Telecommunications ground surveys that can be measured

Abstract

A method for generating a ground survey of an area of ​​interest includes measuring DFOS data in an optical fiber located within a sensing distance of the area of ​​interest and determining subsurface characteristics of the area of ​​interest based on the measured DFOS data. The ground survey system may include an optical fiber, a DFOS device, and a processor configured to calculate the ground data based on the DFOS data.

Description

[Technical field]

[0001] [CROSS REFERENCE TO RELATED APPLICATIONS] This application claims the benefit of U.S. Provisional Application No. 63 / 256,079, filed October 15, 2021, which is incorporated by reference herein in its entirety. [Background technology]

[0002] The physical properties of the near-surface (10-300m above the Earth's crust) are important for the safe construction of new infrastructure. Soil investigations are common in urban and rural areas around the world, as well as offshore areas. For example, building engineers design structures taking into account the site grade, the soil strength that must support the building's foundation, the potential need to reinforce the foundation, and the saturated or unsaturated characteristics of the subsurface. Soil investigations are used in new building construction permit and detailing processes, and help property owners follow safe building practices.

[0003] Geotechnical investigations are also important for the analysis of earthquake and liquefaction hazards in earthquake-prone regions, such as the western United States, Mexico, Central and South America, the Caribbean, New Zealand, Japan, Indonesia, Malaysia, Thailand, Taiwan, India, Italy, Turkey, and Greece. Geotechnical seismic investigations aim to assess the physical properties of the near-surface to characterize the risk posed by future large earthquakes and high-amplitude ground motion scenarios. When an earthquake occurs, the magnitude of the earthquake is amplified in thick and unconsolidated zones because the seismic waves generated by the earthquake become trapped in shallow subsurface soil layers. This reverberation causes an amplification of ground motion, which has repeatedly been shown to be 5 to 20 times greater compared to adjacent zones that are more compact near the surface, or where rock mass is created at the surface such that no sedimentary layers exist.

[0004] Near-surface properties are often described in geological terms such as rock and / or soil type, layer thickness, and water table depth (e.g., "a 5 meter thick layer of sand above bedrock"), or using physical and material parameters such as density, porosity, shear modulus, bulk modulus, Poisson's ratio, and saturation. One method of communicating near-surface property information or the results of a geotechnical investigation is as a 1-D (vertical) property profile between identified points on the surface, or a 2-D "cross-section" image diagram that connects multiple vertical profiles to show lateral variations in the property along an identified line at the surface.

[0005] Near-surface properties can be assessed using different techniques. The most detailed property information comes from extracting deep vertical cores throughout the entire portion of near-surface interest at the site of interest, typically using a drilling machine. The core samples can then be visually assessed by hand, and the depth of the formation and rock type can be measured directly. This method provides an actual "ground truth" at the site of the core hole, but is intrusive, often requires intensive permitting, can be logistically difficult or impossible to operate into tight urban areas due to the size of the drill rig, and generally requires several weeks to complete drilling, thus having a very large project cost. In the construction of new offshore infrastructure, obtaining drill core samples is even more difficult. However, a central problem with drilling to obtain near-surface property information is that the information varies laterally. In some areas, the shallow soil thickness must be sampled horizontally every 10 to 100 meters to capture the degree of complexity required. Thus, while drill core analysis may be useful for a single 100m x 100m area, it is impractical and expensive for larger areas, such as urban areas with a footprint of 10,000m x 10,000m.

[0006] Another method to obtain near-surface topographic information by non-intrusive means is to apply geophysical methods such as seismic imaging to explore subsurface structures and materials. Seismic imaging involves deploying an assemblage of seismometer / geophone devices at the surface and setting up active controlled sources such as explosives, Betsy guns, or using VibroSeis trucks. In seismic imaging, common inertial seismometers, accelerometers, and / or hydrophones are deployed at the surface and seismic sources are detonated to generate the input seismic wave energy required for the experiment. Similar to x-ray or other forms of medical imaging, seismic imaging can be used to retrieve profiles of near-surface properties by characterizing subsurface material properties according to their seismic wave velocities using the principles of reflection and refraction and comparing them to well-established material properties. This type of technique has been applied to geotechnical field investigations since the 1960s. The advantage of using geophysics for ground investigation is that seismic surveys are non-invasive, can be rapidly deployed in hours up to days covering areas of 1,000m x 1,000m, and can be designed to provide lateral properties (2-D information) as well as 1-D profiles. A limitation of ground investigation, especially in urban and offshore areas, is that the investigation can be difficult to tolerate due to the need for large seismic sources underwater or in urban areas where marine mammals are present. Therefore, there is a need for a non-invasive ground investigation method that overcomes the above challenges, among others. Summary of the Invention [Means for solving the problem]

[0007] Embodiments of the present disclosure provide methods, systems, and devices for performing unrestricted ground investigations without the drawbacks discussed above. In particular, the present disclosure addresses ground investigation challenges by providing systems and methods for acquiring geophysical ground investigation information at the scale of urban areas or offshore areas using one or more distributed optical fiber sensing systems deployed over a telecommunications network in the urban or offshore areas, a technique referred to as surveyable telecommunication ground investigation.

