to the drawings.

[93] Various implementations of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the disclosure. Thus, the following description and drawings are illustrative and are not to be construed as limiting. Numerous specific details are described to provide a thorough understanding of the disclosure. However, in certain instances, well-known or conventional details are not described in order to avoid obscuring the description. A reference to an implementation in the present disclosure can be a reference to the same implementation or any other implementation. Such references thus relate to at least one of the implementations herein.

[94] The terms used in this specification generally have their ordinary meanings in the art, within the context of the disclosure, and in the specific context where each term is used. Alternative language and synonyms may be used for any one or more of the terms discussed herein, and no special significance should be placed upon whether or not a term is elaborated or discussed herein. In some cases, synonyms for certain terms are provided. A recital of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification including examples of any terms discussed herein is illustrative only and is not intended to further limit the scope and meaning of the disclosure or of any example term. Likewise, the disclosure is not limited to various implementations given in this specification. References to ranges of values or values “between” two values should be interpreted as encompassing the end points of those ranges unless otherwise specified.

[95] A method for analysing one or more ground properties, such as the shear velocity, of a target region beneath a surface of a bed of a body of water will now be described. In short, the method involves receiving a data set indicative of generated noise at the surface of the target region, as measured at or near the surface by receivers; performing various operations on the data; and, ultimately, generating a 2D or 3D model of the target region in terms of the one or more ground properties. As has already been described, the ability to generate a 2D or 3D model of a target region – such as a candidate site for a new construction – in terms of its shear velocity, for example, provides a valuable insight into the composition of the target region. In turn, this can be used to influence subsequent site surveys and construction decisions.

[96] In some examples, ambient noise can be received alongside the generated noise. Ambient noise is generated from various ambient sources. The ambient sources fall under two categories: natural and cultural. Natural sources are those such as oceans or the wind which cause naturally occurring vibrations. Cultural sources are those which stem from human activity, such as industry, industrial machinery, vehicles such as cars or trains, power lines, and human noise.

[97] Generated noise is generated based on a generated noise signal and is output by a noise source at or near the surface of a target region. The generated noise is similar to, the same as, or shares characteristics with the ambient noise generated by ambient sources.

[98] More specifically, the received data set includes response signals. Each response signal is measured at or near the surface by a respective receiver. The response signals are indicative of the generated noise (and ambient noise, if present) that was transmitted from various sources (these could be one or more noise sources for the generated noise and cultural and / or natural sources for the ambient noise, as explained more below) through the subsurface target region and measured by the respective receiver. In particular, the vibrations resulting from surface waves or interface waves (e.g., Scholte waves or Leaky Rayleigh waves) and pressure-to-shear converted waves (P-S), generated by generated noise output by one or more noise sources incident on the surface, are measured.

[99] Surface waves may also be generated by cultural or natural processes occurring at or near the surface (i.e., ambient noise), as well as generated by generated noise output by one or more noise sources. The elliptical motion and dispersive properties of the surface waves allow us to retrieve information about the ground, for example shear, properties of the subsurface region.

[100] The receivers, which may also be considered to be sensors, may be geophones, accelerometers (such as vertical or triaxial accelerometers), particle velocity sensors, fibre-optic based sensors, seismometers, pressure sensors, vibration sensors, hydrophones and / or transducers or an array of any of these types of receivers.

[101] The noise source may be any device capable of generating vibrations through a medium (or media). In some implementations, the noise source can output generated noise for an extended period of time. In some implementations, the noise source is a vibratory noise source or vibrator (used interchangeably herein), which is electromagnetically driven, hydraulically driven, or otherwise driven to generate vibrations. In some implementations, the noise source is a speaker, a woofer, a subwoofer, a buzzer (e.g., piezoelectric), a tweeter, or any other device capable of producing sound, vibrations, or seismic activity. In some implementations, the noise source is a combination of one or more of the aforementioned devices. The noise source can be placed at or near the surface of a target region, though in some implementations, the noise source can be placed at a distance from the target region.

[102] Information useful for understanding the present invention will now be provided.

[103] First, an explanation of shear modulus and its usefulness in building and infrastructure projects will be given. Shear modulus is a measure of the elastic shear stiffness of a material and represents the deformation of a solid when it experiences a force parallel to one of its surfaces while its opposite face experiences an opposing force. Such forces and their effects in subsurface ground volumes (such as the target region of the method described in connection with Figure 3), are an important parameter for study before and during the design of land based and offshore building and infrastructure projects. To determine the shear modulus of a volume, the shear velocity, Vs, is determined. This in turn gives an indication of the stiffness of the subsurface material, and its ability to support structures extending above and / or through the volume.

[104] Next, an explanation of wave types will be given. In the context of ground study, two types of waves are generally distinguished: P-waves and S-waves. In P-waves, particles in the volume oscillate in the direction of movement of the wave, which causes a compression and de-compression of the ground as the waves propagate through the ground. Meanwhile S-waves are shear waves, in which particles oscillate in a direction perpendicular to the direction of propagation of the waves.

[105] P-waves and S-waves are body waves and propagate in all directions through the body of the volume. The interaction of P- and S-waves with the earth’s surface generates surface waves, which propagate along that surface. Several types of surface waves can be distinguished. In the systems and methods described herein, Scholte waves are measured and studied because it is convenient to measure the vertical component of surface vibrations. They also provide S-wave information can be measured at the surface of a bed of a body of water (e.g., a seabed), as well as within the body of water close to the surface of the bed. However, it will be appreciated that other surface waves (e.g., Rayleigh and / or Love waves) may be measured and harnessed in the systems and methods described herein. Scholte waves are dispersive waves (different frequencies propagate at different velocities) that travel along the water / sediment (e.g., seabed) interface. The systems and methods described herein can be adapted to measure Scholte and / or Rayleigh waves and / or Love waves by using single component receivers or multi-component receivers.

[106] Because surface waves propagate in 2D (at the surface), they attenuate less rapidly than body waves (which propagate in 3D). Surface waves are generally present within a depth range of one wavelength from the surface, generally travel more slowly and have a predominantly lower frequency than body waves.

[107] This lower attenuation, slower travel time and lower frequency of surface waves makes their study particularly attractive for the purposes of determining shear velocity, Vs. Since the surface waves have lower attenuation, the signal strength is better maintained over a longer travel distance. The resulting measurement results therefore generally have a higher signal quality (signal-to-noise ratio) than body wave studies.

[108] Some of the subsequent examples will be described in the context of receivers and arrays of receivers, such as geophones and a geophone array, although any other suitable receivers of the types mentioned herein could be used. It will, however, be appreciated that the disclosed systems and methods are applicable to a variety of receiver (that is, sensor) types, including but not limited to geophones, accelerometers (such as vertical or triaxial accelerometers), particle velocity sensors, fibre-optic based sensors, seismometers, vibration sensors and / or transducers.

[109] An example system for collecting data indicative of generated noise at a surface of a bed of a body of water will now be described in reference to Figure 1. Figure 1 shows a vessel 102 on a body of water 110. A remotely operated vehicle, ROV, 104 has been deployed by the vessel 102 and is operating within the body of water 110. Coupled to the ROV 104 is a noise source 106 directing noise 114 at a surface 112 of the bed of the body of water 110 above a subsurface volume 118 (such as the target region of the method described in connection with Figure 5) within the bed of the body of water 110. Finally, a plurality of receivers 108 are coupled to a tow cable of the vessel 102. The plurality of receivers 108 may comprise at least one of pressure sensors, geophones, accelerometers (such as vertical or triaxial accelerometers), particle velocity sensors, fibre-optic based sensors, hydrophones, seismometers, vibration sensors and / or transducers, or an array of any of these types of receivers.

[110] The impact of the noise 114 at a surface 112 of a bed of the body of water 110 generates Scholte waves 116 that are detected by the plurality of receivers 108 at or near the surface 112 of a bed of the body of water 110 above the subsurface volume 118. Alternatively, or in addition to Scholte waves, it is also possible for the impact of the noise 114 at a surface 112 to generate and acquire and P- and S- waves 116 for high resolution sediment characterisation, for example, in a shallow water environment. P-waves, excited in the body of water 110, can convert to S- waves at the bed of the body of water 110 or at any interface where the elastic properties of the soil changes (e.g., sediment layers). These various converted modes can be recorded by receivers 108. A more detailed discussion of this process is described in relation to Figure 5.

[111] The plurality of receivers 108 depicted in Figure 1 may be coupled to a streamer that is towed through the body of water 110 via a tow cable coupled to the vessel 102. In this way, the plurality of receivers 108 may be towed close to or along the surface 112 in order to detect Scholte waves, P- waves, and / or S- waves 116 propagating in the subsurface volume 118.

[112] Figure 2 shows an alternative system for collecting data indicative of generated noise 114 at the surface 112 of the bed of the body of water 110, in which like reference numerals with respect to Figure 1 denote the same features. The difference here is that the plurality of receivers 202 are located on the surface 112 of the bed of the body of water 110 above the subsurface volume 118. The plurality of receivers 202 may comprise at least one of pressure sensors, geophones, hydrophones, accelerometers (such as vertical or triaxial accelerometers), particle velocity sensors, fibre-optic based sensors, seismometers, vibration sensors and / or transducers.

[113] The vessel 102 of Figures 1 and 2 could be any suitable vessel or a ship, including an uncrewed surface vessel (USV) or an ROV, located on or in the body of water 110. The vessel 102 may be controlled by an operator located at the vessel 102 or in a remote operating centre (not depicted). The vessel 102 could also be autonomously controlled.

