Method of using generated noise to analyze a target area under the surface of the bed of a body of water and related apparatus

Abstract

A method for determining one or more land properties of a target area beneath the surface of a water body's bed, the method comprising: generating a noise signal; outputting noise based on the noise signal from a noise source located within the water body; receiving a dataset containing: a first response signal indicating generated noise measured at or near the surface by a first receiver positioned at a first location; processing the first response signal; performing cross-correlation or deconvolution on the processed first response signal; and using the cross-correlated or deconvolutioned first response signal to generate a two-dimensional '2D' or three-dimensional '3D' model of the target area regarding one or more land properties. This invention unlocks insights from geographic data related to improvements in sustainability and environmental development: together we create a safe and livable world.

Description

Technical Field

[0001] This disclosure relates to methods and systems for analyzing target areas beneath the surface of a water body's bed using generated noise. More specifically, this disclosure relates to a method and system for determining one or more land properties of a target area beneath the surface of a water body's bed based on generated noise output from a noise source at or near the surface, according to a generated noise signal. This invention unlocks valuable insights from geographic data and also relates to improvements in sustainability and environmental development: together we create a safe and livable world. Background Technology

[0002] There has always been a widespread need for systems and methods to determine subsurface land parameters. In particular, there is a need for systems and methods that can be used to model the properties of target volumes beneath the surface of a water body's bed to provide information useful for infrastructure planning, such as, but not limited to, offshore foundation design, offshore infrastructure, and offshore storage facilities (e.g., carbon storage), as well as infrastructure related to carbon storage monitoring. Determining subsurface land properties in the early planning stages of a construction project reduces uncertainty in project location, foundation design, and construction phases. This, in turn, reduces delays, cost overruns, and unnecessary use of material resources (e.g., concrete) during construction and the asset's lifecycle.

[0003] A key parameter for determining the land properties within a volume of interest is the shear modulus and the shear velocity Vs. The shear velocity Vs is the speed at which a shear wave travels through the material and is controlled by the material's shear modulus. The relationship between shear velocity and shear modulus G is determined by… Limited, among which It is the density of the material. Therefore, the measurement of Vs provides valuable insights into the soil properties of underground land areas. The low-strain shear modulus (Gmax) is also important in foundation design, where... .

[0004] Surface wave spectrum analysis (SASW) and surface wave multichannel analysis (MASW) are examples of techniques for collecting surface wave information that can be used to determine land properties within subsurface volumes. For completeness, surface waves are waves that occur at or near the Earth's surface. In SASW and MASW, surface vibrations are measured from environmental sources (vibrations on the surface caused by background noise sources) or from active sources (such as a heavy object falling onto the bed of a water body or an air gun in water near the bed), and the dispersion of the resulting surface waves is studied. ReMi (Refraction Micromotion) is another surface technique that uses environmental noise and surface waves to infer land properties in subsurface areas based on observations of surface environmental noise.

[0005] To obtain information on shear waves in a marine environment, pulse sources located in the water body, such as air guns, can be used to generate interface waves or surface waves (which may be used interchangeably in this document, examples of which include Sholtz waves or leaking Rayleigh waves) and pressure-shear conversion waves (PS), thereby recording interface waves, surface waves and / or shear waves induced in the seabed of the water body at or very close to the seabed of the water body.

[0006] Pulsed sources, similar to air guns, are used to generate interface or surface waves, for example, by towing them behind a boat and triggering them in the water to induce interface, surface, and / or shear waves on the seabed. Pulsed sources release acoustic energy into the water for an extremely short time. They have limited control over frequency composition, repeatability, and the pressure level at which they are released. They can also be harmful to the marine environment, for example, by disturbing local fauna and creating a poor environmental footprint. Air guns and other pulsed sources can cause devastating effects on species in the surrounding marine environment, such as injury, hearing loss, and behavioral changes. Summary of the Invention

[0007] This overview introduces concepts that are described in more detail in the detailed description section. This overview should not be used to identify the essential features of the claimed subject matter, nor should it be used to limit the scope of the claimed subject matter.

[0008] According to a first aspect of this disclosure, a method is provided for determining one or more land properties of a target area beneath the surface of a water body's bed. In some embodiments, the method further includes generating a noise signal. The step of generating a noise signal can be understood as forming a noise signal or producing a noise signal. The generated / formed / produced noise signal is a noise representation that a noise source, such as a loudspeaker, is capable of producing actual noise based on. In some embodiments, the method further includes generating noise from a noise source in the water body based on the noise signal output. Thus, the generated, formed, or produced noise signal is used to output generated noise. That is, the generated noise, or the output noise, or the output generated noise (i.e., the actual sound wave), is generated by a noise source based on the noise signal. The noise source is configured to output generated noise based on the noise signal. The generated noise output of the noise source is referred to herein as "generated noise". In some embodiments, the method further includes receiving a dataset including: a first response signal indicating the generated noise measured at or near the surface by a first receiver disposed at a first location. In some embodiments, the method further includes processing the first response signal. In some embodiments, the method further includes cross-correlation or deconvolution of the processed first response signal. In some implementations, the method also includes generating a two-dimensional '2D' or three-dimensional '3D' model of the target area about one or more land properties using the first response signal of cross-correlation or deconvolution.

[0009] In some embodiments, the dataset further includes a second response signal indicating generated noise measured at or near the surface by a second receiver positioned at a second location, wherein the first location differs from the second location. In some embodiments, the method further includes processing the second response signal. In some embodiments, the method further includes cross-correlation or deconvolution of the processed second response signal. In some embodiments, the method further includes performing an inversion using the cross-correlated or deconvolutioned first and second response signals to generate a two-dimensional '2D' or three-dimensional '3D' model of the target area regarding one or more land properties.

[0010] Advantageously, this provides a non-invasive technique for measuring the land properties of target areas beneath the surface of a water body's seam (e.g., potential sites for new offshore structures). The ability to generate two- or three-dimensional models of the land properties of such target areas non-invasively and without the use of impulse noise sources such as air guns offers numerous advantages. Compared to existing invasive methods for site analysis and methods utilizing impulse noise sources, this method is simpler, faster, cheaper, less energy-intensive, and has less impact on the local environment and wildlife. This is because less or no drilling is required, and impulse noise excitation is unnecessary. Furthermore, crucially, the disclosed method is both accurate and reliable, making it a practical and technically attractive technique. The disclosed method is particularly suitable for marine environments due to its low-impact characteristics.

[0011] The disclosed method is particularly advantageous for providing targeted follow-up exploration. While some invasive surveying may still be necessary for calibration and ground conditions, the method according to the invention allows for the detection of land anomalies and the prediction of target areas where such anomalies exist, rather than randomly determining land properties by drilling across the entire site. This significantly reduces the amount of invasive exploration required, which is particularly challenging in marine environments. Therefore, a number of benefits according to the invention include providing the desired insight into the target area or the properties of the target area more quickly, thus: reducing the time required; reducing capital expenditure compared to conventional exploration beneath the surface of the seabed; allowing for the use of less heavy machinery and equipment through lighter, more sustainable engineering designs, thereby improving operational safety and minimizing environmental exposure risks. Therefore, in summary, globally, the method according to the invention allows for better risk management and / or risk transfer compared to conventional exploration methods used to determine one or more land properties of target areas beneath the surface of the seabed of a water body.

[0012] Advantageously, using generated noise from a noise source can improve the accuracy and precision of land property determination compared to using pulse noise sources such as air guns. Pulse sources release acoustic energy in a very short time, and they have limited control over frequency components, repeatability, and the pressure level of release. Using generated noise from a noise source solves these problems.

[0013] Furthermore, generated noise output (also referred to as generated noise signal output, or generated noise output, or noise output) provided by a noise source based on a noise signal can further improve the analysis of subsurface land properties in the target area. As will be discussed further herein, surface wave propagation is influenced by subsurface physical properties. Compared to high-frequency surface waves, low-frequency surface waves are influenced by physical properties at greater depths. Noise from a pulse source may be limited in frequency range and / or composition at specific frequencies. As a result, this may negatively impact the quality of the acquired surface wave information and thus affect the determination of land properties at specific depths. By using generated noise output from a noise source, the frequency composition of the generated noise can be controlled, meaning that land properties can be determined with improved efficiency within the depth range of interest. Furthermore, by providing control over the intensity, location, frequency profile, duration, and noise profile of the noise output from the noise source, the quality (e.g., accuracy and precision) of land property determination can be improved using generated noise output from a noise source, as will be discussed further herein. Finally, using generated noise output from a noise source allows for better control over the frequencies, intensities, and sound pressure levels used in geotechnical imaging. This helps to generate signals with amplitudes lower than those of pulse sources such as air guns, thereby reducing the environmental impact of the soil and rock imaging process.

[0014] In some embodiments, the target area can be within the range of 0 to 100 meters below the surface, 0 to 45 meters below the surface, or 50 to 100 meters below the surface. Deeper depths can also be considered target areas, as the method according to the invention has virtually no technical limitations. Advantageously, the illustrated method is therefore suitable for analyzing the depth and profile range of target areas. This makes it very versatile.

[0015] In some implementations, the target area may be a potential site for new structures or tunnels. In some implementations, land risk management framework decisions can be made based on two-dimensional or three-dimensional models. The method according to the invention provides crucial information and insights into land characteristics in the early stages of a construction project, thus offering the best opportunity to influence project outcomes with minimal change costs. For example, plans for subsea structures in or near the target area can be adjusted based on two-dimensional or three-dimensional models. This method can be implemented as part of a feasibility study for construction in or near the target area, such as early site selection. By placing greater emphasis on early site selection, uncertainty in construction projects can be reduced. This translates to significant savings in materials, time, and costs. For example, reduced uncertainty leads to better risk management, which in turn leads to avoiding excessive engineering in the project. This helps reduce the use of materials, such as concrete and steel, which has significant environmental benefits. Furthermore, early site selection facilitates further analysis of the target area, allowing any potentially invasive features to be focused only on areas allegedly identified as problematic or only on certain areas of particular interest.

[0016] In some implementations, the noise source can be a vibration noise source. Using a vibration noise source allows for better control of the frequency, intensity, and sound pressure level used in geotechnical imaging. This helps to generate signals with amplitudes lower than those of pulse sources such as air guns, thereby reducing the environmental impact of the geotechnical imaging process. Examples of vibration noise sources that can be used in the systems and methods described herein include transducers and low-frequency electrodynamic generators.

[0017] In some embodiments, the step of cross-correlating the processed first response signal and / or the processed second response signal includes cross-correlating the processed first response signal with the generated noise and / or cross-correlating the processed second response signal with the generated noise.

[0018] In some embodiments, the first response signal further indicates ambient noise (in addition to generating noise) measured by a first receiver positioned at a first location at or near the surface of the seabed of the water body. In some embodiments, the second response signal further indicates ambient noise (in addition to generating noise) measured by a second receiver positioned at a second location at or near the surface of the seabed of the water body. In some embodiments, a two-dimensional '2D' or three-dimensional '3D' model of the target area regarding one or more land properties is generated by inversion using cross-correlated response signals. In some embodiments, the step of cross-correlateding the processed first response signal and / or the processed second response signal includes cross-correlateding the processed first response signal with the processed second response signal.

[0019] In some implementations, inversion is one of tomographic inversion and full waveform inversion.

[0020] Environmental noise, or seismic environmental noise, may be generated by one or more environmental sources. These sources may be natural (i.e., naturally occurring vibrations) or human-generated (i.e., vibrations generated by human activities). For example, environmental sources may include (one or more) ocean noise (e.g., tidal or wave noise), wind noise, industrial noise, industrial machinery noise, noise from vehicles such as cars or trains, and human noise (e.g., footsteps). Analyzing one or more land properties of a target area based on environmental noise is a practical, convenient, energy-efficient, and low-cost method.

[0021] This method includes a noise source that generates noise that is similar to, identical to, or shares common characteristics with ambient noise generated by environmental sources. Specifically, such a noise source may be included as part of the disclosed method to generate noise that will be received / measured by a receiver. As will be further discussed herein, generating noise similar to ambient noise has less impact on the local environment and wildlife compared to more invasive methods and methods utilizing pulse noise sources such as air guns. Simultaneously, such a noise source can be used to supplement existing ambient noise, enabling improved determination of one or more land properties of a target area underground. The noise source is configured to generate noise based on a noise signal output. The generated noise output of the noise source is referred to herein as "generated noise."

[0022] In some embodiments, the receiver used in this method can be a seismograph, an accelerometer (e.g., a vertical or triaxial accelerometer), a particle velocity sensor, a fiber-optic sensor, a seismograph, a vibration sensor, a pressure sensor, a hydrophone and / or a transducer, or an array of any such receiver. In some embodiments, the receiver can collect data over a considerably long period. For example, ambient noise and / or generated noise can be continuously measured over a five-day period. This longer recording time results in sufficient acquisition of surface wave information from the noise recorded at or near the surface of the target area. The recording time required to obtain sufficient surface wave information depends on the quality and quantity of the noise measured by the receiver. In cases of insufficient surface wave information, a longer recording time can be used to compensate for this by combining the surface wave information to generate a stronger signal. Advantageously, using generated noise output from the noise source can provide sufficient surface wave information without requiring an extended recording time. In fact, in most cases, combining the use of generated noise output from the noise source can reduce the time required to acquire sufficient surface wave information. Therefore, it can improve operational efficiency, that is, it can make more effective use of time, human resources and hardware resources, while minimizing the damage / impact on the local environment, wildlife and communities.