[0008] In embodiments of the systems and methods according to the disclosed embodiments, principles of seismic wavefield correlation analysis are applied to extract coherent seismic signals from background natural and / or man-made seismological activity that is typically present in urban areas. Ultimate sources of this background seismic activity may include generators, hydrological pumps, agricultural equipment, acoustic alarms, construction and excavation equipment, vehicles, pedestrians, animals, wind, rain, earthquakes, windstorms, and ocean waves. It will be appreciated that multiple such activities generate the background seismic energy used to perform geotechnical investigations according to the present disclosure.

[0009] Methods are disclosed for determining how to perform this type of survey in offshore and urban areas using parameters that improve the accuracy and efficiency of the survey's execution. Various parameters can determine the likelihood of the survey quality using prior information. Various methods can complement poor quality survey areas with artificial seismic sources to complement natural and / or anthropogenic sources.

[0010] The disclosed devices, systems, and methods obtain near-surface property information in a continuous and efficient manner throughout a city or along a fiber optic cable laid on the ocean floor (e.g., extending for 50 km or more). The output can be a model of the subsurface properties describing the subsurface structure in a one-dimensional sense (vertical profile), two-dimensional sense (cross-sectional images), or three-dimensional sense (volumetric / layered models). This is accomplished efficiently without the typical fieldwork required for geophysical ground surveys. This type of survey can be performed repeatedly for many years to decades.

[0011] Hereinafter, embodiments will be described in detail with reference to the drawings, in which like reference numerals represent like elements. The accompanying drawings are not necessarily drawn to scale. Some of the drawings may be simplified by omitting selected features for the purpose of more clearly showing other underlying features. Such omission of elements in some figures does not necessarily indicate the presence or absence of a particular element in any of the exemplary embodiments, unless expressly disclosed in the corresponding written description. [Brief description of the drawings]

[0012] [Figure 1A] 1 illustrates an example of a DFOS device for transmitting and receiving laser light, according to an embodiment of the disclosed subject matter. [Figure 1B] 1 illustrates an example geographic region having optical fiber infrastructure, in accordance with an embodiment of the disclosed subject matter. [Diagram 2] 1 illustrates an example of an algorithmic workflow according to an embodiment of the disclosed subject matter. [Diagram 3] 1 illustrates an example of an algorithmic workflow for recording DFOS data, according to an embodiment of the disclosed subject matter. [Figure 4] 1 illustrates an example of an algorithmic workflow for verifying DFOS data quality, according to an embodiment of the disclosed subject matter. [Diagram 5] 1 illustrates an example of an algorithmic workflow for processing DFOS data, according to an embodiment of the disclosed subject matter. [Figure 6] 1 illustrates an example of an algorithm workflow for detecting earthquake phases, according to an embodiment of the disclosed subject matter. [Figure 7] 1 illustrates an example of an algorithmic workflow for modeling a subsurface environment, according to an embodiment of the disclosed subject matter. [Figure 8] 1 illustrates an example of a conceptual representation of background seismological activity and subsurface geological information, according to an embodiment of the disclosed subject matter. [Figure 9] 1 illustrates an example of a geotechnical investigation profile according to an embodiment of the disclosed subject matter. [Figure 10] 1 shows an example of seismic noise recorded along a fiber optic cable and representative of DFOS data in accordance with an embodiment of the disclosed subject matter. [Figure 11A] 1 illustrates an example of extraction of coherent seismic waves from recorded seismic noise according to an embodiment of the disclosed subject matter. [Figure 11B] 1 illustrates an example of extraction of coherent seismic waves from recorded seismic noise according to an embodiment of the disclosed subject matter. [Figure 12A] 13 shows an example of intermediate processing results based on coherent seismic waves extracted from DFOS data, according to an embodiment of the disclosed subject matter. [Figure 12B] 13 shows an example of intermediate processing results based on coherent seismic waves extracted from DFOS data, according to an embodiment of the disclosed subject matter. [Figure 12C] 13 shows an example of intermediate processing results based on coherent seismic waves extracted from DFOS data, according to an embodiment of the disclosed subject matter. [Figure 13] 1 illustrates example velocity versus depth results obtained from processed DFOS data at one location along an optical fiber in accordance with an embodiment of the disclosed subject matter. [Figure 14] 1 illustrates an example of a 2D model of a ground investigation, according to an embodiment of the disclosed subject matter. [Figure 15] 1 illustrates an example relationship between wave velocity and rock type, according to an embodiment of the disclosed subject matter. [Figure 16] 13 conceptually illustrates how velocity is converted to soil type according to an embodiment of the disclosed subject matter. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] 1A, a DFOS instrument 100 shines light into an optical fiber 150 and records the returning energy 110 as a function of time. As shown in FIG 1A, the fiber 150 may be buried under or near a road 140. One or more vehicles 130 travel on the road 140, which generate seismic energy that may be considered a source used for geotechnical investigations according to the present disclosure.