[114] The plurality of receivers 202 depicted in Figure 2 are located on the surface 112 of the bed of the body of water 110 above the subsurface volume 118 in order to detect Scholte waves, P- waves, and / or S- waves 116 propagating in the subsurface volume 118.

[115] The ROV 104 of Figures 1 and 2 may be controlled from the vessel 102, for example by a cable (as depicted in Figures 1 and 2) or via wireless communication (not depicted). In another example, the ROV 104 may be controlled by an operator located at the vessel 102 or in a remote operating centre (not depicted).

[116] The noise source 106 of Figures 1 and 2 may be any device capable of generating vibrations through a medium (or media). In some implementations, the noise source can output generated noise for an extended period of time. In some implementations, the noise source is a vibratory noise source or vibrator (used interchangeably herein), which is electromagnetically driven, hydraulically driven, or otherwise driven to generate vibrations. In some implementations, the noise source is a speaker, a woofer, a subwoofer, a buzzer (e.g., piezoelectric), a tweeter, or any other device capable of producing sound, vibrations, or seismic activity. In some implementations, the noise source is a combination of one or more of the aforementioned devices. The noise source can be placed at or near the surface of a target region, though in some implementations, the noise source can be placed at a distance from the target region.

[117] The noise source 106 of Figures 1 and 2 could be an array of noise sources (as depicted in Figures 1 and 2) and the nature of the noise sources is discussed in greater detail below. The location and direction of the noise source 106 may be altered by controlling movement of the ROV 104. In an alternative example, the noise source 106 can be coupled directly to the vessel 102 via, for example, a tow cable. The noise source 106 is capable of generating noise 114 in the form of a pressure wave in the body of water 110 incident on the surface 112.

[118] In some implementations, there may be multiple ROVs 104 provided in the systems of Figures 1 and 2, each with a respective noise source 106.

[119] In some implementations, there the noise source 106 may be coupled to a structure (not depicted) on the surface 112 of the bed of the body of water 110, rather than being coupled to the ROV 104, as depicted in Figures 1 and 2 or the vessel 102.

[120] An example system for collecting data indicative of the ambient noise and / or generated noise 114 at the surface 112 of a target region 118 will now be described in reference to Figure 3. Figure 3 shows an example cross-sectional view of the subsurface volume 118 of Figures 1 and 2, which may also be the target region of the method described in connection with Figure 5. The receivers 202 of Figure 2 are shown in Figure 3 in the form of geophones (although any suitable receivers may be used). P- and S- waves travel through the volume 118 as body waves. The surface 112 extends above the subsurface volume 118. Surface waves propagate along the surface 112.

[121] At point A, a schematic representation of a particle oscillation (due to Scholte or Rayleigh wave propagation) at a surface above a target subsurface volume is shown. As illustrated, the oscillation of the particle P is partly vertical and partly in the direction of propagation. The resulting particle movement is therefore substantially ellipsoid.

[122] At a surface 112 above the volume 118, a plurality of geophones 202a, 202b are arranged. Geophones 202a, 202b, located at surface 112 can be configured to measure the vertical component of the oscillation shown schematically at point A.

[123] The geophones 202a, 202b, are arranged in a grid array at the surface, the grid extending in two directions. It should be noted that the surface above the target region may, in many cases, not be planar. The array of geophones 202a, 202b, may therefore not be truly “two-dimensional” because each geophone may be offset from its neighbours in the grid in the z-direction. However, such a grid arrangement of geophones will be referred to as a 2D array herein.

[124] A surface wave travelling across surface 112 will cause vertical movement at a plurality of geophones 202a, 202b, as waves travel across the surface.

[125] Near surface 112 and volume 118 is the noise source 106 configured to output generated noise based on a generated noise signal.

[126] Similar concepts as described in relation to Figure 3 apply to the receivers 108 shown in Figure 1. The plurality of receivers 108 at or near the surface 112 are able to detect vertical movement of the surface by a surface wave travelling across surface 112. The receivers 108 shown in Figure 1 can detect surface waves as follows. The pressure and particle motion at the interface between water and the seabed are continuous. If an interface or surface waves is excited, it will propagate along the interface, but it will cause a pressure difference in the water. These pressure variations can be detected the receivers 108, for example, where the receivers 108 are pressure sensors, such as hydrophones. The pressure variation changes exponentially with distance from the seabed. To account for this, in some implementations, the sensors receivers 108 may be towed close to the seabed. Moreover, the pressure variation changes with depth can also be measured using vertical accelerometers or particle velocity sensors. As such, in some implementations, receivers 108 may be vertical accelerometers or particle velocity sensors.

[127] It will now be described, in general terms, how the shear velocity, Vs, can be determined from the observation of surface waves (in particular Scholte waves). This is achieved by measuring the dispersive behaviour of the surface waves. Surface waves are dispersive, which means that their velocity is dependent on frequency. Usually, seismic velocities increase with depth in the earth. As a consequence, normal surface wave dispersion shows a decrease of surface-wave velocity with increasing frequency. It is by studying the behaviour of surface waves at a surface above a volume that the ground properties of the volume can be determined.

[128] There are two ways to measure the velocity in dispersive surface waves and a distinction is made between the determination of group velocity or phase velocity.

[129] The group velocity of a wave is the velocity with which the overall envelope shape of the wave's amplitudes – known as the modulation or envelope of the wave – propagates through space. The group velocity is equivalent to the speed with which the energy of the wave propagates through the volume. The group velocity is measured by determining the wave propagation between a (synthetic) transducer pair and is a frequency-dependent point property in the volume, which is dependent on depth. The group velocity is obtained as a time of flight (that is, travel time) measurement between a virtual source and a receiver.

[130] Meanwhile, the phase velocity is the velocity at which the phase of any one frequency component of the wave travels. The phase velocity is the speed with which each frequency component of a wave travels. As such, the phase velocity is expressed as a function of frequency. To measure the phase velocity, at least two measurement nodes are chosen to measure the waves propagating through the volume to determine relative time of flight between the geophones for different frequencies. The result is the phase velocity as a function of frequency as averaged over the volume between the two measurement nodes. Phase velocity is acquired as a point in 2D phase space (dispersion spectrum) which is obtained by a 2D transform (such as slant-stack, Radon, FK, or the like) of an array of recorded waveforms (time-distance space).

[131] Referring again to Figure 3, each geophone 202a, 202b, provides a measurement node for measuring the vertical component of passing surface waves. The geophones 202a, 202b, can be configured to measure vibrations due to ambient noise and / or generated noise 114. For completeness, as previously described, ambient noise is the background wavefield due to natural or man-made / human noise (rather than an impulse point such as an explosion or hammer drop used in active methods, or the generated noise 114 of the present disclosure).

[132] Again, similar concepts as described in relation to Figure 3 apply to the receivers 108 shown in Figure 1 where each receiver 108 measures the vertical component of passing surface waves. In some implementations, the receivers 108 shown in Figure 1 may be a towed cable or streamer of receivers that measures pressure variations in the water.

[133] By cross-correlating the noise signals measured at a pair of receivers, the Green’s function for the pair can be obtained, which represents the wavefield as if one of the pair were a virtual (e.g., noise) source and the other of the pair were a receiver.

[134] Figure 4 shows a plurality of virtual source-receiver pairs across a surface above a subsurface region of interest. Ray paths 406 between source-receiver pairs are indicated. In particular, the ray paths from a single central geophone near the centre of the array and each other geophone in the array. The background shading and contour rings indicate the travel-time field from the central geophone to the other geophones. In practice, there are also corresponding ray paths between each geophone and all other geophone (that is, between every pair of geophones), which are not depicted in Figure 4 for simplicity.

[135] Each pair of geophones can provide a signal at location A to be cross-correlated with a signal at location B to reproduce a virtual source-receiver pair using the principle of interferometry. In particular, the cross-correlation of noise measured at respective pairs of geophones at the surface shown in Figure 3 can be used to reproduce a response from the sub-surface target volume, as if it were induced by an impulse point source, which is equal to a Green’s function.

[136] In other words, a response that is received by cross-correlating two receiver recordings can be interpreted as a response that would have been measured at one of the receiver locations as if there were a source at the other. Various approaches to determining the Green’s function for a virtual source-receiver pair are known, with an overview of the approaches described in “Tutorial on Seismic Interferometry: Part 1 – Basic Principles and Applications”; GEOPHYSICS. Vol. 75, No 5 (Sept-Oct 2010; P.75A195075A209; Wapenaar et al.).

[137] An exemplary method 500 for determining one or more ground properties of a target region beneath a surface of the earth, such as subsurface volume 118 of Figures 1, 2 and 3, is shown in 5. The method 500 may be a computer-implemented method. Computer apparatus 900 which can be used to perform the method is described later in reference to Figure 9. The method may be carried out using the setups shown in Figures 1, 2 and 3 and like reference numerals with respect to Figures 1, 2 and 3 denote the same features. The method 500 may begin at optional step 501a and optional step 501b, which are described later in relation to step 506. Below, steps 502, 504, and 506 are described first.

[138] At step 502, a noise signal is generated. The noise signal may be designed to include the frequency content required to generate Scholte waves, P-Waves and / or S-Waves for geotechnical imaging at the surface 112 and / or within the target region 118. Generally, frequencies of 2-120 Hz, or optionally, 5-100 Hz are suitable for this purpose. Having control over the frequency content of the generated noise 114 allows the output generated noise to be tailored to generate Scholte waves and shear waves in the target volume 118.

[139] The noise signal may, in some examples, be the same as, similar to, or share characteristics with the ambient noise. As will be apparent to the person skilled in the art, a noise signal can be generated in any number of ways to resemble ambient noise. In some implementations, the noise signal mimics / matches the ambient noise by recording the ambient noise for replicated output by the noise source. For example, the generated noise signal may be based on the ambient noise profile of the target region 118 determined as step 501a described below. In some implementations, the noise signal is based on colours of noise, such as white noise. In this way, the generated noise 114 can be integrated with the ambient noise for receiving by the receivers, if such functionality is required.

