Full waveform inversion method based on surface wave dispersion inversion driving, control device and storage medium

Abstract

The embodiment of the invention provides a full waveform inversion method based on surface wave dispersion inversion driving, a control device and a storage medium, and belongs to the technical field of seismic inversion. The full-waveform inversion method comprises the following steps: acquiring a Rayleigh surface wave observation signal through a preset detection point according to a three-station interference method; according to one-dimensional surface wave frequency dispersion inversion, through the obtained Rayleigh surface wave observation signal, obtaining a corresponding one-dimensional shear wave velocity representing a central area of the detection point pair; constructing a corresponding three-dimensional velocity model based on the one-dimensional shear wave velocity body; and performing full-waveform inversion on the three-dimensional velocity model to obtain a full-waveform inversion result of the region. According to the embodiment of the invention, aiming at passive source seismic signals, based on a three-station interference method, the high-signal-to-noise-ratio Rayleigh surface wave signals among the one-dimensional linear stations are extracted and enhanced, the surface wave signals are used as the input of the whole inversion process, the inversion result is not influenced by background noise quality, and the precision is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic inversion, and in particular to a full waveform inversion method, a control device and a storage medium based on surface wave dispersion inversion drive. Background Art

[0002] Surface wave tomography based on background noise has been widely used to solve crustal structure problems at different scales (from a few kilometers to tens of meters). Among them, surface wave dispersion inversion based on background noise is an effective passive source seismic modeling method. This method is based on the dispersion phenomenon that the phase velocity of surface waves propagating in the medium changes with frequency. The surface wave phase velocity spectrum at different frequencies is calculated, and it can be calculated based on the layered medium theory to obtain the velocity structure of the underground medium, that is, to obtain the elastic parameters of the underground medium based on the shear wave velocity. This method does not require the setting of active seismic sources, which can reduce exploration costs, and can obtain underground structural morphology with higher resolution by observing noise signals for a long time.

[0003] However, the existing surface wave dispersion inversion method based on background noise has its limitations: 1. The inversion result is greatly affected by the quality of the background noise, and the surface wave signal obtained by the background noise cross-correlation calculation has a low signal-to-noise ratio; 2. This method is based on the one-dimensional layered medium assumption, and the inversion result reflects the elastic parameters averaged between specific receiving detectors. For example, in areas with strong lateral changes in the formation, the inversion error is large. 3. For surface wave imaging, point-to-point inversion is usually used, that is, the surface wave dispersion of each grid point is inverted independently, but this assumption makes it impossible to accurately invert fine lateral velocity changes. Summary of the invention

[0004] An object of an embodiment of the present invention is to provide a full-waveform inversion method based on surface wave dispersion inversion drive, which can accurately invert fine lateral velocity changes.

[0005] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a full waveform inversion method based on surface wave dispersion inversion drive, the full waveform inversion method comprising: according to the three-station interferometry method, obtaining Rayleigh surface wave observation signals through preset detection points; according to the one-dimensional surface wave dispersion inversion, obtaining the corresponding one-dimensional shear wave velocity of the central area of ​​the detection point pair through the obtained Rayleigh surface wave observation signals; based on the one-dimensional shear wave velocity body, constructing a corresponding three-dimensional velocity model; and performing full waveform inversion on the three-dimensional velocity model to obtain a full waveform inversion result of the area.

[0006] Optionally, the preset detection points are arranged according to an arrangement of a one-dimensional array, and the one-dimensional surface wave dispersion inversion is performed to obtain a corresponding one-dimensional shear wave velocity through the acquired Rayleigh surface wave observation signal, including: sequentially extracting a dispersion relationship curve of the phase velocity of the Rayleigh surface wave observation signal obtained between every two detection points varying with frequency; and performing one-dimensional surface wave dispersion inversion on the obtained dispersion relationship curve to obtain the one-dimensional shear wave velocity.

[0007] Optionally, constructing a corresponding three-dimensional velocity model based on the one-dimensional shear wave velocity includes: constructing a three-dimensional shear wave velocity body according to the one-dimensional shear wave velocity; constructing a three-dimensional longitudinal wave velocity body according to a preset longitudinal-to-straight wave velocity ratio and the three-dimensional shear wave velocity body; and constructing the three-dimensional velocity model through the three-dimensional shear wave velocity body and the three-dimensional longitudinal wave velocity body.