[0014] DFOS measurements are sensitive to the movement / deformation of the optical fiber 150 and, therefore, the surroundings of the optical fiber. For example, during an earthquake, as seismic waves propagate near the surface, the soil undergoes compression and rarefaction, and this movement is transmitted to the optical fiber 150.

[0015] DFOS measurements can be made wherever continuous optical fiber exists, one end of which can be connected to the DFOS instrument 100. The fiber can be laid in any orientation, or even wrapped helically around a central cylinder, to introduce two or more motion / deformation components in each gauge length of the DFOS measurement. Existing optical fiber laid for different purposes can be utilized for DFOS measurements. Multiple fibers can be connected in series and used for DFOS with one instrument, or multiple DFOS channels (analyzed with the same DFOS instrument or with separate DFOS instruments) can be used to record DFOS data in the same vicinity. It is advantageous to use existing telecommunications fiber already installed for DFOS measurements to perform geotechnical investigations. Such an approach overcomes many of the shortcomings of other geotechnical investigation techniques mentioned above.

[0016] The optical fiber 150 may span large distances within a geographic region, as shown by the trace 170 of the fiber 150 in the map of FIG. 1B. In an embodiment, the optical fiber bundle includes one or more fibers not normally used or allocated for use for communication. Light can be injected into such a fiber or fibers, also called dark fibers. As shown in FIG. 1A, the seismic energy source used in this process can be provided by man-made noise, such as vehicular traffic, or natural energy, such as from ocean waves. In an embodiment, a supplemental acoustic source 120 can be used. The light source 120 can be placed in the area of ​​interest to generate energy that can be seen by the optical fiber. The source 120 may be an explosive, a soil compactor, or other device, such as a waving hammer, tamper, or air gun, among others.

[0017] The light can be of any type, including simple pulses, chirped pulses, or continuous wave. In an embodiment, the light is laser light. In a further embodiment, the laser light is in the infrared or near infrared frequency range. The energy returning from the optical fiber sensing path, shown as an arrow in FIG. 1A, may have been Rayleigh scattered as a result of light scattering characteristics along the length of the optical fiber, or the returning energy may have been caused by some Brillouin or Raman transition from the incident wavelength. The scattered light may be forward scattered, back scattered, or both.

[0018] The return light can be analyzed optically, digitally, or both. The output of this process is a data set, referred to herein as DFOS data or DFOS record, that contains values ​​related to the optical fiber state for specific time samples at all sensor locations in the fiber. It is also possible to envision using the telecommunications receiver statistics themselves for DFOS, extracting information about the fiber state, such as the time rate of change of the length of the fiber, or properties of polarization or time of flight from one end to the other, which may carry information about the state of stress or strain in the fiber at a point or over its length, without the use of dedicated equipment at one end.

[0019] In one embodiment, the DFOS instrument may be an interrogator unit ("IU"), such as the unit for distributed acoustic sensing described in WO2018 / 045433A1, the entirety of which is incorporated herein by reference.

[0020] In some cases of DFOS, there is no physical fixed length sensor location. In principle, the gauge length is the distance along the optical fiber where one DFOS value is detectable. For example, in one type of pulsed Rayleigh-based DFOS, the gauge length may be 10m and the recorded data may be strain, in which case the 10m gauge length is the "reference length" where displacements cause the resulting strain. The gauge length can affect many aspects of the measurement, including the quality of the measurement, the gain of the measurement, the finest spatial size that can be analyzed without spatial aliasing, and the recorded DFOS data volume. The gauge length can be set in hardware and / or software. The gauge length can record DFOS data at multiple gauge lengths if set in software.

[0021] DFOS measurements are sensitive to the movement / deformation of the optical fiber and therefore the movement / deformation of its surroundings. For example, during an earthquake, as seismic waves propagate near the surface, the soil undergoes compaction and rarefaction, and this movement is transmitted to the optical fiber. In the absence of a major earthquake, small vibrations, often referred to as "seismic noise", are present in the recording due to the low self-noise of the DFOS measurement method. These background vibrations are the result of natural seismological processes such as ocean wave interactions, tides, other small earthquakes, wind, and anthropogenic sources of seismic waves such as vehicles, trains, excavators, construction equipment, generators, hydrological pumps, alarms, and other human technologies. Most of the seismic energy generated by these natural and anthropogenic processes propagates for more than 100 kilometers before attenuating to levels where it can no longer be detected. For example, seismic waves emitted by moving freight trains and wind turbines can be seen up to 40 kilometers from their source.

[0022] In various embodiments, DFOS measurements made along the fiber path are correlated and the extracted coherent seismic background phase information (i.e., wave speed) is used to obtain near-surface information beneath a segment of telecommunications optical fiber.