[140] Where the noise signal used for output is the same or similar to ambient noise, the impact and disruption caused by of the generated noise 114 is reduced or in some cases is unnoticeable by the local environment, wildlife, and communities. By replacing an impulsive source (such as an airgun) with such a noise source 106, the acoustic energy is spread over a longer duration which reduces the peak pressure in the body of water 110, greatly reducing the environmental impact.

[141] In some implementations, the noise signal is generated as a chirp, a sweep (linear, non-linear, optimised) or using a pseudorandom binary sequence. In some implementations, the signal is spread over a wide frequency band, for example, 2-120 Hz or optionally, 5-100 Hz. Spread spectrum techniques may be used to do so. In some implementations, the pseudorandom binary sequence is at least one of a maximum length sequence, Gold sequence, or Kasami sequence. As will be apparent to the skilled person, any kind of sequence (pseudorandom binary or otherwise) or other approaches can be used to generate a noise signal which shares characteristics with noise like properties. Advantageously, pseudorandom binary sequences are deterministic and may be generated efficiently using simple underlying hardware implementation. For example, a maximal length sequence can be generated using a combination of linear-feedback shift registers, and a gold sequence (i.e., a gold code) can be generated using two maximal length sequences.

[142] In some implementations, the noise signal / the noise that is output by the noise source 106 is generated to comply with environmental, health, safety standards, laws, regulations, or guidance. The methods and systems described herein can be applied to, for example, offshore marine applications in which the surface waves received are interface waves / Scholte waves. Scholte waves (also referred to as mud-roll) are dispersive waves (different frequencies propagate at different velocities) that travel along the water / sediment (e.g. seabed) interface, and S-wave information is obtained from such waves. Marine seismic or geophysical surveys typically use airguns which can lead to devasting effects like injury, hearing loss, and behavioural changes to species in the surrounding environment. By using a noise signal that is similar to ambient noise, the impact and disruption to marine life is minimised.

[143] In some implementations, the noise signal is processed to output generated noise by the noise source 106 at an intensity which matches, is equal to, or is substantially equal to the average intensity of ambient noise received at, e.g., the first receiver and / or second receiver 108, 202. In some implementations, the amplitude of the noise signal is decreased or increased to match the ambient noise intensity. In some implementations, the noise source 106 is (in additional or alternatively to the processing of the noise signal) controlled to output generated noise based on the noise signal at an intensity / volume which matches the ambient noise intensity by increasing or decreasing the output intensity / volume of the noise source 106 accordingly. By matching the noise output by the noise source 106 to the average intensity of ambient noise, impact, and disruption of the noise to the local environment, wildlife, and communities can be controlled and minimised.

[144] In some implementations, the noise signal output by the noise source 106 is limited to a threshold output level. The threshold output level can be the average intensity of ambient noise across all directions in the ambient noise profile. However, the threshold output level can also be any suitable threshold, such as the highest ambient noise recorded, based on environmental, health or safety standards, laws, regulations, or guidance, and so forth.

[145] In some implementations, the threshold output level can comprise one or more threshold output sublevels according to one or more frequency values and / or frequency ranges. In some implementations, the threshold output level is based on the species living in the environment at or near the surface of the target region. For example, the noise signal can be modified or designed to comply with a maximum permitted noise level at specific frequencies and / or one or more frequency ranges, e.g., with respect to a species. In some implementations, there are one or more threshold output levels for each of one or more respective species living in the environment at or near the surface of the target region. In a further implementation, the threshold output level is based on the species with the lowest threshold output level.

[146] In some implementations, the generated noise signal can be processed in a number or ways to boost, tune, or otherwise manipulate the noise signal to target a specific frequency range, or specific frequencies of interest. In some implementations, the specific frequency range is 2-120 Hz or optionally, 5-100 Hz ensures the noise signal includes the frequency content required to generate Scholte waves, P-Waves and / or S-Waves for geotechnical imaging at the surface 112 and / or within the target region 118. In some implementations, the signal is spread over one or more of the aforementioned frequency bands, for example, using spread spectrum techniques. In some implementations, the noise signal can be processed using, e.g., a cost function to reward increasing the intensity (boosting) certain frequencies or frequency ranges, at the expense of other frequencies or frequency ranges. In some implementations, the noise signal is generated using a random number generator using different seeds, which enables noise signals (and the resulting noise output by the noise signal) to be generated that is not correlated with each other. In some implementations, the noise signal is filtered to attenuate or boost frequencies outside a specific frequency range, such as, by using a digital filter, a high-pass filter, a low-pass filter, etc. In some implementations, filtering can be implemented using, e.g., the aforementioned cost function. In some implementations, an optimisation function is used that increases the intensity at certain frequencies or frequency ranges without affecting the intensity at other frequencies. By processing the generated noise signal in this way, impact and disruption to the local environment, wildlife, and communities can be reduced.

[147] In some implementations, there may be a second noise source 106, or one or more noise sources included the disclosed methods and systems, for example provided in an array coupled to a single ROV 104 or where a plurality of ROVs 104 are provided, each coupled to a respective noise source 106 to increase the generation of Scholte waves, P-Waves and / or S-Waves at the surface 112 and / or within the target region 118. Where the noise source 106 is being used to supplement ambient noise, the use of a plurality noise sources 106 can also improve coverage of less illuminated azimuths by outputting noise from several locations. In this way, operational efficiency is also improved by enabling efficient retrieval of surface wave information. Further, the impact and disruption to the local environment, wildlife, and communities can also be minimised by, e.g., using a plurality of noise sources 106 to output generated noise at a lower intensity compared to if there was a singular noise source, while maintaining or even reducing data acquisition time.

[148] In some implementations, the noise signal output at each noise source 106 of the plurality of noise sources 106 are generated to be different and / or generated to be uncorrelated with each other. In some implementations, noise signals may be generated to be uncorrelated with each other using a random number generator with different seeds (e.g., for each noise signal). In some implementations, noises signals may be uncorrelated with each other by outputting the noise signal via different types / kinds of devices for the plurality of noise sources 106. In some implementations, noise signals may be generated to be uncorrelated with each other by processing the noise signal via modulation, filtering, and / or shifting the phase of the noise signal. In some implementations, the phase of the noise signal is shifted by outputting the noise signal by each noise source 106 of the plurality of noise sources 106 at different times / timeframes with respect to each other. In some implementations, a set of uncorrelated noise signals are generated using a pseudorandom binary sequence, e.g., using a gold sequence, in which each of the plurality of noise sources 106 outputs a different noise signal from the set of uncorrelated noise signals. As will be apparent to the skilled person, there are a number of ways to generate uncorrelated noise signals, and any of the aforementioned examples may be combined to produce uncorrelated noise signals.

[149] Next, at step 504, noise is output, by the noise source 106 based on the generated noise signal to generate a pressure wave in the body of water 110 incident on the surface 112. This, in turn, generates seismic waves in the target region 118. As discussed, incorporating a noise source 106 to output generated noise can enable improved quality (e.g., accuracy and precision) of the determination of the ground properties of a target region without the environmental impact of an impulsive source, such as an airgun.

[150] As the noise signal is intended to be emitted for an extended period of time, the signal may be looped, or otherwise extended to satisfy the intended operational time required for noise output. In some implementations, the looping or extension of the noise signal may be carried out at the noise source 106 via a processor on the noise source 106, or by computing device 900. In some implementations, the noise signal may also be processed to control the intensity, duration, and frequency profile of the noise that is output by the noise source 106 at the processor on the noise source, or by computing device 900. In some implementations, the noise signal is generated locally by the noise source 106. In some implementations, the noise signal is generated by computing device 900 and is communicated to the noise source 106 (via wired or wireless communication), for example from at least one of the ROV 104, the vessel 102 or a remote operating centre. In some implementations, the computing device 900 controls aspects of the operation of the noise source 106 such as the intensity, frequency profile, duration (including starting and stopping operation of the noise source 106), and / or the noise profile (i.e., what noise is being output by the noise source 106). In some implementations, the noise source 106 may be timed or configured automatically or manually to operate only in specific time frames in a day (e.g., in the daytime). In some implementations, where there are one or more noise sources 106 comprises a plurality of noise sources 106, the noise sources 106 can be controlled to operate sequentially, simultaneously, not at all, or in any combination, across the noise sources 106. Similarly, in a further implementation, wherein there are one or more generated noise signals, the noise sources 106 can be similarly operated to output the one or more generated noise signals across the one or more noise sources in any combination.

[151] In some implementations, where the noise source 106 comprises an array of noise sources 106, the array of noise sources 106 may be configured to form a beam of noise incident on the surface 112 of the bed of the body of water 110 to maximise the generation of Scholte, Rayleigh, P- and / or S-waves at the surface 112 and / or within the target region 118. In some implementations, the beam of noise is incident on the surface 112 of the bed of the body of water 110 at an angle within the range of 10 to 40 degrees, optionally, at an angle of 30 degrees. This further optimises the generation of Scholte, Rayleigh, P- and / or S-waves.

[152] Next, at step 506, a data set is received. The data set comprises a first response signal. The first response signal is indicative of the generated noise 114 (and optionally the ambient noise) measured at or near the surface 112 by a first receiver 108, 202 arranged at a first location. In some implementations, the data set also comprises an optional second response signal. The second response signal is indicative of the generated noise 114 (and optionally the ambient noise) measured at or near the surface 112 by a second receiver 108, 202 arranged at a second location. The first and second locations are different. The first and second receivers 108, 202 measure the vibrations in the surface 112 and output a voltage response.