[0023] Conversely, as explained in more detail below, (processed) surface wave information can be used for inversion, such as tomographic inversion, to obtain a model of subsurface shear wave velocity.

[0024] It will be apparent to those skilled in the art that the noise source used in this method can be any device capable of generating vibration. In some embodiments, the noise source can output noise for an extended period of time.

[0025] Each response signal can indicate a Sholtz wave, Rayleigh wave, and / or Love wave caused by the generated noise (and in some implementations, ambient noise).

[0026] Each of the first response signal and the second response signal may respectively indicate the vertical and / or horizontal components of the generated noise (and in some embodiments, the ambient noise) measured by the first receiver and the second receiver at or near the surface.

[0027] In embodiments where the first and second response signals indicate both ambient noise and generated noise, although the ambient noise and generated noise originate from different sources, they can be treated / received as a single response signal by the first and second receivers respectively. In other words, the first and second receivers may not know the source of the noise, whether it is an ambient source or a noise source.

[0028] In some embodiments, the method may include determining the locations of a first and a second receiver prior to the step of receiving the dataset. These locations may be at or near a surface. These locations may be based on the minimum and / or maximum depth of the target area and / or the expected resolution of the waves in the target area caused by generating noise (and, in some embodiments, ambient noise). By determining the receiver locations based on the minimum and / or maximum depth of the target area and / or the expected resolution of the noise waves in the target area, the first and second receivers are optimally positioned to measure the noise waves at their most sensitive points. This contributes to the accuracy of the response signal, thereby aiding in the obtained two-dimensional or three-dimensional model.

[0029] In some implementations, there may be more than two receivers. The receivers may be arranged in an array (row or mesh), or otherwise, to cover the target area of ​​interest.

[0030] In some implementations, the method may include selecting recording frequencies for a first receiver and a second receiver prior to the step of receiving the dataset. The recording frequency may be selected based on the depth of the target region and / or the expected wavelength of noise waves in the target region. The recording frequency may be referred to as the target recording frequency and may be a range.

[0031] In some implementations, one or both of the first and second receivers are coupled to a towline of a vessel on or in the water. The vessel is able to control the position of the first and second receivers relative to the surface of the seabed, for example, to optimize the response signals obtained by the receivers.

[0032] In some implementations, the noise source is coupled to the towing cable of a vessel on or in the water. The vessel is able to control the position of the noise source relative to the water surface, for example, to optimize the generation of Sholtz waves, Rayleigh waves, and / or shear waves in a target area.

[0033] In some implementations, the noise source is coupled to a structure on the surface of the substrate of the water body.

[0034] In some implementations, the vessel is a ship, an unmanned surface vessel (USV), or a remotely operated vehicle (ROV).

[0035] In some implementations, one or both of the first and second receivers are arranged on the surface of the bottom of the water body.

[0036] In some implementations, the noise source is coupled to the towing cable of another vessel on or in the water.

[0037] In some implementations, the other vessel is a ship, an unmanned surface vessel (USV), or a remotely operated vehicle (ROV).

[0038] In some implementations, the noise source is coupled to the towing cable of the vessel, which is the same as one of the first and second receivers or both.

[0039] In some implementations, one or both of the first and second receivers are at least one of a pressure sensor, a seismograph, a hydrophone, an accelerometer (e.g., a vertical or triaxial accelerometer), a particle velocity sensor, a fiber-optic sensor, a seismograph, a vibration sensor, and / or a transducer, or an array of any such receivers.

[0040] In some implementations, the step of generating noise from a noise source located in the water body based on a noise signal output includes generating a pressure wave in the water body that is incident on the surface of the bottom of the water body.

[0041] In some implementations, the pressure wave is incident on the surface of the water body's bottom at an angle ranging from 10 to 40 degrees, optionally at a 30-degree angle. This angle has been found to optimize the generation of Sholtz waves, Rayleigh waves, P waves, and / or S waves at the surface and in the target area.

[0042] In some implementations, the noise source includes a noise source array configured to form a noise beam incident on the surface of the seabed of the water body. This can enhance the generation of Sholtz waves, Rayleigh waves, P waves, and / or S waves at the surface and in the target area.

[0043] In some implementations, the noise beam is incident on the surface of the water body's bottom at an angle ranging from 10 to 40 degrees, optionally at a 30-degree angle. This angle has been found to optimize the generation of Sholtz waves, Rayleigh waves, P-waves, and / or S-waves at the surface and in the target area.

[0044] In some implementations, the first response signal and / or the second response signal include Sholtz waves, Rayleigh waves, and / or shear waves generated in the target region by the generated noise.

[0045] In some implementations, the noise source, the first receiver, and / or the second receiver move relative to the surface of the substrate of the water body.

[0046] In some implementations, processing the first and / or second response signals includes applying motion correction operations to account for the movement of the noise source, the first receiver, and / or the second receiver relative to the surface of the seabed of the water body. By using motion correction to account for the relative motion of the noise source / receiver, the accuracy of the method is improved.

[0047] In some embodiments, the method may include receiving an initial dataset prior to the step of receiving a dataset, the initial dataset containing an initial first response signal indicating ambient noise measured by a first receiver at or near the surface and / or an initial second response signal indicating ambient noise measured by a second receiver at or near the surface. In some embodiments, the method may further include determining an ambient noise profile of a target area based on a comparison between the initial first and initial second response signals, the ambient noise profile indicating the direction and intensity of ambient noise received at the target area. In some embodiments, the initial first and second response signals measured by the first and / or second receivers at or near the surface indicate both ambient noise and generated noise. In some embodiments, the initial first and / or second response signals measured by the first and second receivers at or near the surface indicate only ambient noise (from natural or man-made sources). In some embodiments, the method includes using beamforming to determine the ambient noise profile of the target area.

[0048] Advantageously, by determining the direction and intensity of ambient noise, the location of noise sources can be determined, which improves operational efficiency and enables efficient use of hardware resources. Specifically, considering only ambient noise, the initial location of noise sources can be determined. Similarly, when considering both ambient noise and generated noise, the arrangement of noise sources can be modified / rearranged accordingly. In this way, using a first and a second receiver (or, in some embodiments, a receiver array) makes it possible to determine the ambient noise profile of the target area using existing receivers.

[0049] In some implementations, pseudo-random binary sequences are used to generate or form noise signals. Advantageously, pseudo-random binary sequences are deterministic and can be generated efficiently using simple low-level hardware implementations. Furthermore, uncorrelated signals can be generated efficiently / easily from pseudo-random binary sequences, enabling efficient / faster determination of the Green's function, especially when noise is simultaneously output from one or more noise sources, as will be further explained herein. Similarly, the use of pseudo-random binary sequences can reduce data acquisition time, thus making efficient use of time and hardware resources. Moreover, pseudo-random binary sequences possess acoustic properties similar to white noise, which has the advantage of reducing the impact / damage to the environment and wildlife.

[0050] In some implementations, the pseudo-random binary sequence is at least one of a maximum-length sequence, a Gold sequence, or a Kasami sequence. In some implementations, any pseudo-random binary sequence sharing characteristics with the noise can be used, such as white noise or noise of other colors. In some implementations, any combination of the aforementioned pseudo-random binary sequences can be used to generate the noise signal. Advantageously, the maximum-length sequence, Gold sequence, and Kasami sequence are sequences that are relatively low cross-correlation with other sequences in the same group, which allows for efficient determination of the Green's function and reduces data acquisition time.

[0051] In some implementations, the noise signal is generated to contain frequency components within a specific frequency range of 2-120 Hz. Advantageously, this ensures that the noise signal contains the frequency components required for generating Sholtz waves, P-waves, and / or S-waves for geotechnical imaging at the surface and / or within the target area.

[0052] 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 range, it is possible to target a specific depth of one or more target areas. In some implementations, this specific frequency range is 2-120 Hz, or alternatively 5-100 Hz.

[0053] In some implementations, the noise signal is generated using a random number generator. By using different seeds for random number generation, noise based on one signal can be generated in a way that is uncorrelated with noise based on another signal. This helps to better determine the Green's function and reduce data acquisition time, especially when one or more noise sources are outputting noise simultaneously.

[0054] In some embodiments, the noise signal is filtered to attenuate frequencies outside a specific frequency range. Similar to the modulation of the noise signal described above, filtering the noise signal to attenuate frequencies outside a specific frequency range allows control over the noise signal to increase the noise energy / intensity at a specific frequency or frequency range. In some embodiments, this specific frequency range is 2-120 Hz, or optionally 5-100 Hz. Advantageously, this ensures that the noise signal contains the frequency components required to generate Sholtz waves, P-waves, and / or S-waves for geotechnical imaging at the surface and / or target area.

[0055] In some implementations, the intensity of the output noise signal is based on, matches, equals, or is audibly equal to the average intensity of the ambient noise received at the first and / or second receivers. This allows the noise source to generate noise with an intensity that minimizes its destructive / impact on the local environment and wildlife.

[0056] In some embodiments, the noise signal output by the noise source is limited to a threshold output level. In some embodiments, this threshold output level may be the average intensity of ambient noise in all directions of an ambient noise profile, or, in some embodiments, it may be the aforementioned average intensity of ambient noise received at the first and / or second receivers. However, as will be further discussed herein, the threshold output level may be any suitable threshold, such as the highest ambient noise recorded based on environmental standards / regulations. In some embodiments, the threshold output level may include one or more threshold output sub-levels depending on one or more frequency values ​​or frequency ranges that generate the noise / noise signal.

[0057] In some implementations, during the method, the noise source may be moved to different locations, and the step of outputting noise based on a noise signal by the noise source located in the water body may include outputting noise by the noise source at multiple locations in the water body.

[0058] In some embodiments, the noise source comprises multiple noise sources. In some embodiments, a second noise source may be located at a different location than the first noise source. In some embodiments, the second noise source comprises multiple noise sources. Advantageously, by employing a noise source comprising multiple noise sources, the accuracy and precision of land property determination can be further improved, as will be discussed further herein.

[0059] In some implementations, the plurality of noise sources are configured to output noise signals sequentially. By having the plurality of noise sources output noise signals sequentially (i.e., one at a time in a predetermined order or, in some implementations, one at a time in a random order), it is ensured that the generated noise signals are uncorrelated with each other because they do not overlap in time. This makes it possible to efficiently determine the Green's function and to reduce data acquisition time. Thus, even if each of the plurality of noise sources outputs the same noise signal, there is no correlation between these noise sources.

[0060] In some implementations, the noise signal output at each of the plurality of noise sources is different. That is, the noise signal output by each of the plurality of noise sources is different from each other. Advantageously, having each of the plurality of noise sources generate noise based on a different noise signal output can provide a noise output that is similar to, imitates, or shares characteristics with ambient noise.

[0061] In some implementations, the different noise signal outputs at each of the plurality of noise sources are uncorrelated with each other. Advantageously, using uncorrelated noise signals for the outputs of each of the plurality of noise sources enables efficient determination of the Green's function and reduces data acquisition time.

[0062] In some implementations, the one or more land properties may include one or more elastic properties of the target area, such as shear velocity Vs. As described above, obtaining the shear velocity of the target area provides valuable insights into the land properties of the target area, such as the low-strain shear modulus of the target area. For example, this allows engineers to identify weak areas in the target area beneath the surface of a water body's bed, or lateral variations in the geology. For these reasons, the earlier these features are identified during the project's lifespan, the better.

[0063] In some implementations, the processing steps may include processing the first and / or second response signals to enhance the representation of the received noise. In other words, enhancing the broadband nature of the received noise. This can be achieved, for example, by removing the instrument response and / or by filtering out large amplitudes. Such large amplitudes may be caused by unwanted seismic signals. Advantageously, this prevents large amplitude events from overwhelming the generated noise (and ambient noise, if present) of interest in the response.

[0064] Additionally or alternatively, in some embodiments, the processing steps may include one or more of the following steps: segmenting each response signal; fine-tuning 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 embodiments, the method may include downsampling each response signal by an integer factor. For example, the method may include downsampling at least two response signals by approximately 10 times (e.g., 10x). The advantage of segmenting and fine-tuning the data is that this allows cross-correlation and superposition of response signals measured by different receivers 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 components of each response signal to only the frequencies that can be obtained without aliasing after the subsequent cross-correlation step. Aliasing occurs when the sampling of the signal is insufficient to reconstruct the waveform at a particular frequency. The advantage of downsampling is that it reduces the computational cost of the method and the total time required to generate the model.

[0065] In some implementations, the step of cross-correlating the processed response signals may include estimating the Green's function between the first and second receivers. In other words, the step of cross-correlating at least two processed response signals may include simulating a wavefield, which, if one of the first and second receivers is a dummy source, is recorded at the other of the first and second receivers. For completeness, the cross-correlation step may be applied to segmented and / or downsampled and / or fine-tuned response signals. Advantageously, by cross-correlating the response signals, it is possible to analyze the similarity between the response signals. This is an important step in generative modeling.

[0066] In some implementations, the tomographic inversion step may include using only the fundamental mode surface waves in the inversion. Advantageously, using only the fundamental mode surface waves results in a model with high accuracy.

[0067] In some implementations, the inversion can be performed in the time domain or the frequency domain. Usefully, choosing between the time and frequency domains means that the domain that results in a shorter processing time can be selected.