[0023] It should be noted that using DFOS is different from other approaches such as discrete fiber Bragg grating sensing (FBG). In FBG, a sensor or multiple sensors are added along the optical fiber, or a new optical fiber is used to connect multiple such sensors, and light is transmitted to the sensor. Such an approach is less desirable than DFOS, since new sensors (and possibly new fibers) must be added to the existing telecommunications infrastructure, whereas in DFOS, the existing optical fiber itself acts as the sensor, and there is no need to add more sensors to the existing fiber along the fiber or new fibers. Furthermore, the entire optical fiber is used as the sensor, and thus any location along the fiber selected for sensing allows for focusing on a specific geographical area and location along the fiber for ground investigation.

[0024] This disclosure highlights an exemplary workflow algorithm that starts with optical processes and ends with subsurface property information. With reference to FIG. 2, an exemplary workflow algorithm for generating a subsurface survey is shown. In S201, DFOS data is collected, as described in more detail below with reference to FIG. 3. In S203, the collected data is checked to determine if it meets certain quality parameters. If the collected data does not pass the data quality check, new data is recorded and stored. More details on the data quality check are provided below with reference to FIG. 4. If the data passes the data quality check, processing proceeds to S205 for processing of the DFOS data, which is described in more detail below with reference to FIG. 5.

[0025] In S207, the process selects a seismic phase from the output of S205, as described in more detail below with reference to Figure 6. In S209, the process models the subsurface of the area to be studied, thereby generating a ground survey, as described in more detail below with reference to Figure 7. Further details of these processing steps are described below.

[0026] Figure 3 provides further details of an exemplary embodiment for collecting DFOS data (Figure 2, S201). It will be appreciated that optical fibers are generally part of an existing infrastructure, such as communication cables, used to carry digital traffic encoded as light pulses. Often, such communication infrastructure includes bundles of multiple optical fibers, not all of which are utilized. Unutilized optical fibers (i.e., dark cables) can be connected to a DFOS device, such that light can be launched into and received from the fiber.

[0027] The first step (S301) involves shining light into an optical fiber using a light emitting device and then receiving the light using a receiving device at the same or a different end of the optical fiber (S303). In an embodiment, the light emitting device is a laser emitter capable of emitting laser light. In an embodiment, the emitted laser light is in a band of frequencies spanning a wavelength of 1550-1580 nanometers. In a further embodiment, a single frequency is used. In a further embodiment, multiple frequency bands separated from one another are used.

[0028] The result of S301 is a DFOS data stream containing the distributed vibration or temperature fields acting on the fiber optic cable over a time interval. In DFOS, the spatial sampling rate or spatial resolution can be from millimeters to kilometers or as small as kilometers. Also, the temporal sampling rate may range from microseconds to hours. The actual spatial and temporal sampling rates are software selectable and are chosen to capture ground motion underground, typically one spatial sensing point every meter along the fiber, and one data point every 0.1-0.001 seconds (10-1000 Hz). The second step of the algorithm is to record a subset of the DFOS data stream, where it is desired to determine subsurface properties. Recordings consist of M sensing channels (positions in the fiber) and T minutes of continuous data, in S307. Figure 10 shows an exemplary result of S307.

[0029] The optical fiber may be included as part of an optical fiber bundle used for communications or other infrastructure purposes. In an embodiment, the optical fiber from which the light is emitted is a dark fiber, meaning that it is not normally used as part of infrastructure purposes. In some embodiments, multiple optical fibers are utilized in parallel, resulting in multiple ground surveys, and processing may be performed separately on each of the multiple fibers, so that the multiple ground surveys can be combined to improve accuracy. In a further embodiment, multiple parallel optical fibers are used, and data from the parallel fibers is combined (e.g., by averaging values) at an early stage of processing to improve the accuracy of the final ground survey.

[0030] The step of subsetting the data set may also be accomplished without actually storing the subset data set if sufficient RAM is available to store the data set. The subset data set may be all channels for a 6 hour period, where M is equal to the number of channels in the DFOS data stream (e.g., 12000) and T is equal to 2160.

[0031] Referring again to FIG. 2, a quality check is performed in S203. An exemplary embodiment of S203 is detailed in FIG. 4. If the quality check passes, the workflow is allowed to continue with the subset data set. If the quality check identifies poor quality, the workflow returns to the DFOS data stream and selects a new raw DFOS subset portion. Any one of the following steps can be used as a criterion for discarding the recorded data and obtaining a new data set. Although the following processes are listed in a particular order, it will be understood that the following data quality checks may be performed in any order.

[0032] In S401, the signal-to-noise ratio of the recorded data is verified. The signal-to-noise ratio relates to the recovered fidelity of the vibration signal above the optical noise floor. Since the optical noise of the DFOS is generally found to vary over the range of the optical fiber array, this step can be used to establish where there is usable data.

[0033] In S403, the attenuation characteristics of the data are verified by applying statistical tests of seismic attenuation to verify that the signal is valid. For example, a model of how underground seismic waves attenuate can be fitted to the data, and then any subset of data for which the test fails can be rejected.