[153] The response signal collected in each measurement may be indicative of the amplitude of the generated noise output by one or more noise sources 106 through the subsurface and measured by the first and second receivers (such as vibration sensors / transducers, pressure sensors, geophones, hydrophones, accelerometers, seismometers, and / or transducers) and, optionally, the amplitude of ambient noise that was transmitted from various cultural or natural ambient sources. Particularly, the vibrations resulting from surface waves are measured. As has been discussed, surface waves are generated by the impact of the generated noise 114 at the surface 112 of a bed of the body of water 110 and / or by cultural or natural processes occurring at or near the surface 112. Their elliptical motion and dispersive properties allow us to retrieve information on the shear properties in the target region. In this example, the first and second response signals are each indicative of the vertical component of the generated noise 114 (and optionally ambient noise) measured at or near the surface 112 by the first and second receiver 108, 202, respectfully. In other examples, the first and second response signals are each indicative of the horizontal, and optionally additionally vertical, component of the generated noise 114 (and optionally ambient noise) measured at or near the surface 112 by the first and second receiver 108, 202, respectively.

[154] The first and / or second signals may be received directly from the first and second receivers, or via one or more intermediary devices. For example, a computing device (as described below with reference to Figure 9), may be situated locally to the receivers for the signals to be transmitted via any form of wired or wireless communication. Alternatively, the signals may be received at a location far away (that is, remote) from the location of the receivers for performing the methods disclosed herein remotely, for example by internet communication or transporting a physical computer-readable medium with a recording of the signals saved thereon. In some examples, the first and second signals are received in real time. In other examples, the first and second signals are received after recording has finished, or at intervals throughout recording sessions.

[155] Optionally, the method 500 may comprise, before step 506 of receiving the data set, selecting a recording frequency for the first and / or second receivers. The recording frequency may be selected based on the depth of the target region and / or the expected wavelength of the waves in the target region. Additionally, or alternatively, the recording frequency for a given receiver may be selected based on its characteristics, such as its optimum recording frequency or frequency range The recording frequency may be called a target recording frequency and may be a range. Advantageously, this means that the recording frequency can be optimised for the characteristics of the target region, and the ambient noise and / or the generated noise 114 in the target region, which means that the use of an excessively high recording can be avoided. This helps reduce the processing burden involved with having excessive data points.

[156] Optionally, the method 500 may comprise before step 506 of receiving the data set, determining the locations for the first and / or second receivers 108, 202. The locations may be locations on or near the surface 112. The locations may be based on the minimum and / or maximum depth of the target region and / or the desired resolution of the waves in the target region caused by the ambient noise and / or the generated noise 114. By determining the locations for the receivers 108, 202 based on the minimum and / or maximum depth of the target region and / or the desired resolution of the waves in the target region, the first and / or second receivers are best placed to measure the waves at their most sensitive. This contributes to the accuracy of the response signals and thus the resulting 2D or 3D model.

[157] Optionally, the method 500 may comprise before step 506 of receiving the data set, step 501a of receiving an initial data set. The initial data set comprises an initial first response signal indicative of the ambient noise measured at or near the surface 112 by the first receiver 108, 202 and / or an initial second response signal indicative of the ambient noise measured at or near the surface 112 by the second receiver 108, 202. Step 501a may further comprise determining an ambient noise profile of the target region 118.

[158] In some implementations, the ambient noise profile is used at step 502 to generate the noise signal. In this manner, it can be ensured that the generated noise signal is generally the same as, similar to, or shares characteristics with the ambient noise. This can reduce the environmental impact of the noise signal in a marine environment, when compared with airguns and other impulsive noise sources.

[159] In some implementations, the ambient noise profile is indicative of the direction and intensity of the ambient noise received at the target region based on a comparison between the initial first response signal and the initial second response signal. In some implementations, the ambient noise profile is determined using beamforming.

[160] In some implementations, the ambient noise profile can specifically be determined using the same receivers 108, 202 used to receive the first response signal and / or the second response signal for determining one or more ground properties of a target region. To determine the ambient noise profile, said receiver can receive the noise as a response signal, analyse the frequency content of the noise, apply a time-shift correction, and sum the noise signals along the different azimuths, to determine the directions and intensities of the ambient noise at or near the target region.

[161] In some implementations, the generated noise 114 can be taken into account with the ambient noise to generate the ambient noise profile, which can enable evaluation of the combined noise received by the receivers as a result of both the noise from ambient sources and the generated noise output by a noise source 106. In some implementations, the determination of where to arrange the noise source 106, re-arrange the noise source, changing the intensity, location, frequency profile, duration, and / or noise profile of the noise 114 that is output by the noise source 106 can be based on the ambient only noise profile that considers solely ambient noise, or instead / additionally a “combined” ambient noise profile that considers both ambient noise and generated noise.

[162] In some implementations, the method 500 further comprises determining the ambient noise profile of the target region using beamforming. In particular, as will be apparent to the person skilled in the art, beamforming may comprise one or more steps of transforming the initial first response signal and initial second response signal to the frequency domain, estimating delay times for each receiver and wavenumber, applying these delay time shifts, and summing the transformed signals together. After the summation, the stacking power can be computed for different azimuths (directions).

[163] Optionally, the method 500 may comprise before step 506 of receiving the data set, step 501b of arranging a noise source 106, for example by controlling the ROV 104 to position the noise source 106 at a particular location within the body of water 112. The disclosed methods and systems can include a single noise source 106 or one or more noise sources 106, for example, each coupled to respective ROVs 104, or all coupled to the same ROV 104. The one or more noise sources 106 can be defined as a second, third, fourth, etc. noise source 106, or defined as part of a plurality of noise sources 106. Each noise source can be independently controlled and positioned (arranged and rearranged). In some implementations, the noise source(s) 106 are arranged at a location within the body of water 112 based on the ambient noise profile of the target region, e.g., along a direction in which the intensity of ambient noise is below a threshold level (either by rearranging an existing noise source 106 or providing a new noise source). In other implementations, wherein there are several noise sources 106, they can be arranged within the body of water 112 in a geometric shape at or near the target region. For example, noise sources 106 can be arranged in a circle, square or any other shape at or near the target region. In some implementations, noise sources 106 can be arranged equidistant from the edges of the target region by five times or at least five times the receiver to receiver spacing. In some implementations, noise sources may be arranged on one side of the target region, in a straight line, in a curved line, in a specific azimuthal direction / angle, or a cardinal direction with respect to the target region. As will be apparent to the skilled person, the noise sources 106 may be arranged in any number of ways. The examples provided are one of many ways one or more noise sources 106 can be arranged at or near the target region 118. As will also be apparent to the skilled person, the noise sources can be rearranged in any number of ways after they have been arranged in an initial position.

[164] The threshold level can be defined as the average intensity of ambient noise across all directions in the ambient noise profile. However, as will be discussed further herein, the threshold level can be defined as any suitable threshold such as the average beam power of the noise source, highest ambient noise recorded, an environmental standard / regulation, and so forth. In some implementations, the threshold level can comprise one or more threshold “sublevels” according to one or more frequency values (or frequency ranges).

[165] Next, at step 508, the first and / or second response signals are processed. The first and / or second response signals are processed to optimise them for the subsequent steps of the method. Processing of the first and / or second response signals is described later in this description.

[166] Next, at step 510, the first and / or second response signals are cross-correlated. There are two possible approaches to this step, distinguished by where the first and / or second response signals indicative of the generated noise or whether they are indicated of the generated noise as well as ambient noise.

[167] Where the first and / or second response signals indicative of the generated noise, step 510 may be as follows. When the generated noise is output by the noise source 106, it travels through the subsurface volume 118 or target region and undergoes reflection at contrasts. The reflected signal is a sum of interferences arising from the interaction of the emitted signal with the various earth layers and accompanying noise. To detect the presence of reflectors and mitigate the noise, the technique of match filtering proves valuable advantages. Match filtering involves measuring the similarity between the generated noise is output by the noise source 106 and the responses (response signals) recorded by each receiver 108, 202. The key mathematical operation employed is cross-correlation, where the generated noise is output by the noise source 106 is correlated with the response signal at each receiver 108, 202. Match filtering is useful when dealing with signals that are buried in noise or interference. By exploiting the known characteristics of the generated noise is output by the noise source 106, match filtering enhances the signal-to-noise ratio, making it easier to detect and identify the reflections.

[168] In a typical implementation, the cross-correlation step involves multiplying the recorded response signal by the generated noise is output by the noise source 106 in the frequency domain. The duration of the recorded response signal from each receiver 108, 202 matches the time used for generated noise transmission plus the listening time. Applying cross-correlation across all receivers 108, 202 results in an output signal highlighting peaks where the recorded response signal aligns with the emitted signal, corresponding to the locations of reflectors (e.g., boundaries between layers or interfaces that have different seismic properties. Seismic waves reflect off these boundaries and are measured by receivers).

[169] Alternatively, reflections within the recorded response signal can be identified through the application of a deconvolution step, in place of or in addition to step 510. The recorded response signal is considered the outcome of the convolution between the generated noise is output by the noise source 106 and the reflectors. To reverse this convolution, the recorded response signal is deconvolved by dividing it by the generated noise in the frequency domain. It's important to note that other noise may be amplified during this processing step. To address this, a stabilizing factor may be introduced into the denominator of the division to mitigate the impact of noise.