[0008] Optionally, constructing a three-dimensional shear wave velocity body based on the one-dimensional shear wave velocity includes: selecting dispersion relationship curves of multiple detection points according to a preset distance, forming a two-dimensional shear wave velocity surface through one-dimensional velocity combination and interpolation; and based on the formed two-dimensional shear wave velocity surface, replicating and extending it in an observation direction perpendicular to the two-dimensional shear wave velocity surface to construct the three-dimensional shear wave velocity body.

[0009] Optionally, performing full waveform inversion on the three-dimensional velocity model to obtain a full waveform inversion result of the area includes: constructing a virtual seismic source through the preset detection points; performing forward modeling of elastic wave fields through the virtual seismic source, the detection points and the three-dimensional velocity model to obtain a Rayleigh surface wave prediction signal received at the preset detection points; calculating a waveform residual between the Rayleigh surface wave prediction signal and the Rayleigh surface wave observation signal, performing back propagation on the waveform residual based on an elastic wave equation, and constructing an update gradient of elastic parameters of the three-dimensional velocity model according to the forward propagation wave field and the back propagation wave field; and iteratively updating the three-dimensional velocity model according to the update gradient until the waveform residual reaches a set threshold or the number of iterative updates reaches a preset value, stopping the iterative update to determine the elastic parameters of the three-dimensional velocity model, and using the three-dimensional velocity model with the elastic parameters as the result of full waveform inversion.

[0010] Optionally, constructing a virtual seismic source through the preset detection points includes: setting the type of the virtual seismic source to a directional force source in a direction perpendicular to the ground, and selecting a Delta function as a seismic source wavelet.

[0011] Optionally, before calculating the waveform residual between the Rayleigh surface roll prediction signal and the Rayleigh surface roll observation signal, the full waveform inversion method further includes: filtering the Rayleigh surface roll prediction signal and the Rayleigh surface roll observation signal within an effective frequency band of the observation signal surface roll.

[0012] Optionally, before iteratively updating the three-dimensional velocity model according to the update gradient, the full waveform inversion method further includes: constructing a calculation formula of the objective function f of full waveform inversion relative to the update gradient by the following formula:

[0013]

[0014] Among them, Vp is the three-dimensional longitudinal wave velocity body, Vs is the three-dimensional shear wave velocity body, represents the Laplace operator, and They represent the gradient of the objective function to the P-wave velocity and S-wave velocity, ρ is the underground medium density, τ xx ,τ yy ,τ zz represents the three normal stress components at the detection point, τ xy ,τ xz ,τ yz It represents the three shear stress components at the detection point, T is the duration of the observation record and the prediction record, the top “→” of the symbol represents the wave field component of forward propagation, and “←” represents the wave field component of reverse propagation.

[0015] Optionally, before iteratively updating the three-dimensional velocity model according to the update gradient, the full waveform inversion method further includes: smoothing the update gradient.

[0016] Optionally, after iteratively updating the three-dimensional velocity model according to the update gradient, the full waveform inversion method further comprises: suppressing the acquisition footprint of the strong energy at the virtual source and the detection point, and reducing the update gradient at the acquisition footprint.

[0017] An embodiment of the present invention also provides a control device for full waveform inversion, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned full waveform inversion method based on surface wave dispersion inversion drive.

[0018] An embodiment of the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions enable a machine to execute the above-mentioned full waveform inversion method based on surface wave dispersion inversion drive.

[0019] Through the above technical scheme, the embodiment of the present invention obtains enhanced and high signal-to-noise ratio Rayleigh surface wave observation signals for passive source seismic signals based on the three-station interferometry method; according to the one-dimensional surface wave dispersion inversion, the corresponding one-dimensional shear wave velocity of the central area of ​​the detection point pair is obtained through the obtained Rayleigh surface wave observation signal; based on the one-dimensional shear wave velocity body, a corresponding three-dimensional velocity model is constructed; the three-dimensional velocity model is subjected to full waveform inversion to obtain the full waveform inversion result of the area. The inversion result is not affected by the quality of background noise and has higher accuracy. Furthermore, the three-dimensional velocity model of the embodiment of the present invention is extracted from the one-dimensional surface wave dispersion inversion. Therefore, the Rayleigh surface wave prediction signal formed by the model can better fit the observed surface wave phase information. The waveform phase difference between the observed surface wave and the predicted surface wave in the low-frequency band involved in the surface wave signal is less than half a cycle, so the problem of cycle jump will not be caused.