[0034] In S405, a low optical noise check is performed by evaluating the time-invariant optical noise characteristics, or alternatively, it is possible to eliminate these effects.

[0035] At S407, a channel range check determines the linearity of the subset channels to determine if the curvature of the subset section is outside a range of usefulness.

[0036] In S409, a map check is used to verify that the selected optical fiber has a physical layout that does not preclude accurate measurements, such as being located inside a pit or building or along a bridge pier, or being located far from the earthquake source, or being too close to the earthquake source, etc. If the data quality check is satisfied, processing proceeds to S205, details of which are shown in FIG.

[0037] The overall processing in S205 can be thought of as seismic interferometry applied to segments in the DAS array (i.e., virtual sensor locations specified along the optical fiber). Seismic interferometry is a process in which adjacent channels are cross-correlated with respect to one another. This step "enhances" the coherent components and "reduces" the incoherent components. Therefore, the process can also be thought of as seismic noise compression. The inputs are shown diagrammatically in Figure 10, with time on the vertical axis and channel number along the horizontal axis.

[0038] Data from N regularly or irregularly spaced sensors (N<=M) are convolved with P neighboring sensors and averaged over time to extract coherent components from the background wavefield. The process loops over sensor locations in S501 and window start times in S503. DFOS data for a particular sensor location and time is cross-correlated over multiple time windows with DFOS data from neighboring sensors in S505. In an embodiment, one minute cross-correlations are stacked over three hours until the "extraction" converges to a clear image, as shown in the figure. Figures 11A and 11B depict a wavefield where the coherent energy is clearly visible.

[0039] More specifically, the result is a compressed wavefield data set of size N×P.

[0040] Next, the process of FIG. 2 continues in S207 with the detection of seismic phases, the details of which are shown in FIG. 6. The variation in seismic wave velocity within the Earth, the possibility of conversion between compressional (P) and shear (S) waves, results in many possible wave paths. Each path produces a separate seismic phase on the seismogram. Referring to FIG. 6, a schematic diagram of the process of seismic phase detection is shown. At a high level, the process applies several different seismological processing techniques to analyze, model, and interpret the compressional wavefield data with respect to subsurface property information. The process recognizes that the velocity of seismic waves depends on the frequency of the wave. In most places on Earth, seismic wave velocity increases with increasing depth due to compaction of materials. Higher frequency (relatively short wavelength) seismic waves are more sensitive to shallow layers, while lower frequency (relatively long wavelength) seismic waves are more sensitive to deeper layers.

[0041] Different processes in this step may yield different results, for example, techniques applied to surface wave analysis may yield subsurface property information related to shear wave velocity (Vs) as a function of space (Z), while analysis of body wave information may yield subsurface property information related to compressional wave velocity (Vp). Such information may be combined to produce a geotechnical investigation report.

[0042] In one embodiment, the seismic phase detection process receives as input the wavefield dataset from S205, the sensor locations, and a list of possible velocities of seismic waves in the area under study. The process loops over the sensor locations in S601 and also over the velocities to create a table based on the wavefield and velocities of interest. This can be thought of as a grid search of the velocity list. In S605, a table of variances is calculated based on a Fourier transform (e.g., FFT) of the table above. An example of the resulting dataset is shown in FIG. 12A, where velocity is plotted against frequency. The result can be thought of as wave velocity for a set of frequencies seen by a particular sensor (i.e., a portion of optical fiber), with the color in the plot representing the velocity, and the brighter central region 1200 representing the highest velocity in the plot.

[0043] In S607, the seismic phase is chosen by finding peaks in the data. Figure 12B shows the data set of Figure 12A after peak detection has been applied, and only the highest energy levels are saved as shown in 1210. This intermediate data is further processed, for example by applying a best-fit method, to return a velocity vs. frequency curve (or curves 1220 and 1240), as shown in Figure 12C. In an embodiment, the first 30 meters of depth at 10 Hz are of interest, which corresponds to VS30 data, defined as the average seismic shear wave velocity from the surface to a depth of 30 meters. At this measurement depth, the optical fiber depth does not significantly affect the measurement.

[0044] The process then continues to S209 where the subsurface is modeled based on the seismic phase selected in S207. Details of S209 are shown in FIG. 7. Conceptually, the process converts a 1-D model of shear wave velocities that represent the subsurface geology. This process can use Markov Chain Monte Carlo to efficiently explore the possible model parameter space and identify the best-fit final model family, as shown by the results of the process in FIG. 13.

[0045] Referring to Fig. 7, in S701, a set of models is generated considering the wave velocities of various subsurface formations shown in Fig. 15. In an embodiment, the number of models is 100,000 or less. These models are then used to calculate synthetic seismic phase selections in S703. In S705, the synthetic seismic phase selections are compared with the calculated phase selections from S207. The most similar synthetic phase selection is selected using a method such as RMS to minimize the error.