[170] Where the first and / or second response signals indicative of the generated noise, the purpose of the cross-correlation is to detect the emitted signal (by the source) in the recorded receiver responses. The cross-correlation step measures the similarity between the emitted signal and the recorded signal. The output of this processing step indicates the presence of the generated noise signal in the recorded response signal. As a result of this processing step the signal-to-noise ratio is improved and the output signal is compressed. Alternative steps to this, include deconvolution whereby the emitted signal (e.g., designed noise) is deconvolved from the recorded signal. This is a mathematical step to remove any repetition in the emitted signal in the recorded signal. This can be implemented in the frequency domain by dividing the frequency response of the recorded signal by that of the emitted (generated noise) signal.

[171] Where the first and second response signals indicative of the generated noise and ambient noise, the purpose of the cross-correlation is to simulate the wavefield that would be recorded at one of the first and second receivers if the other of the first and second receivers were a virtual noise source, such that the cross-correlated response signals can be more easily compared. The simulated wavefield is an essential input to the next step of the method. The step of cross-correlation is described in more detail later in this description. In some implementations, an optional inversion, such as a tomographic inversion or a full-waveform inversion, is performed using the cross-correlated response signals.

[172] Tomographic inversion is a combination of tomography and inversion. Tomography is the name given to techniques for displaying representations of object cross-sections using penetrating waves. In other words, tomography is the name given to imaging techniques which are based on penetrating waves. In this method, tomography may be combined with inversion – either in one-step, or in two-steps. In this context, inversion is the name given to the process of taking the cross-correlated response signals and transforming them into a predicted 2D or 3D model of the target region in terms of the one or more ground properties (thus, in this example, in terms of shear velocity, Vs). Again, the step of tomographic inversion is described in more detail later in this application. Whereas tomographic inversion involves the use of travel time of waves to obtain shear velocities, a full-waveform inversion uses both the amplitude (waveform) and traveltime to obtain shear velocities.

[173] In full-waveform inversion, the entire recorded seismic wavefield (travel times and amplitudes of the recorded waves) are used to extract the physical properties of the subsurface. Modelling techniques solving the wave equation (such as the reflectivity method and finite-difference) are used to predict the seismic wavefield starting from an initial guess of the velocity model. The predicted wavefield is compared to the measured wavefield. The model is then updated to reduce the misfit between the predicted data and the measured data.

[174] Finally, at step 512, the mentioned model of the target region in terms of its shear velocity is generated.

[175] This may be the output from the inversion, and indeed the output from the method overall. It is this model which provides a valuable insight into the constitution of the target region and thus its suitability as a site for possible construction.

[176] In some examples, the model is sent to an output device. For example, the model may be displayed on a monitor or other user interface or sent via wired or wireless communication to another computing device for further processing or display.

[177] While the steps of Fig. 5 are described in order, it will be apparent to the skilled person that the steps may be re-ordered, performed simultaneously in relation to other steps, or performed more than once. For example, the step of 501a could be performed continuously and simultaneously with respect to all other steps in Fig. 5, enabling constant monitoring of the ambient noise. In another example, the arranging of a noise source in step 501b can be carried out after generating a noise signal in step 502. In yet another example, while any of steps 501a, 502, 504 or 506 are being performed, step 501b of arranging a noise source can be simultaneously carried out one or more times. In particular, in relation to the ambient noise profile, step 501b can comprise iteratively rearranging the noise source to new positions / locations with insufficient ambient noise (e.g., in order of lowest to highest ambient noise intensities) to illuminate less illuminated azimuths that are below the threshold level.

[178] Steps 508-512 of method 500 will now be described in more detail.

[179] At step 508, the first and second response signals are processed. The overall aim of the processing is to optimise the first and second response signals for subsequent steps of the method 500, most particularly cross-correlation step 510. This involves obtaining broad band signals. In this example, each of the response signals is processed individually and a number of distinct operations may be carried out (on each response signals). These operations will now be described.

[180] A first operation is to remove the instrument response from the response signals. This operation is sometimes referred to as designature or deconvolution and may be carried out in various ways, as will occur to the skilled person.

[181] A second operation is to remove linear trend and mean from the response signals. This is called detrending and involves removing aspects from the response signals causing distortion (for example, overall linear increase in the mean) over time to reveal subtrends. It is these subtrends which typically are better representations of the noise (and thus shear velocity) at the surface.

[182] A third operation is to reduce spectral leakage. In the context of this disclosure and example, spectral leakage refers to an effect that occurs when the waves caused by noise do not have a frequency which is periodic with the sampling interval of the receiver measuring them. The effect is that, in the measured signals, the frequency distribution is not entirely accurate: a particular frequency in the original wave may leak into (that is, fall within) two adjacent frequencies in the measured data, say, and thus give an inaccurate representation of the frequency profile of the wave (and so its amplitude). To reduce this undesirable effect, the edges of a seismogram plot of the noise measured at a particular receiver as a function of time are tapered with a cosine taper. In this example, the cosine taper is 5% of the trace length. Also in this example, the cosine taper is applied before a low-pass filter is applied (the latter will now be described). In other examples, the taper value may be different.

[183] A fourth operation is to filter out anonymously large amplitude values, such as values having an amplitude above a threshold amplitude value. The filtering is carried out such that noise originating from transient events, such as earthquakes or instruments, which typically result in shear velocity waves in the surface having larger amplitude values than the waves caused by ambient noise and generated noise 114, are filtered out. If these large amplitude events are not filtered out, they can overpower the ambient noise and generated noise 114 response.

[184] In more detail, in certain examples, each response signal is plotted on a graph (over time) and any clear outliers – that is, data points with clearly outlying amplitude values – are identified. This can be done in an automated fashion or by visual inspection (by a human, say). If there is a clear outlier (or outliers), further investigation is done. The further investigation can include deducing whether the outlier pertains to a certain frequency band, deducing whether it is instrument noise, or deducing whether it is a transient signal (that we would like to remove). If a clearly anomalous amplitude is found to be present that is related to an event that overpowers the amplitude of the ambient noise and the generated noise, then the next step is to experiment with filter values and techniques known to the person skilled in the art for removing the anomalous data point. This is done in such a way as to leave the rest of the signal as untouched as possible.

[185] A fifth operation is to split each response signal into shorter segments. The response signals received at step 506 of the method 500 may be indicative of the ambient noise and generated noise measured at or near the surface over a period of several days. In this example, the response signals represent five days of measurements. It is beneficial to split up these potentially large data packets into shorter time segments to: allow for the response signals to be synchronized (that is, for time alignment); reduce computational burden (and thus speed up the processing); and allow for the signals to be more easily manipulated to focus on good patches (where the recording went well) in the subsequent analysis, and avoid bad data patches (where the recording did not go so well, for example due to receivers being disrupted by wildlife, say). A consistent approach to segmentation is taken across the response signals. In this example, the response signals are split into 24-hour periods. In other examples, the durations of the segments may be shorter, longer, or indeed each response signal may be segmented into segments of different durations.

[186] Optionally, step 508 may also include ‘chunking’ the segmented response signals together. This means putting together multiple segmented response signals to greater longer signal chunks. These longer signal chunks may then be used in the cross-correlation of step 510 of the method 500. For completeness, after the cross-correlation, the cross-correlated longer signal chunks may be stacked up to represent even long periods of time. In some examples, multiple segmented response signals are chunked together to represent one-hour windows of time. Such chunking processes are sometimes known as concatenation. Chunking is useful for optimization, including making best use of computer resources.

[187] A sixth operation is to trim each response signal to the nearest second. This allows the response signals collected at different receivers to be synchronized (that is, for time alignment), as necessary later in the method 500. In this example, the segmented response signals are trimmed to the nearest whole second. In other examples, the raw or amplitude filtered response signals may by trimmed (with the segmentation optionally happening after). And different trim values may be used: the response signals may be trimmed to the nearest whole minute, for example, or the nearest millisecond.

[188] A seventh operation is to apply a low-pass filter to each response signal (or indeed the segments of each response signal). This is to reduce high frequency components in the signals, which can cause aliasing. For completeness, aliasing is an undesirable effect which occurs when the sampling frequency is not high enough to accurately sample high frequency components of a signal. In short, aliasing results in inaccuracies in the data. The effect is minimised however by applying a low-pass filter and removing the high frequency components. In this example, the cut-off value (that is, corner frequency) is set to be one quarter of the desired sampling frequency. In this example, a desired sampling frequency is 1 Hz, and so an example cut-off value is 0.25 Hz.

[189] An eighth operation may be to downsample the response signals. This reduces the amount of data that is processed in subsequent steps of the method 500 and thus reduces memory usage, computational burden, and – crucially – reduces the time taken to perform the method 500. This time saving makes the method 500 a practical choice for site analysis in even the most time pressured construction projects. In more detail, the response signals are downsampled by an integer number, n, such that only every nthsample is kept. In this example, n is 100. In other examples, the value of n may be different. The value of n chosen and used depends upon the highest non-aliased frequencies that it is possible to retrieve in that particular setting.

[190] Optionally, the downsampling may be done in integer steps, such as by a factor of 4 or 5. For example, if n is 100, the data may be downsampled from 100 Hz to 20Hz, from 20Hz to 5 Hz, and finally to 1 Hz. This approach can be useful in examples where n is quite high, say, because the data may need a significant amount of downsampling.

[191] A ninth operation may be to apply a motion correction operation is the response signals, where at least one of the noise source 106, the first receiver 108, and / or the second receiver 108 move relative to the surface 112. In some implementations, this step is carried out independently of the other described operations and / or may be the only one of the above operations carried out. In some implementations, known motion correction techniques are carried out.