[0068] In some implementations, the step of performing tomographic inversion may include using a combination of tomographic imaging and inversion.

[0069] In some implementations, the steps for performing tomographic inversion can follow a one-step or two-step approach.

[0070] In some implementations, the steps of performing tomographic inversion may include providing an initial model of one or more land properties of the target area; providing a noise input to the initial model; using the initial model to calculate the travel time of response signals to be measured at a first receiver and a second receiver; comparing the travel time calculated using the initial model with cross-correlated first and second response signals; and updating the initial model based on the comparison results to generate a two-dimensional or three-dimensional model.

[0071] In some implementations, the step of providing an initial model may include determining a phase velocity dispersion profile between the virtual source and one of the first and second receivers.

[0072] In some implementations, the step of providing an initial model may include determining the structure of the initial model (e.g., two-dimensional or three-dimensional) by computer tomography of two or more phase velocity dispersion profiles selected between different virtual sources and different receivers.

[0073] In some implementations, the two-dimensional model can be a two-dimensional representation of the shear velocity of the target region. The three-dimensional model can be a three-dimensional representation of the shear velocity of the target region.

[0074] In some implementations, the method may be a computer-implemented method.

[0075] In some implementations, this method can be used in conjunction with one or more other techniques for analyzing surface waves, such as SASW or MASW. Usefully, this allows for the analysis of subsurface portions at greater depths.

[0076] According to another aspect, a computer program product containing instructions is provided, which, when executed by a computer, cause the computer to perform the method of the first aspect or any of the methods described herein.

[0077] According to another aspect, an apparatus is provided that is configured to perform the method of the first aspect or any of the methods described herein.

[0078] In some implementations, the device may be a system comprising: one or more processors; and one or more memories having computer-readable instructions stored thereon, the computer-readable instructions being configured to cause the one or more processors to perform operations including the steps of the first aspect.

[0079] According to yet another aspect, a computer-readable medium containing instructions that, when executed by a computer, cause the computer to perform the method of the first aspect or any of the methods described herein.

[0080] According to another aspect, a system is provided, comprising: a noise source, a first receiver (optionally, a second receiver), one or more processors; and one or more memories storing computer-readable instructions thereon, the computer-readable instructions being 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 of the methods described herein.

[0081] According to another aspect, a system is provided comprising: a noise source, a first receiver (optionally, a second receiver), and a vessel, wherein the first receiver and / or the second receiver is coupled to the vessel. In some embodiments, the noise source is coupled to another vessel on or in a body of water. In some embodiments, the noise source is coupled to the same vessel as the first receiver and / or the second receiver. In some embodiments, the vessel and / or the other vessel is a ship, an unmanned surface vessel (USV), or a remotely operated vehicle (ROV). In some embodiments, the system further includes one or more processors; and one or more memories storing computer-readable instructions thereon 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 of the methods described herein. Attached Figure Description

[0082] To illustrate how the above and other advantages and features of this disclosure are obtained, the principles briefly described above will be explained in more detail below with reference to the specific embodiments shown in the accompanying drawings. It should be understood that these drawings illustrate only exemplary embodiments of this disclosure and should not be considered as limiting the scope of this disclosure. The principles of this disclosure are explained more specifically and in more detail herein with reference to the accompanying drawings, in which: Figure 1 An exemplary system for collecting data indicating noise generated at the surface of the bed of a body of water is shown; Figure 2 An alternative exemplary system for collecting data indicating noise generated at the surface of the bed of a body of water is shown; Figure 3 A cross-sectional view of the underground target area is shown; Figure 4 Multiple seismic detectors are arranged in a two-dimensional array on the surface above the underground target area; Figure 5 This is a flowchart illustrating an exemplary method for analyzing one or more land properties in a target area; Figure 6 It shows that it is optional to be able to Figure 5 A flowchart of an exemplary method used in the method to perform tomographic inversion; Figure 7This is a perspective view of the shear wave velocity model; Figure 8 This is an example of a shear wave velocity model obtained from the method described in this paper; and Figure 9 A block diagram of one embodiment of a computing device that can be used to perform the methods described herein is shown.

[0083] Throughout the specification and drawings, the same reference numerals denote the same features. Detailed Implementation

[0084] The following is a description of certain embodiments of the invention by way of example only and with reference to the accompanying drawings.

[0085] Various embodiments of this disclosure are described in detail below. While specific implementations are discussed herein, it should be understood that these implementations are for illustrative purposes only. Those skilled in the art will understand that other components and constructions may be used without departing from the spirit and scope of this disclosure. Therefore, the following descriptions and figures are exemplary and should not be construed as restrictive. Numerous specific details are set forth to provide a thorough understanding of this disclosure. However, in some cases, well-known or conventional details have not been described to avoid obscuring the description. References to embodiments in this disclosure may be to the same embodiment or to any other embodiment. Therefore, such references relate to at least one of the embodiments described herein.

[0086] The terms used in this specification generally have their ordinary meaning in the art, in the context of this disclosure, and in the specific context in which each term is used. Alternative languages ​​and synonyms may be used for any one or more terms discussed herein, and no special meaning should be assigned to terms whether they are set forth or discussed herein. In some cases, synonyms for certain terms are provided. The description of one or more synonyms does not preclude the use of other synonyms. When instances are used anywhere in this specification (including instances of any terms discussed herein), such use is merely exemplary and is not intended to further limit the scope and meaning of this disclosure or any exemplary terms. Likewise, this disclosure is not limited to the various embodiments given in this specification. Unless otherwise stated, references to numerical ranges or numerical values ​​“between” two numerical values ​​should be interpreted as including the endpoints of those ranges.

[0087] A method for analyzing one or more land properties (e.g., shear velocity) of a target area beneath the surface of a water body's bed will now be described. In short, the method involves receiving a dataset of generation noise indicating the surface of the target area, measured by a receiver at or near the surface; performing various operations on the data; and ultimately generating a two-dimensional or three-dimensional model of the target area regarding one or more land properties. As previously described, the ability to generate two-dimensional or three-dimensional models of target areas, such as candidate sites for new structures, based on shear velocity provides valuable insights into the composition of the target area. This can then be used to influence subsequent site exploration and construction decisions.

[0088] In some instances, ambient noise can be received along with generated noise. Ambient noise is generated by a variety of environmental sources. Environmental sources are divided into two categories: natural and anthropogenic. Natural sources are sources that cause naturally occurring vibrations, such as the ocean or wind. Anthropogenic sources are noise sources originating from human activities, such as industrial noise, industrial machinery noise, noise from vehicles such as cars or trains, power line noise, and human noise.

[0089] Generated noise is based on generated noise signals and is output by noise sources at or near the surface of the target area. Generated noise is similar to, identical to, or shares common characteristics with environmental noise generated by environmental sources.

[0090] More specifically, the received dataset contains response signals. Each response signal is measured by a corresponding receiver at or near the surface. The response signals indicate the generated noise (and environmental noise, if present) transmitted through the subsurface target area from various sources (which can be one or more noise sources generating the noise, as well as anthropogenic and / or natural sources of environmental noise, which will be explained in more detail below) and measured by the corresponding receiver. Specifically, the vibrations caused by surface waves or interface waves (e.g., Sholtz waves or leaking Rayleigh waves) and pressure-shear conversion waves (PS) generated by the generated noise incident on the surface are measured.

[0091] Surface waves can also be generated by anthropogenic or natural processes occurring at or near the surface (i.e., environmental noise), as well as by generated noise from one or more noise sources. The elliptical motion and dispersion properties of surface waves allow us to obtain information about the land, such as the shear properties of subsurface areas.

[0092] A receiver can also be considered a sensor, which can be a seismograph, accelerometer (e.g., a vertical or triaxial accelerometer), particle velocity sensor, fiber optic sensor, seismograph, pressure sensor, vibration sensor, hydrophone and / or transducer, or an array of any such receiver.

[0093] A noise source can be any device capable of generating vibrations through one or more media. In some embodiments, the noise source can output noise for an extended period. In some embodiments, the noise source is a vibratory noise source or vibrator that is electromagnetically driven, hydraulically driven, or otherwise driven to generate vibrations (these terms are used interchangeably herein). In some embodiments, the noise source is a loudspeaker, woofer, subwoofer, buzzer (e.g., piezoelectric), tweeter, or any other device capable of producing sound, vibration, or seismic activity. In some embodiments, the noise source is a combination of one or more of the above devices. The noise source can be placed on or near the surface of the target area, although in some embodiments the noise source can be placed at a distance from the target area.

[0094] Information that will now help in understanding the invention will be provided.

[0095] First, we explain the shear modulus and its applications in building and infrastructure projects. The shear modulus is a measure of the elastic shear stiffness of a material, representing the deformation of a solid when subjected to a force parallel to one of its surfaces while the opposite surface is subjected to an opposing force. This force and its application in underground volumes (e.g., combined) Figure 3 The effects of the methods described in the target area are important parameters for studies prior to and during the design of land-based and offshore building and infrastructure projects. To determine the volumetric shear modulus, the shear rate Vs is determined. This, in turn, provides an indication of the stiffness of the subsurface material and its ability to support structures extending above and / or through the volume.

[0096] Next, we will explain the waveform types. In the context of land studies, two types of waves are generally distinguished: P-waves and S-waves. In a P-wave, particles in a volume oscillate along the direction of wave propagation; this oscillation causes the land to compress and recover as the wave propagates through it. An S-wave, on the other hand, is a shear wave, in which particles oscillate along a direction perpendicular to the wave's propagation direction.

[0097] P-waves and S-waves are body waves and propagate through a volume in all directions. The interaction of P-waves and S-waves with the Earth's surface produces surface waves that propagate along the surface. Many types of surface waves can be distinguished. In the systems and methods described herein, Sholtz waves are measured and studied because it is convenient to measure the vertical component of surface vibrations. They also provide S-wave information, which can be measured at the surface of the seabed (e.g., the seabed) and in water bodies near the seabed. However, it should be understood that other surface waves (e.g., Rayleigh waves and / or Love waves) can be measured and utilized in the systems and methods described herein. Sholtz waves are dispersive waves (different frequencies propagate at different speeds) that propagate along the water / sediment (e.g., seabed) interface. The systems and methods described herein can be adapted to measure Sholtz waves and / or Rayleigh waves and / or Love waves using single-component or multi-component receivers.

[0098] Because surface waves propagate in two dimensions (at the surface), they decay more slowly than volume waves (which propagate in three dimensions). Surface waves typically occur at a depth of one wavelength from the surface, generally propagate more slowly, and have significantly lower frequencies than volume waves.

[0099] The lower attenuation, slower propagation time, and lower frequency of surface waves make their study particularly attractive for determining shear velocities. Because of the lower attenuation, the signal strength of surface waves is better maintained over longer travel distances. Therefore, the resulting measurements typically have higher signal quality (signal-to-noise ratio) than those obtained from bulk wave studies.

[0100] Some subsequent examples will be illustrated in the context of receivers and receiver arrays, such as in the context of seismic detectors and seismic detector arrays; of course, any other suitable type of receiver mentioned herein may be used. However, it should be understood that the disclosed systems and methods are applicable to a variety of receiver (i.e., sensor) types, including but not limited to seismic detectors, accelerometers (e.g., vertical or triaxial accelerometers), particle velocity sensors, fiber-optic sensors, seismographs, vibration sensors, and / or transducers.

[0101] Now refer to Figure 1 An exemplary system is described for collecting data on generated noise at the surface of the substrate of a water body. Figure 1 A vessel 102 is shown on a body of water 110. A remotely operated vehicle (ROV) 104 has been deployed on vessel 102 and is operating within the body of water 110. Coupled to the ROV 104 is a noise source 106, which directs noise 114 to a subsurface volume 118 within the seabed of the body of water 110 (e.g., combined with...). Figure 5The method describes the surface 112 of the bottom of the water body 110 above the target area. Finally, a plurality of receivers 108 are coupled to the towing cable of the vessel 102. The plurality of receivers 108 may include at least one of pressure sensors, seismographs, accelerometers (e.g., vertical or triaxial accelerometers), particle velocity sensors, fiber optic sensors, hydrophones, seismographs, vibration sensors and / or transducers, or an array of any such receivers.

[0102] The impact of noise 114 on the surface 112 of the bedrock of water body 110 generates a Sholtz wave 116, which is detected by multiple receivers 108 located at or near the surface 112 of the bedrock of water body 110 above subsurface volume 118. As an alternative to or supplement to the Sholtz wave, the impact of noise 114 at surface 112 may also generate and acquire P-waves and S-waves 116, for example, for high-resolution sediment characterization in shallow water environments. P-waves excited in water body 110 can be converted into S-waves at the bedrock of water body 110 or at any interface of soil elasticity variation (e.g., sedimentary layers). These different conversion modes can be recorded by receivers 108. The following is combined with... Figure 5 This process will be discussed in more detail.

[0103] exist Figure 1 The plurality of receivers 108 shown can be coupled to a tow cable that is dragged in the water 110 via a towline coupled to the vessel 102. In this way, the plurality of receivers 108 can be dragged close to or along the surface 112 to detect Sholtz waves, P waves and / or S waves 116 propagating in the subsurface volume 118.