[0046] In S707, a model of wave velocity versus depth is generated based on the selected synthetic phase selection, as shown in Figure 13. The model of Figure 13 can be further refined, for example, by averaging the wave velocity versus depth lines into a single line and mapping actual substrate materials to each wave velocity based on known properties of the substrate, as shown in Figure 16.

[0047] The results in Figures 13 and 16 represent the subsurface conditions at a particular sensor location (i.e., a segment of optical fiber). These locations can be combined to show the subsurface conditions over a long span of optical fiber, as shown in Figure 14, where the color of each pixel represents the wave speed in Km / s. As is evident from the example in Figure 14, three different substrates, each with a range of wave speeds, extend within the study area at various depths. The sudden change in wave speed (color in the plot) at a particular row represents the boundary between two separate layers of the substrate. This result can be considered the final result, or can be further analyzed into data such as that shown in Figure 9.

[0048] Further embodiments are as follows. According to a first further embodiment, a method for generating a ground survey of an area of ​​interest is provided, comprising measuring DFOS data in an optical fiber located within a sensing distance of the area of ​​interest and determining subsurface characteristics of the area of ​​interest based on the measured DFOS data. According to a second further embodiment, the method of the first further embodiment is provided, wherein the measuring of the DFOS data comprises measuring induced strain in the optical fiber cable. According to a third further embodiment, the method of the first further embodiment is provided, wherein the measuring of the DFOS data comprises cross-correlating adjacent channels with respect to one channel to amplify coherent noise components and suppress incoherent noise components. According to a fourth further embodiment, the method of the third further embodiment is provided, wherein the measuring of the DFOS data comprises stacking time segments of cross-correlation duration T1 over a total duration T2 that is longer than T1.

[0049] According to a fifth further embodiment, the method of the second further embodiment is provided, wherein the distortion is induced in the fiber optic cable by background seismic noise. According to a sixth further embodiment, the method of the fifth further embodiment is provided, wherein the background seismic noise is caused by at least one of generators, vehicles, construction and drilling equipment, pumps, machinery, infrastructure, wind, and ocean waves. According to a seventh further embodiment, the method of the second further embodiment is provided, wherein determining the subsurface properties of the area of ​​interest based on the measured DFOS data includes measuring seismic wave velocity as a function of frequency. According to an eighth further embodiment, the method of the first further embodiment is provided, wherein determining the subsurface properties of the area of ​​interest based on the measured DFOS data includes modeling the subsurface formations below the fiber optic cable based on the calculated wave velocity as a function of frequency. According to a ninth further embodiment, the method of the seventh further embodiment is provided, wherein measuring the seismic wave velocity includes extracting coherent surface waves at a predetermined spatial resolution.

[0050] According to a tenth further embodiment, the method of the ninth further embodiment is provided, wherein determining the subsurface properties of the area of ​​interest comprises modeling the subsurface formations based on frequency dependence of the velocity of seismic waves at a plurality of depths. According to an eleventh further embodiment, the method of the tenth further embodiment is provided, wherein determining the subsurface properties of the area of ​​interest comprises converting to a 1-D model of shear wave velocities representative of the subsurface geology by applying Markov Chain Monte Carlo to explore a space of possible model parameters. According to a twelfth further embodiment, the method of the second further embodiment is provided, wherein determining the subsurface properties of the area of ​​interest based on the measured DFOS data comprises measuring seismic wave velocities as a function of offset. According to a thirteenth further embodiment, the method of the twelfth further embodiment is provided, wherein determining the subsurface properties of the area of ​​interest comprises modeling the subsurface formations based on measuring the travel time of seismic waves penetrating the subsurface. According to a fourteenth further embodiment, the method of the twelfth further embodiment is provided, wherein determining the subsurface properties of the area of ​​interest comprises modeling the subsurface formations based on measuring the attenuation of seismic waves penetrating the subsurface.

[0051] According to a fifteenth further embodiment, there is provided the method of the tenth further embodiment, wherein determining the subsurface properties of the area of ​​interest comprises converting to a 1-D model of compressional and / or shear wave velocities representative of the subsurface geology by applying seismic tomography. According to a sixteenth further embodiment, there is provided the method of the tenth further embodiment, wherein determining the subsurface properties of the area of ​​interest comprises calculating a ratio of compressional and shear wave velocities. According to a seventeenth further embodiment, there is provided the method of the first further embodiment, further comprising connecting the DFOS device to an optical fiber, and measuring the DFOS data comprises transmitting light from the DFOS device to the optical fiber and receiving refracted light from the optical fiber. According to an eighteenth further embodiment, there is provided the method of the first further embodiment, wherein measuring the DFOS data comprises measuring strain. According to a nineteenth further embodiment, there is provided the method of the first further embodiment, wherein measuring the DFOS data comprises measuring ground motion.