[192] It will be apparent that, the noise source 106 as described herein can move within the body of water 110 relative to the surface 112. In the system of Figure 1, the receivers 108 that are coupled to a tow cable of the vessel 102 can move within the body of water 110 relative to the surface 112. The motion correction operation is able to account for this movement. For example, the motion of the noise source 106 when towed by the vessel 102 or ROV 104 while emitting generated noise 114 can cause a doppler effect. This results in an apparent difference between the frequency sent out by the noise source 106 and the frequency at which they are recorded. This effect can be corrected by applying a filter in the frequency-wavenumber domain (or rayparameter domain).

[193] In more detail, the movement of the noise source 106 and receivers 108 towed by a vessel 102 or a ROV 106 during data acquisition causes Doppler shifting of the signals emitted by the noise source 106 and recorded by the receivers 108. When the receivers move, it introduces a spatial shift into the data that varies with time. The distortion is geometrical and compared to stationary receivers, such as receivers 202, the moving receivers 108 measure the wavefield with delays in time and space. A common approach to correct for this effect is to describe the distorted seismic measurement as the convolution of the stationary state with a time-varying spatial filter. This filter is a function of the speed at which the receivers move in the water. To undo this effect, the recorded data with receiver motion can be deconvolved using a time-varying spatial filter that is a function of the receiver motion at a given time.

[194] On the other hand, when the noise source 106 is in motion, the energy emitted by the noise source 106 propagates into the earth at a range of locations rather a single point. Also, the noise source 106 signature is modified in frequency when the noise source 106 emission times are long. Depending on the direction of the source, energy with either lower or higher frequencies are propagated through the Earth. Correlation of the pilot sweep and a Doppler-shifted received signal produces a phase distorted seismic data. The Doppler effect or change in frequency is a function of the speed of the vessel 102 or ROV 104 and the dip of the seismic data. The phase distortion is corrected for in the frequency wavenumber domain by applying a frequency wavenumber phase filter per emitted signal and for a given vessel speed.

[195] Conversely, when the noise source 106 is in motion, the energy emitted by the noise source 106 propagates into the Earth across a range of locations rather than originating from a single point. Additionally, the source signature undergoes modification in frequency due to the extended duration of source emissions. Depending on the direction of the source, energy with either lower or higher frequencies is transmitted through the Earth. The correlation between the pilot sweep and a Doppler-shifted received signal results in phase-distorted seismic data. The Doppler effect, or frequency change, is influenced by the speed of the vessel 102 or ROV 104 and the dip of the seismic data. The phase distortion is corrected in the frequency-wavenumber domain by applying a frequency-wavenumber phase filter per emitted signal and for a given vessel speed.

[196] In some examples, the order of the described operations is different to the order presented above. Additionally, or alternatively, in some examples, the number of operations carried out is different. For example, a sub selection of the described operations may be carried out, as best suits the particular project being undertaken. Such sub selection is useful for adapting the analysis to the project requirements, which might include one or more of timings, cost, granularity of the final model required, and size of the target region.

[197] At step 510, where the first and second response signals indicative of the generated noise and ambient noise the processed response signals – here, the first and second processed response signals – are cross-correlated. In this section, the processed response signals may simply be referred to as response signals. The purpose of the cross-correlation is to simulate the wavefield that would be recorded at the receiver of one of the response signals if the receiver of the other were a virtual source. In other words, the wavefield between the first and second receivers is simulated for the case where one of the first and second receivers is a virtual source. Put another way, the recorded response signal in time at one of the first and second receivers is measured relative to the recorded response signal at the other of the first and second receivers. This gives the surface wavefield that has travelled in-between the receivers. Advantageously, by cross-correlating the response signals, the travel-time differences in the noise wavefield between the first and second receivers are retrieved. Step 510 is an important step towards generating the model at step 512 of the method 500.

[198] In this example, the response signals have been segmented and trimmed, and so the first step in the cross-correlation is to select trimmed segments from the two (processed) response signals which correspond to the same time period. The particular segments (and thus time period) chosen is based on a variety of factors, such as the quality of the data in that time period. In other examples, the response signals may not have been trimmed and / or indeed processed at all.

[199] The next step is to cross-correlate the response signals (or segments) and thereby obtain the Green’s function which represents the wavefield between the two receivers, as if one of the receivers were a virtual source.

[200] In this example, the at least two response signals are cross-correlated in the frequency domain; however cross-correlations can also be performed in the time domain. Conveniently, the user is able to choose the domain with the shorter expected processing time. This helps to reduce the overall time needed for the method 500.

[201] The result from step 510 may be normalized before proceeding to step 512.

[202] In other examples, where there are more than two receivers and thus more than two response signals in the data set input to the method 500, each response signal (or processed response signal) is cross-correlated with each other response signal and the Green's function is obtained from each cross-correlation result. In other words, each receiver is paired with each other receiver, and one Green’s function (and thus wavefield simulation) is obtained per receiver pair, or per virtual source-receiver pair. The output from the cross-correlation between the response signal measured at a particular receiver selected as the virtual source and the response signals measured at each other of the plurality of receivers is a virtual source gather showing the Green’s function between the virtual source and each other of the plurality of receivers, thereby creating a virtual shot gather. As previously discussed, an example wavefield simulation for a plurality of receivers from a single central geophone (a type of receiver) is shown in Figure 4.

[203] Overall, the result of step 510 is a representation of the surface waves for the target region that can subsequently be used as the indirect measurement input for the production of the final model of ground properties of interest (which is obtained by solving the inverse problem of known response signals from a ground region having unknown properties).

[204] One approach to solving the inverse problem is a tomographic inversion, as will now be described. The cross-correlated response signals – here, the result of the cross-correlation of the first and second processed response signals – are / is used in a tomographic inversion. The result of the tomographic inversion may be the model of the target region generated at step 512.

[205] To account for the fact that it is unlikely that the sources of ambient noise and / or generated noise measured at the surface of the target region are homogenously distributed, in this example, only the fundamental mode (that is, first harmonic) surface waves are included in the tomographic inversion. This is because these surface waves are assumed to be reconstructed well for the cross-correlated receiver pair, despite the lack of homogeneity of the ambient noise and / or generated noise.

[206] A preparatory step in the exemplary tomographic inversion operation described herein is to determine the average phase velocity between the virtual source-receiver pair. The average group velocity is then calculated from the average phase velocity.

[207] Exemplary tomographic inversion operations will now be described, first in reference to Figure 6. Figure 6 shows a method 600 for performing tomographic inversion that can, optionally, be used in the method 500. There are two distinct ways in which the method 600 can be carried out: as a one-step approach, or as two-step approach. The steps of the method 600 common to both approaches will now be described.

[208] At step 602, an initial model of the one or more ground properties of the target region is provided. Advantageously, by using an initial model, the model generated in the method 600 may be arrived at more quickly.

[209] At step 604, a noise input is provided to the initial model. The noise input is a theoretical noise input.

[210] At step 606, the travel time of the response sign that would be measured at the first and second receivers as a result of the noise input is calculated.

[211] At step 608, the travel time calculated at step 606 and the cross-correlated first and second response signals (that is, the output from step 510) are compared.

[212] At step 610, the initial model is updated based on the result of the comparison.

[213] At step 612, based on the updating of the initial model, the 2D or 3D model of the target region in terms of the one or more ground properties is generated.

[214] The inversion may be performed in the time domain or the frequency domain. Usefully, this means the user can pick the approach resulting in the shorter processing time.

[215] The one-step approach and the two-step approach for performing the method 600 will now be described.

[216] Important to both the one-step and the two-step approach is an understanding of: (1) the grid of cells making up the model of the target region, and (2) of ray paths. A description of (1) and (2) will therefore now be given.

[217] With reference to Figure 7, an example shear wave velocity model 700 has a grid of cells mxy across the surface above the subsurface target volume, comprising columns mx1, mx2, mx3, etc. extending in the x-direction and rows m1y, m2y, m3y, etc extending in the y-direction. Each cell defines an area of the surface. For example, each cell may define a 5m by 5m square; other example options include a 1m by 1m square, or a 10m by 10m square. In other words, the shear wave velocity model comprises a plurality of cells arranged in a two-dimensional grid 500. The two-dimensional grid spans at least the area of the surface above the subsurface target region. Choosing a smaller cell area increases the resolution of the model. Each cell also includes a volume extending vertically below the area of the surface.

[218] In some examples, the shear wave velocity model 700 is not a grid of square cells but instead comprises cells having a rectangular shape, a rhombic shape or otherwise tessellating shapes including non-uniform shapes or a combination of different shapes.

[219] Each cell is associated with a shear wave velocity value, an example of a ground property value, which represents the expected value of shear wave velocity in the actual subsurface target region of interest. The shear wave value carries depth information, either in that the shear wave value is constant throughout the target region below the cell area or in how the shear wave value varies with depth. For example, the defined shear wave value for each cell may be explicitly a function of depth, either a continuous function or a series of values with associated ranges of depth for each value. In another example, the shear wave value may be a function of frequency, which corresponds to depth information because surface wave propagation is influenced by the physical properties of the subsurface volume up to approximately one wavelength depth. In other words, surface waves at low frequencies are affected by physical properties at deeper depths than surface waves at high frequencies.

[220] Meanwhile, a ray path is defined as a line between the first and second receivers. The ray path signifies the motion of a surface wave as it travels from the first receiver to the second receiver (or vice versa) according to ray theory. In particular, a ray path is defined as the direction of propagation of a surface wave, i.e., the direction perpendicular to wave fronts in wave theory or perpendicular to travel-time contours. In more general terms, a ray path is the line between receivers in a receiver pair.