[0020] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0022] Figure 1 It is a schematic flow chart of a full waveform inversion method based on surface wave dispersion inversion drive provided by an embodiment of the present invention;

[0023] Figure 2 is a schematic diagram of the linear array layout in the example area;

[0024] Figure 3 is a flow chart illustrating two inversion stages. DETAILED DESCRIPTION

[0025] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0026] Figure 1 is a flow chart of a full waveform inversion method based on surface wave dispersion inversion drive provided by an embodiment of the present invention, please refer to Figure 1 , the full waveform inversion method may include the following steps:

[0027] Step S110: According to the three-station interferometry method, Rayleigh wave observation signals are obtained through preset detection points.

[0028] The embodiment of the present invention is based on the three-station interferometry method for passive source seismic signals, and obtains the de-noised and enhanced Rayleigh surface wave (Rayleigh) observation signal through the preset detection points. The preset detection points of the embodiment of the present invention are preferably arranged according to the arrangement mode of the one-dimensional array, please refer to Figure 2 Example, a map of an array of detector points (or stations) arranged along the survey line of the study area (shown as a row of inverted triangles). Rayleigh wave observation signals are obtained through the stations shown in Figures R01-R20 and used for subsequent inversion calculations. Other scattered stations do not participate in subsequent inversion calculations. The three-station interferometry method is used to calculate the Rayleigh wave signal, which can provide a higher signal-to-noise ratio signal compared to the conventional two-station method.

[0029] Among them, the three-station interferometry method (or three-station interferometry) can be simply understood as: consider the records of three stations (detection points) observed at one time, of which two stations (detection points) are regarded as receiving stations, and the third station (detection point) is regarded as a virtual source (station). The cross-correlation between the virtual source (station) and the records of each receiving station is calculated to obtain the Green's function (Rayleigh surface wave signal) between the two receiving stations. Please refer to Figure 2 For example, a station other than R01-R20 is set as a virtual source (station), and the Rayleigh wave signal between R01-R20 is obtained by three-station interferometry. For example, the signal received by a station other than the detection points R01 and R20 is cross-correlated with the signal received by the R01 station and the signal received by the R20 station, and the results of the two cross-correlations are cross-correlated again to obtain the Green's function (Rayleigh wave signal) between the R01 station and the R20 station.

[0030] Step S120: According to the one-dimensional surface wave dispersion inversion, the corresponding one-dimensional shear wave velocity representing the central area of ​​the detection point pair is obtained through the acquired Rayleigh surface wave observation signal.

[0031] Preferably, step S120 may include: sequentially extracting dispersion relation curves of phase velocity versus frequency of the Rayleigh surface wave observation signal obtained between every two detection points; and performing one-dimensional surface wave dispersion inversion on the obtained dispersion relation curve to obtain the one-dimensional shear wave velocity.

[0032] Taking an example to illustrate, the dispersion relation curve of the phase velocity of the Rayleigh surface wave observation signal obtained between each two detection points varying with frequency (or period) is extracted in turn, and the obtained dispersion relation curve is subjected to one-dimensional surface wave dispersion inversion to obtain the one-dimensional shear wave velocity. The inversion process can be based on, for example, the CPS open source program, by inverting the dispersion relation curve of each detection point to obtain the one-dimensional shear wave velocity between each pair of detection points.

[0033] Among them, the quality control of the one-dimensional surface wave dispersion inversion process can compare the consistency between the predicted dispersion relationship curve obtained by the one-dimensional dispersion inversion at the center position of different detection point pairs and the observed dispersion relationship curve. However, the inversion based on surface wave dispersion information has natural limitations, that is, it is assumed that the underground medium is layered between each detection point pair. This assumption is obviously not accurate enough in areas where the lateral velocity changes dramatically. Therefore, the embodiment of the present invention preferably performs a three-dimensional full waveform inversion on the study area to improve the accuracy of the one-dimensional shear wave velocity (model).

[0034] Step S130: constructing a corresponding three-dimensional velocity model based on the one-dimensional shear wave velocity body.

[0035] Preferably, step S130 may include: constructing a three-dimensional shear wave velocity body according to the one-dimensional shear wave velocity; constructing a three-dimensional longitudinal wave velocity body according to a preset longitudinal-to-straight wave velocity ratio and the three-dimensional shear wave velocity body; and constructing the three-dimensional velocity model through the three-dimensional shear wave velocity body and the three-dimensional longitudinal wave velocity body.