[0104] Figure 2 An alternative system is shown for collecting data on generated noise 114 at the surface 112 of the substrate indicating water body 110, wherein relative to Figure 1 The same reference numerals denote the same characteristics. The difference here is that the plurality of receivers 202 are located on the surface 112 of the bed of the water body 110 above the underground volume 118. The plurality of receivers 202 may include at least one of pressure sensors, seismographs, hydrophones, accelerometers (e.g., vertical or triaxial accelerometers), particle velocity sensors, fiber optic sensors, seismographs, vibration sensors, and / or transducers.

[0105] Figure 1 and Figure 2The vessel 102 can be any suitable vessel or ship located on or within the body of water 110, including unmanned surface vessels (USVs) or remotely operated vehicles (ROVs). The vessel 102 can be controlled by operators located at the vessel 102 or at a remote operations center (not shown). The vessel 102 can also be autonomously controlled.

[0106] Figure 2 Multiple receivers 202 shown are located on the surface 112 of the bed of the water body 110 above the underground volume 118 to detect Sholtz waves, P waves and / or S waves 116 propagating in the underground volume 118.

[0107] Figure 1 and Figure 2 The ROV 104 can be controlled from the vessel 102, for example, via cable (e.g. Figure 1 and Figure 2 (as shown) or controlled via wireless communication (not shown). In another instance, ROV 104 can be controlled by an operator located at vessel 102 or at a remote operations center (not shown).

[0108] Figure 1 and Figure 2 The noise source 106 can be any device capable of generating vibrations through one or more media. In some embodiments, the noise source can output noise for an extended period. In some embodiments, the noise source is a vibration noise source or vibrator that is electromagnetically driven, hydraulically driven, or otherwise driven to generate vibrations (these terms are used interchangeably herein). In some embodiments, the noise source is a loudspeaker, woofer, subwoofer, buzzer (e.g., piezoelectric), tweeter, or any other device capable of generating sound, vibration, or seismic activity. In some embodiments, the noise source is a combination of one or more of the above devices. The noise source can be placed at or near the surface of the target area, although in some embodiments the noise source can be placed at a distance from the target area.

[0109] Figure 1 and Figure 2 The noise source 106 can be a noise source array (such as...) Figure 1 and Figure 2 As shown below, the nature of the noise source will be discussed in more detail below. The position and orientation of the noise source 106 can be changed by controlling the movement of the ROV 104. In an alternative example, the noise source 106 can be directly coupled to the vessel 102 via, for example, a tow cable. The noise source 106 is capable of generating noise 114 in the form of pressure waves incident on the surface 112 in the water body 110.

[0110] In some implementation schemes, Figure 1 and Figure 2The system can be configured with multiple ROVs 104, each with its own noise source 106.

[0111] In some embodiments, the noise source 106 may be coupled to a structure (not shown) on the surface 112 of the substrate of the water body 110, rather than as... Figure 1 and Figure 2 It is coupled to ROV 104 or vessel 102 as shown.

[0112] Now refer to Figure 3 An exemplary system is described for collecting data on ambient noise and / or generated noise 114 at a surface 112 indicating a target area 118. Figure 3 It shows Figure 1 and Figure 2 An exemplary cross-sectional view of the underground volume 118, which may also be combined with... Figure 5 The target area of ​​the method described. Figure 2 Receiver 202 in Figure 3 The subsurface is shown in the form of a seismic detector (although any suitable receiver may be used). P-waves and S-waves travel as body waves through volume 118. Surface 112 extends above subsurface volume 118. Surface waves propagate along surface 112.

[0113] At point A, a schematic representation of particle oscillations (due to the propagation of Scholz waves or Rayleigh waves) at the surface above the target underground volume is shown. As shown, the oscillation of particle P is partially vertical and partially occurs in the direction of propagation. Therefore, the resulting particle motion is substantially elliptical.

[0114] On surface 112 above volume 118, a plurality of seismic detectors 202a and 202b are arranged. The seismic detectors 202a and 202b located on surface 112 can be configured to measure the vertical component of the oscillation schematically shown at point A.

[0115] Seismic detectors 202a and 202b are arranged in a grid array at the surface, extending in two directions. It should be noted that in many cases, the surface above the target area may not be planar. Therefore, the array of seismic detectors 202a and 202b may not be truly “two-dimensional”, as each seismic detector may be offset in the z-direction from its neighbor in the grid. However, this grid arrangement of the seismic detectors will be referred to herein as a two-dimensional array.

[0116] When the surface wave travels through surface 112, the surface wave traveling through surface 102 will cause the vertical movement of multiple seismic detectors 202a and 202b.

[0117] Near the surface 112 and the volume 118 is a noise source 106, which is configured to generate noise based on the output of a generated noise signal.

[0118] Combination Figure 3 Similar concepts described apply to Figure 1 The receiver 108 shown. Multiple receivers 108 at or near surface 112 are capable of detecting vertical movement of the surface by surface waves traveling through surface 112. Figure 1 The receiver 108 shown can detect surface waves as follows. Pressure and particle motion at the interface between water and the seabed are continuous. If an interface or surface wave is excited, it will propagate along the interface, but it will cause a pressure difference in the water. These pressure changes can be detected by the receiver 108, which is, for example, a pressure sensor, such as a hydrophone. The pressure changes vary exponentially with distance from the seabed. With this in mind, in some embodiments, the sensor receiver 108 can be towed near the seabed. Furthermore, a vertical accelerometer or a particle velocity sensor can be used to measure pressure changes with depth. Thus, in some embodiments, the receiver 108 can be a vertical accelerometer or a particle velocity sensor.

[0119] This will now be explained in general terms how shear velocity Vs can be determined from observations of surface waves, particularly Sholtz waves. This is achieved by measuring the dispersion behavior of surface waves. Surface waves are dispersed, meaning their velocity depends on frequency. Generally, seismic velocities increase with increasing Earth's depth. Therefore, conventional surface wave dispersion indicates that surface wave velocities decrease with increasing frequency. By studying the behavior of surface waves at the surface above a volume, the land properties of that volume can be determined.

[0120] There are two ways to measure the velocity of dispersive surface waves, and there is a difference between determining the group velocity or the phase velocity.

[0121] The group velocity of a wave is the speed at which the overall envelope shape of the wave's amplitude propagates in space; this overall envelope shape is called the wave's modulation or envelope. Group velocity is equivalent to the speed at which the wave's energy propagates within a volume. Group velocity is measured by determining the wave propagation between (synthesized) transducer pairs, and it is a frequency-dependent point property within the volume that depends on depth. Group velocity is obtained as a measurement of the time of flight (i.e., travel time) between a virtual source and receiver.

[0122] Simultaneously, phase velocity is the speed at which the phase of any frequency component of a wave travels. Phase velocity is the speed at which each frequency component of a wave travels. Therefore, phase velocity is expressed as a function of frequency. To measure phase velocity, at least two measurement nodes are selected to measure the wave propagating through the volume to determine the relative flight time between seismic detectors of different frequencies. The result is the phase velocity as a function of the average frequency of the volume between the two measurement nodes. Phase velocity is obtained as a point in a two-dimensional phase space (dispersion spectrum), which is obtained through a two-dimensional transformation (e.g., tilt stacking, Radon, FK, etc.) of an array of recorded waveforms (time-distance space).

[0123] Please refer to this again. Figure 3 Each seismic detector 202a, 202b provides a measurement node for measuring the vertical component of the passing surface wave. Seismic detectors 202a, 202b can be configured to measure vibrations caused by ambient noise and / or generated noise 114. For completeness, as previously stated, ambient noise is the background wave field generated by natural or man-made / human noise (not such as pulse points like explosions or falling hammers used in active methods or the generated noise 114 of this disclosure).

[0124] Similarly, combined Figure 3 Similar concepts described apply to Figure 1 The receiver 108 shown measures the vertical component of the passing surface wave. In some embodiments, Figure 1 The receiver 108 shown can be a traction cable or tow cable for a receiver that measures pressure changes in water.

[0125] By cross-correlating the noise signals measured at a pair of receivers, it is possible to obtain the Green's function for the pair of receivers, which represents the wave field as if one of the receivers were a virtual (e.g., noise) source and the other a receiver.

[0126] Figure 4 Multiple pairs of virtual source-receivers are shown traversing the surface above the subsurface region of interest. Ray paths 406 between source-receiver pairs are indicated. Specifically, ray paths from a single central seismic detector near the array center and from each of the other seismic detectors in the array are shown. Background shading and outline rings represent the travel time field from the central seismic detector to the other seismic detectors. In reality, there are also corresponding ray paths between each seismic detector and all the other seismic detectors (i.e., between each pair of seismic detectors), but for simplicity, these are not shown in the diagram. Figure 4 These ray paths are not shown in the diagram.

[0127] Each pair of seismic detectors can provide a signal from location A, which will be cross-correlated with a signal from location B to reconstruct a virtual source-receiver pair using interferometry principles. Specifically, in Figure 3 The cross-correlation of the noise measured at the corresponding seismic detector on the surface shown can be used to reproduce the response from the subsurface target volume as if it were caused by a pulse point source, which is equal to the Green's function.

[0128] In other words, the response received by cross-correlation of two receivers can be interpreted as if the response measured at one of the receiver locations were a sound source at the other location. Various ways of determining the Green's function for a virtual source-receiver pair are known, and these ways are outlined in "Tutorial on Seismic Interferometry: Part 1 – Basic Principles and Applications" in Volume 75, Issue 5 of *GEOPHYSICS* (September-October 2010; pp. 75A195075A209; Wapenaar et al.).

[0129] Figure 5 An example is shown for determining a target area below the Earth's surface (e.g.) Figure 1 , Figure 2 and Figure 3 An exemplary method 500 for determining one or more land properties (subsurface volume 118). Method 500 may be a computer-implemented method. See below. Figure 9 This describes a computer device 900 that can be used to perform this method. The method can be used... Figure 1 , Figure 2 and Figure 3 The apparatus shown is used to perform this, and relative to... Figure 1 , Figure 2 and Figure 3 The same reference numerals denote the same features. Method 500 may begin with optional steps 501a and 501b, which will be described later in conjunction with step 506. Steps 502, 504, and 506 will be described first below.

[0130] In step 502, a noise signal is generated. This noise signal can be designed to contain the frequency components required to generate Sholtz waves, P-waves, and / or S-waves for geotechnical imaging at surface 112 and / or within target region 118. Typically, frequencies of 2-120 Hz or optionally 5-100 Hz are suitable for this purpose. Controlling the frequency components of the generated noise 114 allows for customization of the generated noise output to produce Sholtz waves and shear waves within target volume 118.

[0131] In some instances, the noise signal may be the same as, similar to, or share common characteristics with ambient noise. It will be apparent to those skilled in the art that noise signals can be generated in various ways to analogize ambient noise. In some embodiments, the noise signal simulates / matches ambient noise by recording ambient noise copied from a noise source. For example, the noise signal can be generated based on an ambient noise profile of the target area 118 determined in step 501a as described below. In some embodiments, the noise signal is based on the color of the noise, such as white noise. In this way, the generated noise 114 can be combined with ambient noise for reception by a receiver, if such functionality is required.

[0132] When the noise signal used for output is the same as or similar to the ambient noise, the impact and damage caused by the generated noise 114 are reduced, or in some cases go unnoticed by the local environment, wildlife, and community. By replacing a pulse source (such as an air gun) with this noise source 106, sound energy propagates over a longer duration, which reduces the peak pressure in the water body 110, thereby significantly reducing environmental impact.

[0133] In some implementations, the noise signal is generated as a chirped signal, a swept-frequency signal (linear, nonlinear, optimized), or using a pseudo-random binary sequence. In some implementations, the signal is spread over a wide frequency band, such as 2-120 Hz, or optionally 5-100 Hz. This can be done using spread spectrum techniques. In some implementations, the pseudo-random binary sequence is at least one of a maximum-length sequence, a Gold sequence, or a Kasami sequence. It will be apparent to those skilled in the art that any kind of sequence (pseudo-random binary or others) or other method can be used to generate a noise signal with characteristics similar to noise. Advantageously, the pseudo-random binary sequence is deterministic and can be generated efficiently using simple low-level hardware implementations. For example, a combination of linear feedback shift registers can be used to generate a maximum-length sequence, and two maximum-length sequences can be used to generate a Gold sequence (i.e., Gold code).

[0134] In some implementations, the noise signal / noise output by noise source 106 is generated to comply with environmental, health, and safety standards, laws, regulations, or guidelines. The methods and systems described herein can be applied, for example, to offshore operations where the received surface waves are interface waves / Sholtz waves. Sholtz waves (also known as mudslide waves) are dispersive waves (different frequencies propagating at different speeds) that travel along the water / sediment (e.g., seabed) interface, and S-wave information is obtained from such waves. Marine seismic or geophysical exploration often uses air guns, which can lead to destructive impacts on species in the surrounding environment, such as injury, hearing loss, and behavioral changes. By using noise signals similar to ambient noise, it is possible to minimize the impact and damage on marine life.

[0135] In some embodiments, the noise signal is processed to output noise generated by noise source 106, the intensity of which matches, is equal to, or substantially equal to the average intensity of ambient noise received, for example, at the first and / or second receivers 108, 202. In some embodiments, the amplitude of the noise signal is decreased or increased to match the intensity of the ambient noise. In some embodiments, noise source 106 is controlled (as a supplement or alternative to noise signal processing) based on a noise signal with an intensity / volume that matches the ambient noise intensity by correspondingly increasing or decreasing the output intensity / volume of noise source 106. By matching the noise output by noise source 106 to the average intensity of ambient noise, the impact and damage of noise on the local environment, wildlife, and communities can be controlled and minimized.