[0052] According to a twentieth further embodiment, the method of the first to sixteenth further embodiments is provided, further comprising providing an active seismic source within sensing range of the optical fiber and emitting seismic energy from the active source. According to a twenty-first further embodiment, the method of the first to seventeenth further embodiments is provided, wherein the survey is performed in one dimension. According to a twenty-second further embodiment, the method of the first to seventeenth further embodiments is provided, wherein the survey results in a two-dimensional profile of the subsurface along the fiber path. According to a twenty-third further embodiment, the method of the first to seventeenth further embodiments is provided, wherein the survey is performed in three dimensions. According to a twenty-fourth further embodiment, the method of the twenty-third further embodiment is provided, wherein the result is a subsurface volume with layers and isopach lines across the region. According to a twenty-fifth further embodiment, the method of the first to twenty-first further embodiments is provided, wherein the optical fiber is installed horizontally in the water, either above or below the seabed.

[0053] According to a twenty-sixth further embodiment there is provided a DFOS apparatus comprising an optical transmitter configured to transmit light into an optical fiber, a receiver configured to receive light from the optical fiber, and a controller configured to perform a method defined in any of the first to twenty-fifth further embodiments.

[0054] According to a twenty-seventh further embodiment, there is provided a system for processing seismic data comprising one or more DFOS devices according to the twenty-sixth further embodiment and one or more optical fibers operably connected to the one or more DFOS devices.

[0055] According to a twenty-eighth further embodiment, there is provided a method according to any one of the first to twenty-fifth further embodiments, where the result of the investigation is lithological information for the top 50 m, including rock and soil type, layer thickness, and layer depth. According to a twenty-ninth further embodiment, there is provided a method according to any one of the first to twenty-fifth further embodiments, where the result of the investigation is physical parameters including density, shear modulus, bulk modulus, Poisson's ratio, Vp / Vs ratio, saturation, layer thickness, and layer depth. According to a thirtieth further embodiment, there is provided a method according to any one of the first to twenty-fifth further embodiments, where the result of the investigation is a Vs30 value, or a different statistical representation of seismic shear wave velocity information (e.g., average or weighted average or median of seismic shear wave velocity in the top 30, 40, 50 meters). According to a thirty-first further embodiment, there is provided a method according to any one of the first to twenty-fifth further embodiments, where the result of the investigation is hydrological information including water table depth, recharge state, hydraulic health stage, total available water, water banking volume, and permeability.

[0056] According to a further embodiment of the 32nd, the method of the further embodiments of the 1st to 25th is provided, wherein the result of the investigation is a deep acoustic at a depth of >150m. According to a further embodiment of the 33rd, the method of the further embodiments of the 1st to 25th is provided, wherein the result of the investigation is a measurement of the depth of the optical fiber cover. According to a further embodiment of the 34th, the method of the further embodiments of the 1st to 30th is provided, wherein the recorded signal used for the ground investigation is natural seismic energy and artificially generated seismic energy. According to a further embodiment of the 35th, the method of the further embodiments of the 1st to 30th is provided, wherein the recorded signal used for the ground investigation is natural seismic energy. According to a further embodiment of the 36th, the method of the further embodiments of the 1st to 30th is provided, wherein the recorded signal used for the ground investigation is artificially generated seismic energy.

[0057] According to a thirty-seventh further embodiment, there is provided a geotechnical surveying system comprising a DFOS device including an optical transmitter configured to transmit light into an optical fiber, a receiver configured to receive light from the optical fiber, and a controller configured to perform the method defined in any of the first to twenty-second further embodiments. According to a thirty-eighth further embodiment, there is provided a system of the thirty-seventh further embodiment, further comprising an optical fiber. According to a thirty-ninth further embodiment, there is provided a system of the thirty-eighth further embodiment, wherein the optical fiber is part of a fiber bundle used for communication. According to a fortieth further embodiment, there is provided a system of the thirty-seventh further embodiment, further comprising at least one active seismic source configured to generate and output seismic energy into the ground to generate seismic waves detectable by the DFOS device.

[0058] According to a forty-first further embodiment, there is provided the system of the thirty-seventh further embodiment, wherein the result of the survey is rock information for the top 50 m, including rock and soil type, layer thickness, and layer depth.

[0059] According to a further embodiment of the 42nd, the system of the further embodiment of the 37th is provided, where the result of the survey is physical parameters including density, shear modulus, bulk modulus, Poisson's ratio, Vp / Vs ratio, saturation, formation thickness and formation depth. According to a further embodiment of the 43rd, the system of the further embodiment of the 37th is provided, where the result of the survey is a Vs30 value or a different statistical representation of seismic shear wave velocity information (e.g. average or weighted average or median of seismic shear wave velocity in the top 30, 40, 50 meters). According to a further embodiment of the 44th, the system of the further embodiment of the 37th is provided, where the result of the survey is hydraulic information including water table depth, recharge state, hydraulic health stage, total available water volume, water banking volume, permeability. According to a further embodiment of the 45th, the system of the further embodiment of the 37th is provided, where the result of the survey is deep acoustics at depths of >150m.