[221] Also, important to both the one-step approach and the two-step approach is the initial model of step 602 of the method 600. The initial model sets initial physical property values for each cell of the initial model, which the method 600 will refine using the response signals. Accordingly, it is not essential for the initial model and initial physical property values to be a highly accurate or high-resolution model of the subsurface target region, although a more accurate initial model may increase the expected accuracy of the end result of the method or decrease the computational time to reach the end result.

[222] In some examples, the initial model is determined based on a user input (for example, according to historic data or map information indicating possible ground property values across the subsurface target region). Alternatively, the initial model may be determined using the first and second response signals (and other response signals if there are more than two response signals). This is done by performing an inversion of group velocity or phase velocity dispersion curves between the first and second response signals (and others, if present), which can be calculated from cross-correlation of the response signals as described above, to find a using a coarser grid or quicker method. As a last resort, an arbitrarily chosen typical value of the ground property can be used for each cell as a starting point. In these examples, the arbitrary model may be selected based on an estimation of the ground properties of the subsurface target region.

[223] The two-step approach will now be described. This is described in the context of there being a plurality of receivers. In other examples, there may be only the first and second receivers.

[224] In this example, the two-step approach comprises selecting a subset of the plurality of virtual-source receiver pairs, wherein the surface wave ray path between each virtual-source receiver pair (that is the ray path between the virtual source and the receiver in each pair) traverses two or more cells. Usefully, this selection process reduces the amount of computation required in generating the model of the target region, and so speeds up the method.

[225] Next, the approach comprises performing tomographic inversion using the cross-correlated response signals for each virtual-source receiver pair in the subset. In this way, the ground property value can be obtained for each cell.

[226] The tomographic inversion for the two-step approach involves a method for carrying out traveltime tomography on the group traveltime information obtained for each of the selected source-receiver pairs. This process tomographically maps the group traveltime information from each source-receiver pair into the cells of the physical property model. The result of the process is therefore an empirical model of group or phase velocity (depending on the traveltime data used), with each cell of the model having a respective group or phase velocity value. This process is carried out for each of a plurality of frequencies to obtain group or phase velocity values for each cell for each frequency. This process is the first stage (i.e., the tomography stage) of the two-step approach to tomographic inversion.

[227] In more detail, the first stage involves first obtaining the initial group velocity model. This can be done in substantially the same way as already mentioned.

[228] Next, the first stage involves determining modelled travel times for each of the selected source-receiver pairs using the initial group velocity model. This is carried out by identifying the wave path from the source to the receiver (for example as a straight ray, curved ray or Fresnel zone) and identifying the cells that are traversed by the wave path. The modelled traveltime based on the initial velocity model is then determined based on the known distance between the source and receiver and the velocity values of each cell traversed by the wave path.

[229] Next, the first stage involves determining an error value indicative of the difference between the modelled travel times and the empirical travel times for each of the selected source-receiver pairs.

[230] Next, the first stage involves determining an updated initial group velocity model using the error value. The updated model is generally in all aspects the same as the initial group velocity model except for new group velocity values associated with at least some of the cells. In other words, the updated model is an updated version of the initial group velocity model taking into account the determined error value between the empirical and modelled group traveltimes for each source-receiver pair. This feedback process may involve a least-squares method, Markov-chain Monte Carlo method or other inversion technique to iteratively update the initial model based on an updated model.

[231] The steps described are typically all part of a subroutine of the first stage tomographic process, which is then iterated according to the inversion method such as least-squares inversion. The iteration continues until the error value reaches an end condition, for example, the error value falling below a threshold.

[232] Once the tomography of the first stage process has been carried out, the two-step tomographic inversion approach can proceed to the second stage: the inversion. This second stage is to obtain the resultant model of the ground property of the target region. As with the first stage tomographic process, the second stage inversion process is carried out for each of a plurality of frequencies to obtain group or phase velocity values for each cell for each frequency.

[233] The second stage comprises a first step of obtaining an initial model of the ground property of the subsurface region. This can be done in substantially the same way as already mentioned. The initial model sets initial physical property values for each cell of the model, which the method will refine using the group velocity model obtained from the first stage (iterative) tomographic process using a further iterative process.

[234] Next, the second stage comprises determining modelled surface wave velocities for each cell based on the initial model. To do this, a forward modelling approach is used.

[235] Next, the second stage comprises determining an error value indicative of the difference between the modelled velocities (determined based on the initial model) and the empirical velocities (obtained from the first stage tomography process) for each cell of the model. The error value may be determined for each cell in a similar manner to that described in relation to the first stage in which an error value is determined between modelled and empirical traveltimes for each source-receiver pair. In some examples, the initial model may be determined based on empirical phase dispersion data between source-receiver pairs.

[236] Next, the second stage comprises determining an updated physical model based on the error value determined. The updated physical model is generally in all aspects the same as the initial physical model except for new physical property values associated with at least some of the cells. In other words, the updated model is an updated version of the initial physical model taking into account the determined error value between the empirical and modelled surface wave velocities for each cell of the model. This feedback process may involve a least-squares method, Markov-chain Monte Carlo method or other inversion technique to iteratively update the initial physical model based on an updated physical model.

[237] The steps described are typically all part of a subroutine of the second stage tomographic process, which is then iterated according to the inversion method such as least-squares inversion. The iteration continues until the error value reaches an end condition, for example, the error value falling below a threshold.

[238] Ultimately, the final model is the 2D or 3D model of the target region in terms of the one or more ground properties generated (and, optionally output to a user device, say), at step 612.

[239] The one-step approach will now be described in more detail. As for the two-step approach, this is described in the context of there being a plurality of receivers. In other examples, there may be only the first and second receivers.

[240] In short, two key differences between the one-step approach and the two-step approach are that: (1) whereas in the two-step approach, the analysis is carried out cell-by-cell, in the one-step approach, the analysis is based on each selected ray path; and (2) whereas in the two-step approach, the tomography and inversion are combined in that they relate to the same step of the method but they are performed in turn as two separate steps, in the one-step approach, tomography and inversion and combined into a single step.

[241] The method of the one-step approach comprises selecting a plurality of ray paths out of the total number of possible ray paths between pairs of receivers. To reduce the processing burden, it is usually beneficial to select a subset of ray paths between receivers. The end result of this selecting step is a selection of ray paths for which dispersion functions (e.g., a group velocity dispersion function and / or a phase velocity dispersion function) can be determined using the response signals from the receivers at each end of the respective ray path.

[242] Next, the method of the one-step approach comprises determining an empirical dispersion function for each ray path, in particular, each ray path selected in the selecting part of the method. The dispersion function may be a group velocity dispersion function or a phase velocity dispersion function (or both may be used). The group velocity dispersion function may be determined as described above with reference to Figure 1, according to any of the usual methods in the art.

[243] Next, the method comprises determining a modelled dispersion function for each ray path using the initial model.

[244] The steps of determining the empirical dispersion function and the modelled dispersion function may be performed in any order (or indeed concurrently).

[245] Next, the method comprises determining an error value indicative of the difference between the modelled dispersion function (determined from the model) and the empirical dispersion function (determined from the detected response signals) for each ray path.

[246] Finally, the method comprises updating the initial model using the error value. The updated model is generally in all aspects the same as the initial model except for new shear wave velocity values (or whichever physical property values are used) are associated with at least some of the cells. In other words, the second model is an updated version of the first model taking into account the determined error value between the empirical and modelled dispersion functions. This feedback process is typically built into the inversion method, e.g., a least-squares method, Markov-chain Monte Carlo method, etc. and performed as part of the inversion routine.

[247] The parts of the method of determining the modelled dispersion function, determining the error value, and determining the updated model using the error value are typically all part of a subroutine of the one-step approach, which is then iterated according to the inversion method such as least-squares gradient-descent inversion. In other words, each time an updated model is determined using the error value resulting from the initial model and resulting modelled dispersion functions for each ray path, the resulting updated model is then used as the initial model for the next iteration. The iteration continues until the error value reaches an end condition, for example, the error value falling below a threshold.

[248] Ultimately, the final updated model is the 2D or 3D model of the target region in terms of the one or more ground properties generated (and, optionally output to a user device, say), at step 612.

[249] An advantage of the one-step approach is that by focusing on ray paths and doing a one-step tomography and inversion based on the ray paths (not every cell, say), the computational burden is reduced, thus reducing the time taken.

[250] For completeness, the tomographic inversion operation used in method 600 may be similar – but different – to that used in multi-channel analysis of surface waves (MASW). MASW has already been described and is an existing technique for gathering surface wave information. Key differences between the described approaches towards tomographic inversion and MASW include that MASW does not use tomographic inversion (instead, a one-dimensional (1D) inversion is used); MASW works with active noise sources (such as sledgehammers or weight drops,), and MASW works with 2D lines of interest along the surface. The target depth in MASW is around 5 to 30 m, and so MASW cannot penetrate as deeply as the approach of the present disclosure.

[251] With reference to Figure 8, an example output of the methods described herein is a final shear wave velocity model showing a subsurface target region. The subsurface target region extends in x and y directions to a depth (z direction) of 100m. The values of shear wave velocity are shown by the shading in the plot and the transition between regions of different shear wave velocity are visible, which indicates the different composition or structure of portions of the subsurface target region. Using the methods described herein, a final shear wave velocity model can be determined with higher resolution and accuracy without resulting in an unfeasible compute time. Such a subsurface model can be used to better understand the suitability of the subsurface target volume for supporting man-made structures on top of or in the subsurface target region.

[252] As already mentioned, there may be more than two receivers. For example, there may be 100 receivers, between 100 and 5000 receivers, 5000 receivers, or more than 5000 receivers.

[253] There are many different types of receivers suitable for use in the method 500. For example, the receivers in the method 500 may be any of (or any combination of): geophones, pressure sensors, hydrophones, accelerometers, seismometers, vibration sensors and transducers.