[0136] In some implementations, the noise signal output by noise source 106 is limited to a threshold output level. The threshold output level can be the average intensity of ambient noise in all directions of 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 guidelines.

[0137] In some embodiments, the threshold output level may include one or more threshold output sub-levels based on one or more frequency values ​​or frequency ranges. In some embodiments, the threshold output level is based on the species living in or near the surface of the target area. For example, a noise signal may be modified or designed to conform to the maximum permissible noise level for a specific frequency and / or one or more frequency ranges, e.g., relative to a species. In some embodiments, one or more threshold output levels exist for each of one or more corresponding species living in or near the surface of the target area. In another embodiment, the threshold output level is based on the species having the lowest threshold output level.

[0138] In some implementations, the generated noise signal can be processed in various ways to enhance, tune, or otherwise manipulate the noise signal to target a specific frequency range or a specific frequency of interest. In some implementations, the specific frequency range is 2-120 Hz, or optionally 5-100 Hz, to ensure that the noise signal contains the frequency components required to generate Sholtz waves, P-waves, and / or S-waves for geotechnical imaging at surface 112 and / or within target region 118. In some implementations, spread spectrum techniques are used, for example, to spread the signal across one or more of the aforementioned frequency bands. In some implementations, a cost function can be used, for example, to process the noise signal, rewarding an increase (enhancement) in the intensity of 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 with different seeds, which makes it possible to generate noise signals (and the noise output resulting from the noise signals) that are uncorrelated with each other. In some implementations, the noise signal is filtered, for example, using digital filters, high-pass filters, low-pass filters, etc., to attenuate or enhance frequencies outside a specific frequency range. In some implementations, the aforementioned cost function can be used, for example, to implement the filtering. In some implementations, optimization functions are used to increase the intensity of certain frequencies or frequency ranges without affecting the intensity of other frequencies. By processing generated noise signals in this way, it is possible to reduce the impact and damage on the local environment, wildlife, and communities.

[0139] In some implementations, the disclosed methods and systems may include a second noise source 106, or one or more noise sources, such as those arranged in an array coupled to a single ROV 104, or, in the case of multiple ROVs 104, each ROV 104 coupled to a corresponding noise source 106 to enhance the generation of Sholtz waves, P-waves, and / or S-waves at the surface 112 and / or within the target area 118. When using noise sources 106 to supplement ambient noise, the use of multiple noise sources 106 can also improve coverage of under-illuminated azimuths by outputting noise from multiple locations. This also improves operational efficiency by enabling efficient acquisition of surface wave information. Furthermore, by using multiple noise sources 106 to output generated noise at a lower intensity compared to the case of a single noise source, while maintaining or even reducing data acquisition time, it is possible to minimize the impact and damage to the local environment, wildlife, and communities.

[0140] In some embodiments, the noise signal outputs at each of the plurality of noise sources 106 are generated to be different from each other and / or generated to be uncorrelated with each other. In some embodiments, random number generators with different seeds (e.g., for each noise signal) can be used to generate uncorrelated noise signals. In some embodiments, noise signals can be made uncorrelated by outputting noise signals via means of different types / kinds for the plurality of noise sources 106. In some embodiments, uncorrelated noise signals can be generated by processing the noise signals by modulating, filtering, and / or shifting the phase of the noise signals. In some embodiments, the phase of the noise signals is shifted by each of the plurality of noise sources 106 outputting noise signals at different times / time periods relative to each other. In some embodiments, a set of uncorrelated noise signals is generated using a pseudo-random binary sequence (e.g., using a gold sequence), wherein each of the plurality of noise sources 106 outputs a different noise signal from this set of uncorrelated noise signals. It will be apparent to those skilled in the art that there are various ways to generate uncorrelated noise signals, and any of the foregoing examples can be combined to generate uncorrelated noise signals.

[0141] Next, in step 504, noise source 106 outputs noise based on the generated noise signal to generate pressure waves incident on surface 112 in water body 110. This, in turn, generates seismic waves in target area 118. As discussed, combining noise source 106 to output generated noise can improve the quality (e.g., accuracy and precision) of land property determination in target area without the environmental influence of pulse sources such as air guns.

[0142] Because the intention is to emit noise signals for a longer period of time, the signal can be cycled or otherwise extended to meet the intended operating time required for noise output. In some embodiments, the cycling or extension of the noise signal can be performed at the noise source 106 via a processor on the noise source 106 or by the computing device 900 at the noise source 106. In some embodiments, the noise signal can also be processed to control the intensity, duration, and frequency profile of the noise output by the noise source 106 at the processor on the noise source or by the computing device 900. In some embodiments, the noise signal is generated locally by the noise source 106. In some embodiments, the noise signal is generated by the computing device 900 and transmitted to the noise source 106 (via wired or wireless communication), for example from at least one of ROV 104, vessel 102, or a remote operations center. In some embodiments, the computing device 900 controls aspects of the operation of the noise source 106, such as intensity, frequency profile, duration (including the start and stop operation of the noise source 106), and / or noise profile (i.e., what noise the noise source 106 outputs). In some embodiments, noise source 106 can be automatically or manually timed or configured to operate only during specific periods of the day (e.g., during the day). In some embodiments, where one or more noise sources 106 comprise multiple noise sources 106, the noise sources 106 can be controlled to operate sequentially, simultaneously, not at all, or in any combination of noise sources 106. Similarly, in another embodiment where one or more generating noise signals are provided, noise source 106 can be similarly operated to output one or more generated noise signals in any combination of one or more noise sources.

[0143] In some embodiments, where noise source 106 includes an array of noise sources 106, the array of noise sources 106 can be configured to form a noise beam incident on the substrate surface 112 of the water body 110 to maximize the generation of Sholtz waves, Rayleigh waves, P-waves, and / or S-waves at the surface 112 and / or within the target region 118. In some embodiments, the noise beam is incident on the substrate surface 112 of the water body 110 at an angle in the range of 10 to 40 degrees, optionally at a 30-degree angle. This further optimizes the generation of Sholtz waves, Rayleigh waves, P-waves, and / or S-waves.

[0144] Next, in step 506, a dataset is received. This dataset contains a first response signal. The first response signal indicates the generated noise 114 (optionally, ambient noise) measured by a first receiver 108, 202 positioned at or near surface 112. In some embodiments, the dataset also includes an optional second response signal. The second response signal indicates the generated noise 114 (optionally, ambient noise) measured by a second receiver 108, 202 positioned at or near surface 112. The first and second positions are different. The first and second receivers 108, 202 measure the vibration of surface 112 and output a voltage response.

[0145] The response signals collected in each measurement can indicate the amplitude of the generated noise output from one or more noise sources 106 via underground and measured by first and second receivers (e.g., vibration sensors / transducers, pressure sensors, seismographs, hydrophones, accelerometers, seismographs, and / or transducers), and optionally the amplitude of ambient noise transmitted from various human or natural environmental sources. In particular, vibrations caused by surface waves are measured. As already discussed, surface waves are generated by the impact of generated noise 114 on the surface 112 of the bed of the water body 110 and / or by human or natural processes occurring at or near the surface 112. Their elliptical motion and dispersion properties allow us to obtain information about the shear properties in the target area. In this example, the first and second response signals each indicate the vertical component of the generated noise 114 (and optionally ambient noise) measured by the first and second receivers 108, 202 at or near the surface 112. In other instances, the first and second response signals each indicate a horizontal component and an optional additional vertical component of the generated noise 114 (and optional ambient noise) measured by the first and second receivers 108, 202 at or near surface 112, respectively.

[0146] The first and / or second signals may be received directly from the first and second receivers, or via one or more intermediate devices. For example, a computing device (see below) Figure 9 The signal (as described) can be located at the receiver's local location for transmitting signals via any form of wired or wireless communication. Alternatively, the signal can be received at a location (i.e., remotely) away from the receiver's location for remotely performing the methods disclosed herein, for example, via internet communication or transmission to a physical computer-readable medium on which a record of the signal is stored. In some instances, the first and second signals are received in real time. In other instances, the first and second signals are received at time intervals after recording is completed, or throughout the recording session.

[0147] Optionally, method 500 may include selecting a recording frequency for the first and / or second receiver prior to step 506 of receiving the dataset. The recording frequency may be selected based on the depth of the target region and / or the expected wavelength of waves in the target region. Additionally or alternatively, the recording frequency of a given receiver may be selected based on its properties, such as its optimal recording frequency or frequency range. The recording frequency may be referred to as the target recording frequency and may be a range. Advantageously, this means that the recording frequency can be optimized for the characteristics of the target region and the ambient noise and / or generated noise 114 in the target region, meaning that excessively high recording frequencies can be avoided. This helps reduce the processing burden associated with too many data points.

[0148] Optionally, method 500 may include determining the locations of the first and / or second receivers 108, 202 prior to step 506 of receiving the dataset. These locations may be at or near surface 112. These locations may be based on the minimum and / or maximum depth of the target region and / or the expected resolution of the waves in the target region caused by ambient noise and / or generated noise 114. By determining the locations of receivers 108, 202 based on the minimum and / or maximum depth of the target region and / or the expected resolution of the waves in the target region, the first and / or second receivers are optimally positioned to measure the waves at their most sensitive points. This contributes to the accuracy of the response signal, thereby contributing to the obtained two-dimensional or three-dimensional model.

[0149] Optionally, method 500 may include a step 501a of receiving an initial dataset prior to step 506 of receiving the dataset. The initial dataset includes an initial first response signal indicating ambient noise measured by the first receivers 108, 202 at or near surface 112 and / or an initial second response signal indicating ambient noise measured by the second receivers 108, 202 at or near surface 112. Step 501a may also include determining an ambient noise profile of the target region 118.

[0150] In some implementations, an ambient noise profile is used in step 502 to generate the noise signal. This ensures that the generated noise signal is substantially the same as, similar to, or shares common characteristics with the ambient noise. Compared to air guns and other impulse noise sources, this reduces the environmental impact of the noise signal in the marine environment.

[0151] In some implementations, the ambient noise profile is used to indicate the direction and intensity of ambient noise received at the target area based on a comparison between an initial first response signal and an initial second response signal. In some implementations, beamforming is used to determine the ambient noise profile.

[0152] In some implementations, the same receivers 108, 202 used to receive a first response signal and / or a second response signal to determine one or more land properties of a target area can be used to specifically determine the environmental noise profile. To determine the environmental noise profile, the receivers can receive noise as a response signal, analyze the frequency components of the noise, apply time-shift correction, and sum the noise signals along different orientations to determine the direction and intensity of the environmental noise at or near the target area.

[0153] In some implementations, the generated noise 114 can be considered together with ambient noise to generate an ambient noise profile. This allows for the evaluation of the combined noise received by the receiver, which is the result of both noise from the ambient source and the generated noise output by noise source 106. In some implementations, determining where to position noise source 106 and rearranging the noise source, as well as altering the intensity, location, frequency profile, duration, and / or noise profile of the noise 114 output by noise source 106, can be based on a pure ambient noise profile considering only ambient noise, or alternatively / additionally on a “combined” ambient noise profile considering both ambient noise and generated noise.

[0154] In some embodiments, method 500 further includes using beamforming to determine the ambient noise profile of the target area. In particular, it will be apparent to those skilled in the art that beamforming may include one or more of the following steps: transforming initial first and second response signals to the frequency domain, estimating the delay time for each receiver and wavenumber, applying these delay time offsets, and summing the transformed signals. After summing, the superimposed power in different azimuths (directions) can be calculated.

[0155] Optionally, method 500 may include step 501b, prior to step 506, of arranging noise sources 106, for example, by controlling ROV 104 to place noise sources 106 at specific locations within water body 112. The disclosed methods and systems may include a single noise source 106 or one or more noise sources 106, for example, each noise source coupled to a corresponding ROV 104, or all noise sources coupled to the same ROV 104. One or more noise sources 106 may be defined as a second, third, fourth, etc., or as part of a plurality of noise sources 106. Each noise source may be controlled and located independently (arranged and rearranged). In some embodiments, based on the ambient noise profile of the target area, the locations of noise sources(s) 106 within water body 112 are arranged, for example, along directions where the ambient noise intensity is below a threshold level (by rearranging existing noise sources 106 or providing new noise sources). In other embodiments where multiple noise sources 106 are present, they may be arranged geometrically within water body 112 at or near the target area. For example, the noise source 106 can be arranged in a circular, square, or any other shape at or near the target area. In some embodiments, the noise source 106 can be arranged at a distance equal to the edge of the target area, which is five times or at least five times the receiver-to-receiver spacing. In some embodiments, the noise source can be arranged to one side of the target area, in a straight line, a curve, at a specific orientation / angle, or in the main direction relative to the target area. It will be apparent to those skilled in the art that the noise source 106 can be arranged in a variety of ways. The provided example is one of many ways in which one or more noise sources 106 can be arranged at or near the target area 118. It will also be apparent to those skilled in the art that the noise source can be rearranged in a variety of ways after it has been arranged in its initial position.

[0156] A threshold level can be defined as the average intensity of ambient noise in all directions within an ambient noise profile. However, as will be further discussed herein, a threshold level can be defined as any suitable threshold, such as the average beam power of a noise source, the highest recorded ambient noise, an environmental standard / prescribed value, etc. In some implementations, the threshold level may include one or more threshold "sub-levels" based on one or more frequency values ​​(or frequency ranges).