[0060] According to a 46th further embodiment, the system of the 37th further embodiment is provided, where the result of the investigation is a measurement of the depth of the optical fiber cover. According to a 47th further embodiment, the system of the 37th further embodiment is provided, where the recorded signal used for the ground investigation is natural seismic energy generated by at least one of ocean waves, wind, vehicles, infrastructure, and / or buildings. According to a 48th further embodiment, the system of the 40th further embodiment is provided, where the at least one active seismic source includes swinging a hammer or using a tamper, an air gun, or an explosive. According to a 49th further embodiment, the system of the 37th to 48th further embodiments is provided, where the recorded signal used for the ground investigation is natural seismic energy and artificially generated seismic energy.

[0061] It is therefore apparent that there has been provided, in accordance with the present disclosure, a distributed fiber optic sensing system, apparatus, and method for providing scalable telecommunications geotechnical surveying. Many alternatives, modifications, and variations are enabled by the present disclosure. Features of the disclosed embodiments can be combined, rearranged, omitted, etc., within the scope of the invention to produce additional embodiments. Furthermore, some features may be used to advantage without the corresponding use of other features. Accordingly, applicants intend to embrace all such alternatives, modifications, equivalents, and variations that are within the spirit and scope of the present invention.

Claims

1. Measure DFOS data in optical fibers located within a sensing distance of a target area; determining subsurface characteristics of the target area based on the measured DFOS data; 1. A method for generating a geotechnical survey of a target area, comprising:

2. The method of claim 1 , wherein measuring the DFOS data includes measuring at least one of strain, strain rate, velocity, displacement, pressure, motion, or acceleration induced in the optical fiber.

3. The measurement of the DFOS data is Cross-correlating adjacent channels with respect to one channel to amplify coherent noise components and suppress incoherent noise components; Stacking time segments of the cross-correlation period T1 over the entire period T2, which is longer than T1; Including, The method of claim 1.

4. the strain is induced in the optical fiber by background seismic noise; the background seismic noise is caused by at least one of generators, vehicles, construction and drilling equipment, pumps, machinery, infrastructure, wind, and ocean waves; The method of claim 2.

5. Determining the subsurface characteristics of the area of ​​interest based on the measured DFOS data includes: Measure seismic wave velocity as a function of frequency, modeling subsurface formations beneath the optical fiber based on the calculated seismic wave velocities as a function of frequency, or modeling subsurface formations based on frequency dependence of seismic wave velocities at multiple depths; converting the data into a 1-D model of compressional and / or shear wave velocities representative of the subsurface geology by applying Markov Chain Monte Carlo methods to explore the space of possible model parameters or by applying seismic tomography; Calculate the ratio of the compressional wave velocity to the shear wave velocity The method of claim 1 , comprising:

6. Determining the subsurface characteristics of the area of ​​interest based on the measured DFOS data includes: Measure seismic velocity as a function of offset, Modeling subsurface formations based on measuring the travel time or attenuation of seismic waves penetrating the subsurface; The method of claim 1 , comprising:

7. the results of the geotechnical investigation are a two-dimensional profile of the subsurface along the path of the optical fiber; or the results of said ground investigation are three-dimensional; or The result of the geotechnical investigation is a subsurface volume with layers and isopach lines across the area; The method of claim 1.

8. The results of the ground investigation are as follows: Rock information in the top 50m, including rock and soil type, layer thickness, and layer depth; or physical parameters including density, shear modulus, bulk modulus, Poisson's ratio, Vp / Vs ratio, degree of saturation, layer thickness, and layer depth; or Vs30 values, or different statistical representations of seismic shear wave velocity information, The method of claim 1.

9. The results of the ground investigation are as follows: Hydrological information including water depth, recharge status, hydraulic health stage, total available water, water banking volume, and infiltration rate; or Deep exploration at depths >150m, or a measurement of the depth of coverage of the optical fiber; The method of claim 1.

10. The method of claim 1 , wherein the recorded signals used in the ground investigation are at least one of natural seismic energy and artificially generated seismic energy.

11. an optical transmitter configured to transmit light into an optical fiber; a receiver configured to receive light from the optical fiber; a control device configured to execute a method for generating a geotechnical survey of a target area; a DFOS apparatus comprising: A ground investigation system comprising: measuring DFOS data in the optical fiber located within a sensing distance of an area of ​​interest; determining subsurface characteristics of the target area based on the measured DFOS data; A ground investigation system, including:

12. The optical fiber is further provided, the optical fiber is part of a fiber bundle used for communications; The ground investigation system according to claim 11.

13. 13. The ground investigation system of claim 11 or claim 12, further comprising at least one active seismic source configured to generate and output seismic energy into the earth to produce seismic waves detectable by the DFOS device.

14. an optical transmitter configured to transmit light into an optical fiber; a receiver configured to receive light from the optical fiber; a control device configured to execute the method for generating a geotechnical survey of an area of ​​interest according to any one of claims 1 to 10; A DFOS device comprising:

15. One or more of the DFOS devices of claim 14; one or more optical fibers operably connected to one or more of said DFOS devices; An apparatus for processing earthquake information, comprising:

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