[254] The target region may be of varying depths and positions relative to the surface. For example, in terms of depth below the surface, the method 500 may be suitable for determining one or more ground properties of a target region at a depth of up to around 100m. For example, the target region may span 0 to 100m below the surface; it may span 0 to 45m below the surface; it may span 50 to 100m below the surface. Thus, the target region may be entirely contained within the subsurface. This makes the described method well suited to a variety of different construction projects, including underground projects.

[255] In some implementations, the processing of the response signals at step 508 includes applying spectral whitening. This technique helps to accentuate the representation of the frequencies of interest of the ambient noise and / or generated noise and, as a result, avoids signals in the micro seismic band from dominating the cross-correlation performed at step 510.

[256] The model and the one or more ground properties may be a ground property other than shear wave velocity, or additional to shear wave velocity, such as compressional wave velocity, density, elastic modulus, shear modulus, or, if a viscoelastic model is being used, optionally also viscosity quality factors Qs and Qp. In general, the model may define multiple ground property values.

[257] Figure 9 shows a block diagram of one implementation of a computing device 900 within which a set of instructions, for causing the computing device to perform any one or more of the methodologies discussed herein, may be executed. In alternative implementations, the computing device may be connected (e.g., networked) to other machines in a Local Area Network (LAN), an intranet, an extranet, or the Internet. The computing device may operate in the capacity of a server or a client machine in a client-server network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The computing device may be a personal computer (PC), a tablet computer, a set-top box (STB), a Personal Digital Assistant (PDA), a cellular telephone, a web appliance, a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single computing device is illustrated, the term “computing device” shall also be taken to include any collection of machines (e.g., computers) that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

[258] The example computing device 900 includes a processor 902, a main memory 904 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), a static memory 906 (e.g., flash memory, static random access memory (SRAM), etc.), and a secondary memory (e.g., a data storage device 918), which communicate with each other via a bus 930.

[259] Processor 902 represents one or more general-purpose processors such as a microprocessor, central processing unit, or the like. More particularly, the processor 902 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, processor implementing other instruction sets, or processors implementing a combination of instruction sets. Processor 902 may also be one or more special-purpose processors such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. Processor 902 is configured to execute the processing logic (instructions 922) for performing the operations and steps discussed herein.

[260] The computing device 900 may further include a network interface device 908. The computing device 900 also may include a video display unit 910 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 912 (e.g., a keyboard or touchscreen), a cursor control device 914 (e.g., a mouse or touchscreen), and an audio device 916 (e.g., a speaker).

[261] It will be apparent that some features of computer device 900 shown in Figure 9 may be absent. For example, one or more computing devices 900 may have no need for display device 910 (or any associated adapters). This may be the case, for example, for particular server-side computer apparatuses 900 which are used only for their processing capabilities and do not need to display information to users. Similarly, user input device 912 may not be required. In its simplest form, computer device 900 comprises processor 902 and memory 904.

[262] The data storage device 918 may include one or more machine-readable storage media (or more specifically one or more non-transitory computer-readable storage media) 928 on which is stored one or more sets of instructions 922 embodying any one or more of the methodologies or functions described herein. The instructions 922 may also reside, completely or at least partially, within the main memory 904 and / or within the processor 902 during execution thereof by the computer system 900, the main memory 904 and the processor 902 also constituting computer-readable storage media.

[263] The various methods described above may be implemented by a computer program. The computer program may include computer code arranged to instruct a computer to perform the functions of one or more of the various methods described above. The computer program and / or the code for performing such methods may be provided to an apparatus, such as a computer, on one or more computer readable media or, more generally, a computer program product. The computer readable media may be transitory or non-transitory. The one or more computer readable media could be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or a propagation medium for data transmission, for example for downloading the code over the Internet. Alternatively, the one or more computer readable media could take the form of one or more physical computer readable media such as semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disc, and an optical disk, such as a CD-ROM, CD-R / W or DVD.

[264] In an implementation, the modules, components, and other features described herein can be implemented as discrete components or integrated in the functionality of hardware components such as ASICS, FPGAs, DSPs, or similar devices.

[265] A “hardware component” is a tangible (e.g., non-transitory) physical component (e.g., a set of one or more processors) capable of performing certain operations and may be configured or arranged in a certain physical manner. A hardware component may include dedicated circuitry or logic that is permanently configured to perform certain operations. A hardware component may be or include a special-purpose processor, such as a field programmable gate array (FPGA) or an ASIC. A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations.

[266] Accordingly, the phrase “hardware component” should be understood to encompass a tangible entity that may be physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein.

[267] In addition, the modules and components can be implemented as firmware or functional circuitry within hardware devices. Further, the modules and components can be implemented in any combination of hardware devices and software components, or only in software (e.g., code stored or otherwise embodied in a machine-readable medium or in a transmission medium).

[268] Unless specifically stated otherwise, as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “providing, “calculating”, “updating”, “generating”, “outputting”, " receiving”, “processing”, “performing”, “determining”, “selecting”, “comparing, and “identifying”, or the like, refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

[269] It is to be understood that the above description is intended to be illustrative, and not restrictive. Many other implementations will be apparent to those of skill in the art upon reading and understanding the above description. Although the present disclosure has been described with reference to specific example implementations, it will be recognized that the disclosure is not limited to the implementations described but can be practiced with modification and alteration within the spirit and scope of the appended claims. Accordingly, the specification and drawings are to be regarded in an illustrative sense rather than a restrictive sense. The scope of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

[270] While at least one exemplary embodiment has been presented in the foregoing detailed description, it should be appreciated that a vast number of variations exist, only some of which have been mentioned above. It should also be appreciated that the exemplary embodiment or exemplary embodiments are only examples, and are not intended to limit the scope, applicability, or configuration of the disclosure in any way. Rather, the foregoing detailed description will provide those skilled in the art with a convenient road map for implementing the exemplary embodiment or exemplary embodiments. It should be understood that various changes can be made in the function and arrangement of elements without departing from the scope of the disclosure as set forth in the appended claims and the legal equivalents thereof.  

Claims

1. A method for determining one or more ground properties of a target region beneath a surface of a bed of a body of water, the method comprising: generating a noise signal; outputting generated noise by a noise source located within the body of water based on the noise signal; receiving a data set, the data set comprising a first response signal, the first response signal indicative of the generated noise measured at or near the surface by a first receiver arranged at a first location; processing the first response signal; cross-correlating or deconvolving the processed first response signal; and using the cross-correlated or deconvolved first response signal to generate a two-dimensional ‘2D’ or three-dimensional ‘3D’ model of the target region in terms of the one or more ground properties.

2. The method of claim 1, the data set further comprising a second response signal, the second response signal indicative of the generated noise measured at or near the surface by a second receiver arranged at a second location, wherein the first and second locations are different, the method further comprising:processing the second response signal;cross-correlating or deconvolving the processed second response signal; andperforming an inversion using the cross-correlated or deconvolved first and second response signals to generate the two-dimensional ‘2D’ or three-dimensional ‘3D’ model of the target region in terms of the one or more ground properties.

3. The method of claim 2, wherein: the first response signal is further indicative of ambient noise measured at or near the surface of the bed of the body of water by a first receiver arranged at the first location; andthe second response signal is further indicative of ambient noise measured at or near the surface of the bed of the body of water by a second receiver arranged at the second location, wherein the step of cross-correlating or deconvolving the processed first response signal and the processed second response signal comprises cross-correlating the processed first response signal with the processed second response signal.

4. The method of claim 1 or claim 2, wherein the step of cross-correlating or deconvolving the processed first response signal and / or the processed second response signal comprises cross-correlating the processed first response signal with the generated noise and / or cross-correlating the processed second response signal with the generated noise.

5. The method of any preceding claim, wherein the noise source, the first receiver, and / or the second receiver move relative to the surface of the bed of the body of water, wherein processing the first and / or second response signals comprises applying a motion correction operation to account for the movement of the noise source, the first receiver, and / or the second receiver relative to the surface of the bed of the body of water.

6. The method of any preceding claim, wherein the first receiver and / or second receiver are coupled to a tow cable of a vessel on or in the body of water.

7. The method of any preceding claim, wherein the first receiver and / or second receiver are arranged on the surface of the bed of the body of water.

8. The method of any preceding claim, wherein the noise source is coupled to a tow cable of a further vessel on or in the body of water.

9. The method of any of claim 7, wherein the noise source is coupled to a tow cable of the same vessel as first receiver and / or second receiver.

10. The method of any preceding claim, wherein the first receiver and / or second receiver are at least one of pressure sensors, geophones, hydrophones, accelerometers, vertical accelerometers, triaxial accelerometers, particle velocity sensors, fibre-optic based sensors seismometers, vibration sensors, and / or transducers, or an array of any of these types of receivers.

11. The method of any preceding claim, wherein the step of outputting noise by the noise source located within the body of water based on the noise signal comprises generating a pressure wave in the body of water incident on the surface of the bed of the body of water at an angle within the range of 10 to 40 degrees.

12. The method of any preceding claims, wherein the noise signal is at least one of: generated using a pseudorandom binary sequence;generated to contain frequency content with a specific frequency range of 2-120 Hz; and output at an intensity which is based on, matches, or is equal to the average intensity of ambient noise received at the first receiver and / or second receiver.

13. The method of any preceding claim, wherein the one or more ground properties comprises one or more elastic properties of the target region, such as the shear-velocity, Vs.

14. A system comprising: one or more processors; one or more memories having stored thereon computer readable instructions configured to cause the one or more processors to perform operations comprising the steps of any of claims 1 to 13.

15. Computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of any of claims 1 to 13. 

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