[0157] Next, in step 508, the first and / or second response signals are processed. The first and / or second response signals are processed to optimize them for subsequent steps of the method. The processing of the first and / or second response signals will be described later in this specification.

[0158] Next, in step 510, the first and / or second response signals are cross-correlated. There are two possible methods for this step, distinguished by whether the first and / or second response signals indicate generated noise or whether they indicate generated noise in conjunction with ambient noise.

[0159] If the first and / or second response signal indicates generated noise, step 510 may proceed as follows. When generated noise is output by noise source 106, it passes through subsurface volume 118 or the target area and undergoes contrast reflection. The reflected signal is the sum of interference generated by the interaction of the transmitted signal with various strata and accompanying noise. Matched filtering techniques show significant advantages for detecting the presence of reflectors and mitigating noise. Matched filtering involves measuring the similarity between the generated noise output by noise source 106 and the response (response signal) recorded by each receiver 108, 202. The key mathematical operation employed is cross-correlation, where the generated noise output by noise source 106 is correlated with the response signal at each receiver 108, 202. Matched filtering is useful when processing signals submerged in noise or interference. By utilizing known characteristics of the generated noise output by noise source 106, matched filtering enhances the signal-to-noise ratio, making reflections easier to detect and identify.

[0160] In a typical implementation, the cross-correlation step involves multiplying the recorded response signal in the frequency domain by the generated noise output by noise source 106. The duration of the recorded response signal from each receiver 108, 202 is matched to the time used for generating the noise transmission plus the listening time. Applying cross-correlation on all receivers 108, 202 results in an output signal with a prominent peak, where the recorded response signal is aligned with the transmitted signal and corresponds to the location of the reflector (e.g., a boundary between layers or interfaces with different seismic properties from which seismic waves are reflected and measured by the receiver).

[0161] Alternatively, as an alternative to or supplement to step 510, reflections within the recorded response signal can be identified by performing a deconvolution step. The recorded response signal is considered to be the result of the convolution between the generated noise output from noise source 106 and the reflector. To reverse this convolution, the recorded response signal is deconvolved by dividing the recorded response signal by the generated noise in the frequency domain. It should be noted that other noise may be amplified in this processing step. To address this issue, a stabilization factor can be introduced into the denominator of the division to mitigate the impact of noise.

[0162] In cases where the first and / or second response signals indicate generated noise, the purpose of cross-correlation is to detect the transmitted signal (emitted by the source) in the recorded receiver response. The cross-correlation step measures the similarity between the transmitted signal and the recorded signal. The output of this processing step indicates the presence of generated noise 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, which deconvolves the transmitted signal (e.g., design noise) from the recorded signal. This is a mathematical step used to eliminate any repetition in the transmitted signal from the recorded signal. This can be achieved in the frequency domain by dividing the frequency response of the recorded signal by the frequency response of the transmitted signal (generated noise).

[0163] When the first and second response signals indicate generated noise and ambient noise, the purpose of cross-correlation is to simulate the wavefield that would be recorded at the other of the first and second receivers if one of the first and second receivers were a dummy noise source, thus making it easier to compare the cross-correlated response signals. The simulated wavefield is a necessary input for the next step of the method. The steps of cross-correlation will be described in more detail later in this specification. In some embodiments, optional inversions, such as tomographic inversions or full waveform inversions, are performed using the cross-correlated response signals.

[0164] Tomographic inversion is a combination of tomographic imaging and inversion. Tomography is the name given to a technique that uses transmitted waves to display a representation of the cross-section of an object. In other words, tomographic imaging is the name given to an imaging technique based on transmitted waves. In this method, tomographic imaging and inversion can be combined in one or two steps. In this context, inversion is the name given to a process that obtains cross-correlated response signals and transforms them into a predictive two-dimensional or three-dimensional model of a target area regarding one or more land properties (and thus, in this example, regarding shear velocity Vs). Similarly, the steps of tomographic inversion will be described in more detail later in this application. Tomographic inversion involves using the wave's travel time to obtain the shear velocity, while full waveform inversion uses both amplitude (waveform) and travel time to obtain the shear velocity.

[0165] In full waveform inversion, the physical properties of the subsurface portion are extracted using the entire recorded seismic wavefield (recorded wave travel time and amplitude). Modeling techniques for solving wave equations (such as the reflectivity method and the finite difference method) are used to predict the seismic wavefield from an initial guess in the velocity model. The predicted wavefield is then compared with the measured wavefield. The model is then updated to reduce the mismatch between the predicted and measured data.

[0166] Finally, in step 512, the model of the target region with respect to the shear velocity of the target region is generated.

[0167] This can be the output of the inversion, but it is actually the overall output of the method. It is this model that provides valuable insights into the composition of the target area, thereby confirming its suitability as a possible structure.

[0168] In some instances, the model is sent to an output device. For example, the model can 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.

[0169] Although Figure 5 The steps are described in sequence, but it will be apparent to those skilled in the art that these steps can be reordered, performed concurrently with other steps, or performed more than once. For example, the steps of 501a can be performed relative to... Figure 5 All other steps are performed sequentially and simultaneously, enabling continuous monitoring of ambient noise. In another instance, the placement of the noise sources in step 501b can be performed after the noise signal is generated in step 502. In yet another instance, step 501b, which involves placing the noise sources, can be performed once or multiple times while any of steps 501a, 502, 504, or 506 are being executed. Specifically, regarding the ambient noise profile, step 501b may include iteratively redistributing the noise sources to new locations / spots where ambient noise is insufficient (e.g., in order of minimum to maximum ambient noise intensity) to illuminate the insufficiently illuminated areas below a threshold level.

[0170] Steps 508-512 of method 500 will now be explained in more detail.

[0171] In step 508, the first and second response signals are processed. The overall goal of this processing is to optimize the first and second response signals for subsequent steps of method 500, particularly for the cross-correlation step 510. This involves obtaining wideband signals. In this example, each response signal is processed individually, and multiple different operations can be performed (for each response signal). These operations will now be described.

[0172] The first operation is to remove the instrument response from the response signal. This operation is sometimes referred to as designature correction or deconvolution, and can be performed in various ways that can be conceived by those skilled in the art.

[0173] The second operation is to remove the linear trend and average from the response signal. This is called detrending and involves removing aspects of the response signal that cause distortion over time (such as an overall linear increase in the average) to reveal secondary trends. It is these secondary trends that often better represent noise (and shear rate) at the surface.

[0174] The third operation is to reduce spectral leakage. In the context of this disclosure and this example, spectral leakage refers to the effect that occurs when waves caused by noise do not have frequencies periodic with the sampling intervals of the receivers measuring them. This effect is that the frequency distribution in the measured signal is not perfectly accurate: for example, a particular frequency in the original wave may leak (i.e., fall into) two adjacent frequencies in the measurement data, thus giving an inaccurate representation of the wave's frequency profile (and its amplitude values). To reduce this undesirable effect, the edges of the seismic record map of the noise measured at a particular receiver as a function of time are gradually reduced with a cosine taper. In this example, the cosine taper is 5% of the track length. Additionally, in this example, the cosine taper is applied before applying a low-pass filter (the latter will now be explained). In other examples, the taper value may be different.

[0175] The fourth operation is to anonymize and filter out large amplitude values, such as those exceeding an amplitude threshold. This filtering removes noise originating from transient events such as earthquakes or instrumentation, which typically result in shear velocity waves on the surface having larger amplitude values ​​than those caused by ambient noise and generated noise 114. If these large amplitude events are not filtered out, they may overwhelm the response of ambient noise and generated noise 114.

[0176] More specifically, in some instances, each response signal is plotted on a graph (over time), and any obvious outliers—data points with significantly anomalous amplitude values—are identified. This can be done automatically or through visual inspection (for example, by a person). If one (or more) obvious outliers are found, further investigation is conducted. Further investigation may include inferring whether the outlier belongs to a certain frequency band, whether it is instrument noise, or whether it is a transient signal (the signal we want to remove). If a significantly anomalous amplitude is found that is associated with an event exceeding the amplitude of ambient noise and generating noise, the next step is to experiment with filtering values ​​and techniques known to those skilled in the art to remove the outlier data points. This is done in a way that preserves as much of the signal as possible.

[0177] The fifth step is to divide each response signal into shorter segments. The response signals received in step 506 of method 500 can indicate ambient and generated noise measured at or near the surface over a period of several days. In this example, the response signals represent measurements over five days. Dividing these potentially large data packets into shorter time segments is beneficial in that it allows for: synchronization of the response signals (i.e., time alignment); reduced computational burden (and thus faster processing); and easier manipulation of the signals in subsequent analysis to focus on good blocks (where recording is done well) and avoid bad blocks (where recording is not done well, for example, due to interference from wildlife). A consistent segmentation method is used for the response signals. In this example, the response signals are divided into 24-hour periods. In other examples, the duration of the segments can be shorter, longer, or each response signal can actually be segmented into segments of different durations.

[0178] Optionally, step 508 may also include “chunking” the segmented response signals together. This means combining multiple segmented response signals into larger, longer signal chunks. These longer signal chunks can then be used in the cross-correlation in step 510 of method 500. For completeness, after cross-correlation, the longer cross-correlated signal chunks can be stacked to represent even longer time periods. In some instances, multiple segmented response signals are chunked together to represent a one-hour time window. Such a chunking process is sometimes referred to as concatenation. Chunking is useful for optimization, including making full use of computing resources.

[0179] The sixth operation is to fine-tune each response signal to the nearest second. This allows response signals collected at different receivers to be synchronized (i.e., for time alignment), which is necessary in a later part of method 500. In this example, the segmented response signals are fine-tuned to the nearest whole second. In other examples, the raw or amplitude-filtered response signals can be adjusted (segmentation optionally occurring later). And different fine-tuning values ​​can be used: for example, the response signals can be fine-tuned to the nearest whole minute or the nearest millisecond.

[0180] The seventh step is to apply a low-pass filter to each response signal (or actually, each segment of the response signal). This is to reduce high-frequency components in the signal that may cause aliasing. For completeness, aliasing is an undesirable effect that occurs when the sampling frequency is not high enough to accurately sample the high-frequency components of the signal. In short, aliasing leads to inaccurate data. However, by applying a low-pass filter and removing high-frequency components, this effect can be minimized. In this example, the cutoff value (i.e., the corner frequency) is set to one-quarter of the desired sampling frequency. In this example, the desired sampling frequency is 1 Hz, so the exemplary cutoff value is 0.25 Hz.

[0181] The eighth operation can be downsampling the response signal. This reduces the amount of data processed in subsequent steps of method 500, thereby reducing memory usage, computational burden, and, crucially, the time spent executing method 500. This time saving makes method 500 a practical choice for site analysis, even in time-pressured construction projects. More specifically, in integer... n The response signal is downsampled so that only each of the following is retained: n One sample. In this example, n It is 100. In other instances, n The values ​​can be different. The selected and used... n The value depends on the highest non-aliasing frequency that can be obtained in a given setting.

[0182] Optionally, downsampling can be performed in integer steps, such as by a factor of 4 or 5. For example, if n If it's 100, then the data can be downsampled from 100Hz to 20Hz, from 20Hz to 5Hz, and finally downsampled to 1Hz. For example, this method... n This is useful in very high-level instances because the data may require extensive downsampling.

[0183] The ninth operation may be applying motion correction to the response signal, wherein at least one of the noise source 106, the first receiver 108, and / or the second receiver 108 moves relative to the surface 112. In some embodiments, this step is performed independently of the other operations described and / or may be the only one of the above operations performed. In some embodiments, known motion correction techniques are performed.

[0184] Clearly, the noise source 106, as described herein, can move relative to the surface 112 within the water body 110. Figure 1In the system, the receiver 108 of the towline coupled to vessel 102 can move relative to surface 112 within the water body 110. Motion correction operations can address this motion. For example, when towed by vessel 102 or ROV 104, noise source 106 moves while emitting generating noise 114, which can cause the Doppler effect. This results in a significant difference between the frequencies emitted by noise sources 106 and the frequencies they are recorded at. This effect can be corrected by applying filtering in the frequency-wavenumber domain (or ray parameter domain).

[0185] More specifically, during data acquisition, the motion of the noise source 106 and receiver 108, towed by vessel 102 or ROV 106, causes a Doppler shift in the signal emitted by the noise source 106 and recorded by the receiver 108. As the receiver moves, it introduces a spatial offset into the time-varying data. The distortion is geometric, and the moving receiver 108 measures a wavefield with temporal and spatial delays compared to a stationary receiver such as receiver 202. A common method to correct for this effect is to describe the distorted seismic measurements as a convolution of a stationary state with a time-varying spatial filter. This filter is a function of the receiver's velocity in the water. To eliminate this effect, a time-varying spatial filter can be used to deconvolve the recorded data containing receiver motion, which is a function of the receiver's motion at a given time.

[0186] On the other hand, when noise source 106 moves, the energy emitted by noise source 106 propagates underground at a series of locations rather than at a single point. Furthermore, when the emission time of noise source 106 is long, the characteristics of noise source 106 change in frequency. Depending on the direction of the source, energy of lower or higher frequencies propagates underground. The correlation between pilot scanning and Doppler shift of the received signals produces phase-distorted seismic data. The Doppler effect, or frequency variation, is a function of the velocity of vessel 102 or ROV 104 and the dip angle (dip) of the seismic data. Phase distortion is corrected in the frequency-wavenumber domain by applying frequency-wavenumber phase filtering to each transmitted signal and a given vessel velocity.

[0187] Conversely, when noise source 106 is in motion, the energy emitted by noise source 106 propagates underground through a series of locations, rather than originating from a single point. Furthermore, due to the longer duration of source emission, the source characteristics change in frequency. Depending on the direction of the source, energy at lower or higher frequencies propagates underground. The correlation between the pilot scan and the Doppler shift received signal results in phase distortion in the seismic data. The Doppler effect, or frequency variation, is influenced by the speed of vessel 102 or ROV 104 and the dip angle of the seismic data. Phase distortion is corrected in the frequency-wavenumber domain by applying frequency-wavenumber phase filtering to each transmitted signal and a given vessel speed.

[0188] In some instances, the order of the operations differs from the order described above. Additionally or alternatively, in some instances, the number of operations performed may differ. For example, a sub-selection of operations best suited to the specific ongoing project may be performed. Such a sub-selection is useful for adapting the analysis to project requirements, which may include one or more of the following: time, cost, the required granularity of the final model, and the size of the target area.

[0189] In step 510, the processed response signals (here, the first and second processed response signals) are cross-correlated, given that the first and second response signals indicate generated noise and ambient noise. In this section, the processed response signals can be simply referred to as the response signals. The purpose of cross-correlation is to simulate the wave field recorded at the receiver of one response signal when the receiver of one response signal is a dummy source. In other words, for the case where one of the first and second receivers is a dummy source, the wave field between the first and second receivers is simulated. In other words, the response signal recorded at the other of the first and second receivers is measured relative to the response signal recorded at one of the first and second receivers. This gives the surface wave field traveling between the receivers. Advantageously, by cross-correlateding the response signals, the travel time difference in the noise wave field between the first and second receivers can be obtained. Step 510 is an important step towards generating the model in step 512 of method 500.

[0190] In this example, the response signal has been segmented and fine-tuned, so the first step in cross-correlation is to select the segment to be fine-tuned from the two (processed) response signals corresponding to the same time period. The selection of a specific segment (and thus the time period) is based on various factors, such as the data quality within that time period. In other examples, the response signal may not have been fine-tuned and / or may not have been processed at all.

[0191] The next step is to cross-correlate the response signals (or segments) to obtain the Green's function representing the wave field between the two receivers, as if one of the receivers were a virtual source.

[0192] In this example, the at least two response signals are cross-correlated in the frequency domain; however, cross-correlation can also be performed in the time domain. Conveniently, the user can select the domain with the shorter expected processing time. This helps reduce the total time required by method 500.

[0193] Before proceeding to step 512, the results from step 510 can be normalized.

[0194] In other instances, where there are more than two receivers and thus more than two response signals in the dataset input to method 500, each response signal (or processed response signal) is cross-correlated with each of the other response signals, and a Green's function is obtained from each cross-correlation result. In other words, each receiver is paired with each of the other receivers, and a Green's function (and thus a wavefield simulation) is obtained for each receiver pair or each virtual source-receiver pair. The output of the cross-correlation between the response signal measured at a specific receiver selected as a virtual source and the response signals measured at each of the other receivers among the multiple receivers is a virtual source gather, which shows the Green's function between the virtual source and each of the other receivers among the multiple receivers, thus producing a virtual shot gather. As described above, Figure 4 An exemplary wavefield simulation of multiple receivers from a single central seismic detector (a type of receiver) is shown.

[0195] In summary, the result of step 510 is a representation of the surface waves of the target area, which can then be used as an indirect measurement input to generate a final model of the land properties of interest (obtained by solving an inverse problem of known response signals from a ground area with unknown properties).

[0196] One approach to solving the inverse problem is tomographic inversion, which will now be explained. Tomographic inversion uses cross-correlated response signals, which are the result of cross-correlation of the first and second processed response signals. The result of the tomographic inversion can be a model of the target region generated in step 512.

[0197] Given that ambient noise sources and / or generated noise sources measured at the surface of the target region are unlikely to be uniformly distributed, in this example, only the fundamental mode (i.e., the first resonance) surface waves are included in the tomographic inversion. This is because, although the uniformity of ambient noise and / or generated noise is lacking, these surface waves are assumed to be well reconstructed for cross-correlated receiver pairs.

[0198] The 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.

[0199] Now we will first refer to Figure 6 This section describes an exemplary tomographic inversion operation. Figure 6 A method 600 for performing tomographic inversion is shown, which can optionally be used in method 500. Method 600 can be performed in two different ways: a one-step method or a two-step method. The steps of method 600 common to both methods will now be described.

[0200] In step 602, an initial model of one or more land properties of the target area is provided. Advantageously, by using the initial model, the model generated in method 600 can be achieved more quickly.

[0201] In step 604, a noise input is provided to the initial model. This noise input is the theoretical noise input.

[0202] In step 606, the travel time of the response symbols to be measured at the first and second receivers is calculated as a result of the noise input.

[0203] In step 608, the travel time calculated in step 606 and the first and second response signals cross-correlated (i.e., the output from step 510) are compared.

[0204] In step 610, the initial model is updated based on the comparison results.

[0205] In step 612, based on the update of the initial model, a two-dimensional or three-dimensional model of the target area regarding the one or more land properties is generated.

[0206] This inversion can be performed in the time domain or the frequency domain. Usefully, this means that users can choose the method that results in a shorter processing time.

[0207] The one-step and two-step methods for performing method 600 will now be explained.

[0208] For both the one-step and two-step methods, it is important to understand: (1) the cell mesh of the model that makes up the target region, and (2) the ray path. Therefore, explanations of (1) and (2) will now be given.

[0209] Please refer to Figure 7 An exemplary shear wave velocity model 700 has a cell grid mxy spanning the surface above the subsurface target volume, including columns mx1, mx2, mx3, etc. extending along the x-direction, and rows m1y, m2y, m3y, etc. extending along the y-direction. Each cell defines a region of the surface. For example, each cell may define a 5m × 5m square; other exemplary options include 1m × 1m squares or 10m × 10m squares. In other words, the shear wave velocity model comprises multiple cells arranged in a two-dimensional grid 500. This two-dimensional grid at least spans the surface region above the subsurface target region. Choosing a smaller cell area can improve the model's resolution. Each cell also includes a volume extending vertically below the surface region.

[0210] In some instances, the shear wave velocity model 700 is not a mesh of square cells, but rather includes cells with rectangular, rhomboid, or other tessellation shapes including irregular shapes, or combinations of different shapes.

[0211] Each cell is associated with a shear wave velocity value, which is an instance of a land property value representing the expected shear wave velocity in the actual subsurface target area of ​​interest. The shear wave value carries depth information because it is constant throughout the target area below the cell, or because it varies with depth in a certain way. For example, the shear wave value used to define each cell can be an explicit function of depth, a continuous function, or a series of values, each with an associated depth range. In another instance, the shear wave value can be a function of frequency, corresponding to the depth information, because surface wave propagation is influenced by the physical properties of the subsurface volume up to approximately one wavelength deep. In other words, low-frequency surface waves are influenced by physical properties at greater depths than high-frequency surface waves.

[0212] Simultaneously, the ray path is defined as the line between the first and second receivers. According to ray theory, the ray path represents the motion of a surface wave as it travels from the first receiver to the second receiver (or vice versa). In particular, the ray path is defined as the direction of surface wave propagation, i.e., perpendicular to the wavefront in wave theory or perpendicular to the isopleths of travel time. More generally, the ray path is the line between the receivers in a receiver pair.

[0213] The initial model in step 602 of method 600 is also important for both one-step and two-step methods. This initial model sets initial physical property values ​​for each element of the initial model, which method 600 refines using response signals. Therefore, a high-precision or high-resolution model of the underground target area is not strictly necessary for the initial model and initial physical property values, although a more accurate initial model can improve the expected accuracy of the final result or reduce the computation time to reach the final result.

[0214] In some instances, an initial model is determined based on user input, such as historical data or map information indicating possible land property values ​​across the entire underground target area. Alternatively, an initial model can be determined using a first response signal and a second response signal (and other response signals, if more than two). This is accomplished by inverting a group velocity or phase velocity dispersion profile between the first and second response signals (and other signals, if present), which can be calculated from the cross-correlation of the response signals as described above, to be found using a coarser grid or a faster method. As a last resort, a typical value of any chosen land property can be used as a starting point for each cell. In these instances, an arbitrary model can be selected based on an estimate of the land properties of the underground target area.

[0215] The two-step method will now be explained. This is explained in the context of having multiple receivers. In other instances, there may only be a first receiver and a second receiver.

[0216] In this example, the two-step method involves selecting a subset of multiple virtual source-receiver pairs, where the surface wave ray path between each virtual source-receiver pair (i.e., the ray path between the virtual source and receiver in each pair) traverses two or more cells. Usefully, this selection process reduces the computational cost required to generate a model of the target region, thus accelerating the method.

[0217] Next, the method involves performing tomographic inversion on the cross-correlation response signals of each virtual source-receiver pair in the subset. In this way, land property values ​​for each cell can be obtained.

[0218] Two-step tomographic inversion involves a method of performing propagation time tomography on the group propagation time information obtained for each selected source-receiver pair. This process maps the group travel time information from each source-receiver pair to cells of a physically specific model in a tomographic manner. Therefore, the result of this process is an empirical model of the group or phase velocity (depending on the travel time data used), with each cell of the model having its own group or phase velocity value. This process is performed for each of a plurality of frequencies to obtain the group or phase velocity value for each cell at each frequency. This process is the first stage (i.e., the tomographic stage) of the two-step tomographic inversion.

[0219] More specifically, this first stage involves first obtaining an initial group velocity model. This can be accomplished in essentially the same way as described above.

[0220] Next, the first phase involves determining the simulated travel time for each of the selected source-receiver pairs using an initial group velocity model. This is achieved by identifying the wave path from the source to the receiver (e.g., a straight ray, a curved ray, or a Fresnel zone) and the cells traversed by the wave path. Then, based on the known distance between the source and receiver and the velocity value of each cell traversed by the wave path, the simulated travel time based on the initial velocity model is determined.

[0221] Next, the first phase involves determining the error value that indicates the difference between the simulated travel time and the empirical travel time for each of the selected source-receiver pairs.

[0222] Next, the first phase involves using the error value to determine an updated initial group velocity model. Except for the new group velocity values ​​associated with at least some cells, the updated model is generally identical to the initial group velocity model in all respects. In other words, the updated model is an updated version of the initial group velocity model, which takes into account a determined error value between the empirical group travel time and the simulated group travel time for each source-receiver pair. This feedback process can involve least squares, Markov chain Monte Carlo, or other inversion techniques to iteratively update the initial model based on the updated model.

[0223] The steps described typically encompass all parts of the subroutines of the first-stage tomographic imaging process, which are then iterated using inversion methods such as least-squares inversion. Iteration continues until the error value reaches a termination condition, such as falling below a threshold.

[0224] Once the first-stage tomographic imaging process has been performed, the two-step tomographic inversion method can proceed to the second stage: inversion. This second stage aims to obtain a model of the land properties of the target area. Similar to the first-stage tomographic imaging process, the second-stage inversion process is performed for each of multiple frequencies to obtain the group or phase velocity values ​​for each cell at each frequency.

[0225] The second stage involves the first step of obtaining an initial model of the land properties of the subsurface area. This can be accomplished in essentially the same way as described above. The initial model sets initial physical property values ​​for each cell of the model, and this method will use a group velocity model obtained from the first-stage (iterative) tomography process to refine these initial physical property values ​​using a further iterative process.

[0226] Next, the second stage involves determining the simulated surface wave velocity for each element based on the initial model. For this, forward modeling is used.

[0227] Next, the second phase involves determining the error value between the simulated velocity (determined based on the initial model) and the empirical velocity (obtained from the first-phase tomographic process) of each element of the indicator model. The error value for each element can be determined in a manner similar to that described in the first phase, which determines the error value between the simulated travel time and the empirical travel time for each source-receiver pair. In some instances, the initial model can be determined based on empirical phase dispersion data between the source-receiver pairs.

[0228] Next, the second phase involves determining an updated physical model based on the identified error values. Aside from new physical property values ​​associated with at least some elements, the updated physical model is generally identical to the initial physical model in all respects. In other words, the updated model is an updated version of the initial physical model, which takes into account the determined error values ​​between the empirical surface wave velocities and simulated surface wave velocities for each element of the model. This feedback process can involve least squares methods, Markov chain Monte Carlo methods, or other inversion techniques to iteratively update the initial physical model based on the updated physical model.

[0229] The steps described typically encompass all parts of a subroutine in the second-stage tomographic imaging process, which is then iterated using an inversion method such as least-squares inversion. Iteration continues until the error value reaches a termination condition, such as falling below a threshold.

[0230] Finally, in step 612, the final model is a two-dimensional or three-dimensional model of the target area with respect to one or more land properties generated (and, for example, optionally output to a user device).

[0231] The one-step method will now be explained in more detail. As for the two-step method, this will be explained in the context of multiple receivers. In other instances, there may be only a first receiver and a second receiver.

[0232] In short, the two main differences between the one-step and two-step methods are: (1) in the two-step method, the analysis is performed cell by cell, while in the one-step method, the analysis is based on each selected ray path; and (2) in the two-step method, tomography and inversion are combined because they involve the same step of the method, but they are performed as two separate steps in sequence, while in the one-step method, tomography and inversion are combined into a single step.

[0233] The one-step method involves selecting multiple ray paths from the total number of possible ray paths between multiple pairs of receivers. To reduce processing overhead, it is often beneficial to select a subset of ray paths between receivers. The end result of this selection step is the selection of the ray path, whose dispersion function (e.g., group velocity dispersion function and / or phase velocity dispersion function) can be determined using the response signals from the receivers at each end of the respective ray path.

[0234] Next, the one-step method involves determining the empirical dispersion function for each ray path, and more particularly, determining the empirical dispersion function for each ray path selected in the selection section of the method. This dispersion function can be a group velocity dispersion function or a phase velocity dispersion function (or both). Reference can be made to any commonly used method in the art, as described above. Figure 1 The location determines the group velocity dispersion function.

[0235] Next, the method involves using an initial model to determine a simulated dispersion function for each ray path.

[0236] The steps to determine the empirical dispersion function and the simulated dispersion function can be performed in any order (or actually simultaneously).

[0237] Next, the method includes determining an error value between the simulated dispersion function (determined from the model) indicating the difference between each ray path and the empirical dispersion function (determined from the detected response signal).

[0238] Finally, the method involves updating the initial model using the error values. Apart from new shear wave velocity values ​​(or any physical property values ​​used) associated with at least some elements, the updated model is essentially identical to the initial model in all respects. In other words, the second model is an updated version of the first model, incorporating a determined error value between the empirical dispersion function and the simulated dispersion function. This feedback process is typically built into inversion methods such as least squares, Markov chain Monte Carlo, etc., and is performed as part of the inversion procedure.

[0239] The process of determining the dispersion function of the simulation, determining the error value, and using that error value to determine the updated model is typically comprised of all parts of a one-step subroutine, which is then iterated using an inversion method such as least squares gradient descent inversion. In other words, each iteration uses the error value generated from the initial model and the simulated dispersion function of each ray path to determine the updated model, and then uses the resulting updated model as the initial model for the next iteration. Iteration continues until the error value reaches a termination condition, such as falling below a threshold.

[0240] Finally, in step 612, the final updated model is a two-dimensional or three-dimensional model of the target area with respect to one or more land properties generated (and, for example, optionally output to the user device).

[0241] The advantage of the one-step method is that by focusing on the ray path and performing single-step tomography and inversion based on the ray path (for example, not per cell), the computational burden is reduced, thereby reducing the time spent.

[0242] For completeness, the tomographic inversion operation used in Method 600 can be similar to, but different from, the operation used in multichannel analysis of surface waves (MASW). MASW has been described and is an existing technique for collecting surface wave information. Key differences between the described tomographic inversion and MASW methods include that MASW does not use tomographic inversion (instead using one-dimensional (1D) inversion); MASW is used in conjunction with active noise sources (such as a sledgehammer or falling heavy object), and MASW studies a two-dimensional line of interest along the surface. The target depth in MASW is approximately 5 to 30 m, therefore MASW cannot penetrate as deeply as the methods disclosed herein.

[0243] Please refer to Figure 8 An exemplary output of the method described herein is a final shear wave velocity model of a subsurface target region. The subsurface target region extends to a depth of 100 m along the x and y directions (z direction). The values ​​of the shear wave velocities are indicated by shading in the figure, and the transitions between regions of different shear wave velocities are visible, indicating the different composition or structure of multiple parts of the subsurface target region. Using the method described herein, the final shear wave velocity model can be determined with higher resolution and accuracy without incurring infeasible computation time. This subsurface model can be used to better understand the suitability of the subsurface target volume for supporting man-made structures above or within the subsurface target region.

[0244] As mentioned earlier, there may be more than two receivers. For example, there may be 100 receivers, 100 to 5000 receivers, 5000 receivers, or more than 5000 receivers.

[0245] There are many different types of receivers suitable for use in method 500. For example, the receiver in method 500 can be any one (or any combination thereof) of a seismic detector, pressure sensor, hydrophone, accelerometer, seismograph, vibration sensor, and transducer.

[0246] The target area can have different depths and locations relative to the surface. For example, in terms of depth below the surface, method 500 may be suitable for determining one or more land properties of a target area up to approximately 100 meters deep. For example, the target area may span 0 to 100 meters below the surface; it may span 0 to 45 meters below the surface; it may span 50 to 100 meters below the surface. Therefore, the target area can be entirely contained within an underground area. This makes the illustrated method well-suited for a wide variety of construction projects, including underground works.

[0247] In some implementations, the processing of the response signal in step 508 includes applying spectral whitening. This technique helps to enhance the representation of frequencies of interest for ambient noise and / or generated noise, thereby preventing the signal in the microseismic band from dominating the cross-correlation performed in step 510.

[0248] The model and the one or more land properties can be land properties excluding shear wave velocity, or land properties including those other than shear wave velocity, such as compressive wave velocity, density, elastic modulus, shear modulus, or, optionally, viscosity quality factors Qs and Qp, if a viscoelastic model is used. In general, the model can define multiple land property values.

[0249] Figure 9 A block diagram of one embodiment of a computing device 900 is shown, in which a set of instructions can be executed to cause the computing device to perform any or more of the methods discussed herein. In alternative embodiments, the computing device may be connected (e.g., networked) to other machines in a local area network (LAN), intranet, extranet, or the Internet. The computing device may operate as a server or client 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), tablet computer, set-top box (STB), personal digital assistant (PDA), cellular phone, network device, server, network router, switch, or bridge, or any machine capable of executing (sequentially or otherwise) a set of instructions specifying the actions to be taken by the machine. Furthermore, although only a single computing device is shown, the term "computing device" should also be understood to include any collection of machines (e.g., computers) that individually or collectively execute a set (or more) of instructions to perform any or more of the methods discussed herein.

[0250] An exemplary computing device 900 includes a processor 902, a main memory 904 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) (e.g., synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM)), a static memory 906 (e.g., flash memory, static random access memory (SRAM), etc.), and an auxiliary memory (e.g., a data storage device 918), the components communicating with each other via a bus 930.

[0251] Processor 902 represents one or more general-purpose processors, such as microprocessors, central processing units, etc. More specifically, processor 902 may be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, a processor implementing other instruction sets, or a processor implementing a combination of instruction sets. Processor 902 may also be one or more special-purpose processors, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), network processors, etc. Processor 902 is configured to execute processing logic (instructions 922) for performing the operations and steps discussed herein.

[0252] The computing device 900 may also include a network interface device 908. The computing device 900 may also 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 a touch screen), a cursor control device 914 (e.g., a mouse or a touch screen), and an audio device 916 (e.g., a speaker).

[0253] It is obvious that Figure 9 Some features of the computer device 900 shown may be absent. For example, one or more computing devices 900 may not require a display device 910 (or any associated adapter). This may be the case, for example, for a particular server-side computer device 900 that utilizes its processing power only without needing to display information to a user. Similarly, a user input device 912 may not be required. In its simplest form, the computer device 900 includes a processor 902 and a memory 904.

[0254] 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 one or more sets of instructions 922 are stored, embodying any one or more methods or functions described herein. During execution of the instructions 922 by computer system 900, the instructions 922 may also reside wholly or at least partially in main memory 904 and / or processor 902, which also constitute computer-readable storage media.

[0255] The various methods described above can be implemented by a computer program. This computer program may contain computer code arranged to instruct a computer to perform the functions of one or more of the methods described above. The computer program and / or code for performing these methods may be provided to a device, such as a computer, on one or more computer-readable media, or more generally on a computer program product. The computer-readable media may be temporary or non-temporary. The one or more computer-readable media may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or a propagation medium for data transmission, such as for downloading code via the Internet. Alternatively, the one or more computer-readable media may take the form of one or more physical computer-readable media, such as semiconductor or solid-state memory, magnetic tape, removable computer disk, random access memory (RAM), read-only memory (ROM), hard disk, and optical disk, such as CD-ROM, CD-R / W, or DVD.

[0256] In one implementation, the modules, components, and other features described herein may be implemented as discrete components or integrated into the functionality of hardware components such as ASICs, FPGAs, DSPs, or similar devices.

[0257] A "hardware component" is a tangible (e.g., non-transitory) physical component (such as a collection of one or more processors) capable of performing a specific operation and which can be configured or arranged in a specific physical manner. A hardware component may include dedicated circuitry or logic permanently configured to perform certain operations. A hardware component may be or include dedicated processors, such as field-programmable gate arrays (FPGAs) or ASICs. A hardware component may also include programmable logic or circuitry that is temporarily configured by software to perform certain operations.

[0258] Therefore, the phrase “hardware component” should be understood to encompass tangible entities that can be physically constructed, permanently configured (e.g., hardwired) or temporarily configured (e.g., programmed) to operate or perform certain operations described herein.

[0259] Furthermore, modules and components can be implemented as firmware or functional circuitry within a hardware device. Additionally, modules and components can be implemented as any combination of hardware devices and software components, or solely as software (e.g., code stored or otherwise embodied in a machine-readable or transportable medium).

[0260] Unless otherwise specifically stated, it will be apparent from the following discussion that throughout this specification, the use of terms such as “provide,” “calculate,” “update,” “generate,” “output,” “receive,” “process,” “execute,” “determine,” “select,” “compare,” and “identify” refers to the actions and processes of a computer system or similar electronic computing device that manipulate and convert data represented as physical (electronic) quantities in the registers and memories of the computer system into other data similarly represented as physical quantities in the computer system’s memory or registers or other such information storage, transmission, or display devices.

[0261] It should be understood that the above description is exemplary only and not restrictive. Many other embodiments will be apparent to those skilled in the art upon reading and understanding the above description. While this disclosure is described with reference to specific exemplary embodiments, it should be recognized that this disclosure is not limited to the described embodiments but can be implemented with modifications and variations within the spirit and scope of the appended claims. Therefore, the specification and drawings should be considered exemplary and not restrictive. The scope of this disclosure should be determined by referring to the appended claims and the full scope of their equivalents.

[0262] While at least one exemplary embodiment has been given in the detailed description above, it should be understood that many variations exist, and only some of these variations have been mentioned above. It should also be understood that this exemplary embodiment or these exemplary embodiments are merely examples and are not intended to limit the scope, applicability, or configuration of this disclosure in any way. Rather, the detailed description above will provide those skilled in the art with a convenient way of implementing this exemplary embodiment or these exemplary embodiments. It should be understood that various changes may be made to the function and arrangement of the elements without departing from the scope of this disclosure as set forth in the appended claims and their equivalents.

Claims

1. A method for determining one or more land properties of a target area beneath the surface of a water body's bed, the method comprising: Generate noise signals; Noise is generated by a noise source located within the water body based on the noise signal output; Receive a dataset containing a first response signal, the first response signal indicating generated noise measured at or near the surface by a first receiver arranged at a first location; Process the first response signal; Perform cross-correlation or deconvolution on the processed first response signal; as well as A two-dimensional (2D) or three-dimensional (3D) model of the target region with respect to one or more land properties is generated using the first response signal of cross-correlation or deconvolution.

2. The method of claim 1, wherein, The dataset further includes a second response signal indicating generated noise measured at or near the surface by a second receiver positioned at a second location, wherein the first location is different from the second location, and the method further includes: Process the second response signal; Perform cross-correlation or deconvolution on the processed second response signal; and Inversion is performed using the first and second response signals of cross-correlation or deconvolution to generate a two-dimensional '2D' or three-dimensional '3D' model of the target area with respect to the one or more land properties.

3. The method according to claim 2, wherein: The first response signal also indicates ambient noise measured by a first receiver positioned at the first location at or near the surface of the bed of the water body; and The second response signal also indicates the ambient noise measured by a second receiver positioned at or near the surface of the bed of the water body. The step of cross-correlation or deconvolution of the processed first response signal and the processed second response signal includes cross-correlation between the processed first response signal and 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 includes 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 water body, wherein processing the first response signal and / or the second response signal includes applying motion correction operations 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 water body.

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

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

8. The method according to any one of the preceding claims, wherein, The noise source is coupled to the towing cable of another vessel on or in the body of water.

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

10. The method according to any one of the preceding claims, wherein, The first receiver and / or the second receiver is at least one of a pressure sensor, a seismograph, a hydrophone, an accelerometer, a vertical accelerometer, a triaxial accelerometer, a particle velocity sensor, a fiber-optic sensor, a seismograph, a vibration sensor and / or a transducer, or an array of any such receiver.

11. The method according to any one of the preceding claims, wherein, The step of outputting noise from a noise source located in the water body according to a noise signal includes generating a pressure wave in the water body, the pressure wave being incident on the surface of the bottom of the water body at an angle in the range of 10 to 40 degrees.

12. The method according to any of the preceding claims, wherein, The noise signal is at least one of the following: Signals generated using pseudo-random binary sequences; The generated signal contains frequency components within a specific frequency range of 2-120Hz; and A signal output with a strength based on, matching, or equal to, the average strength of ambient noise received at a first receiver and / or a second receiver.

13. The method according to any one of the preceding claims, wherein, The one or more land properties include one or more elastic properties of the target area, such as shear velocity Vs.

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

15. A computer-readable medium containing instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 13.

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