[0036] Preferably, constructing a three-dimensional shear wave velocity body based on the one-dimensional shear wave velocity includes: selecting dispersion relationship curves of multiple detection points according to a preset distance, forming a two-dimensional shear wave velocity surface through one-dimensional velocity combination and interpolation; and based on the formed two-dimensional shear wave velocity surface, replicating and extending it in an observation direction perpendicular to the two-dimensional shear wave velocity surface to construct the three-dimensional shear wave velocity body.

[0037] Taking an example, a copy and expansion is performed in the observation direction perpendicular to the two-dimensional shear wave velocity surface to construct a (pseudo) three-dimensional shear wave velocity body. Then, according to a preset longitudinal and shear wave velocity ratio (for example, the longitudinal and shear wave velocity ratio is ) and the above-mentioned (pseudo) three-dimensional shear wave velocity body, construct a (pseudo) three-dimensional longitudinal wave velocity body, and further, the (pseudo) three-dimensional shear wave velocity body and the (pseudo) three-dimensional longitudinal wave velocity body constitute a three-dimensional velocity model.

[0038] Step S140: performing full waveform inversion on the three-dimensional velocity model to obtain a full waveform inversion result for the area.

[0039] Preferably, step S140 may include the following steps S141-S144.

[0040] Step S141, constructing a virtual seismic source through the preset detection points.

[0041] The embodiment of the present invention is aimed at passive source seismic signals, so it is necessary to construct a virtual seismic source. Figure 2As described in step S110, for example, some of the detection points R01-R20 are used as virtual seismic sources. For example, the detection point R01 is used as a virtual seismic source, which transmits a (virtual) seismic signal, and the detection points R01-R20 receive and obtain the corresponding Rayleigh surface wave observation signal. Similarly, the detection point R02 is used as a virtual seismic source, which transmits a (virtual) seismic signal, and the detection points R02-R20 receive and obtain the corresponding Rayleigh surface wave observation signal.

[0042] Preferably, the constructing of a virtual seismic source through the preset detection points includes: setting the type of the virtual seismic source to a directional force source in a direction perpendicular to the ground, and selecting a Delta function as a seismic source wavelet.

[0043] Since the amplitude energy of the surface wave signal generated in the seismic signal excited by the directional force source perpendicular to the ground is significantly stronger than that of the longitudinal wave and shear wave signals, the predicted waveform can be directly matched with the observed surface wave signal.

[0044] Preferably, in the inversion process, in order to overcome the near-field effect, the minimum offset distance (which can be understood as the distance between the virtual source and the preset detection point) range of the detection points participating in the inversion can be set. For example, Rayleigh surface wave observation signals and Rayleigh surface wave prediction signals with an offset distance less than 0.3 km are not fitted.

[0045] Step S142, performing elastic wave field forward simulation through the virtual seismic source, the detection point and the three-dimensional velocity model to obtain a Rayleigh surface wave prediction signal received at the preset detection point.

[0046] By performing elastic wave field forward modeling on the three-dimensional velocity model obtained in step S130, a Rayleigh surface wave prediction signal received at the corresponding detection point can be obtained.

[0047] Step S143, calculating the waveform residual between the Rayleigh wave prediction signal and the Rayleigh wave observation signal, performing back propagation based on the elastic wave equation on the waveform residual, and constructing the update gradient of the elastic parameters of the three-dimensional velocity model according to the forward propagation wave field and the backward propagation wave field.

[0048] Preferably, before calculating the waveform residual between the Rayleigh surface roll prediction signal and the Rayleigh surface roll observation signal, the full waveform inversion method further comprises: filtering the Rayleigh surface roll prediction signal and the Rayleigh surface roll observation signal within an effective frequency band of the observation signal surface roll.

[0049] For example, all resulting waveforms are bandpass filtered between 0.5 Hz and 1.4 Hz.

[0050] Preferably, the calculation formula of the objective function f of full waveform inversion relative to the update gradient is constructed by the following formula:

[0051]

[0052] Among them, Vp is the three-dimensional longitudinal wave velocity body, Vs is the three-dimensional shear wave velocity body, represents the Laplace operator, and They represent the gradient of the objective function to the P-wave velocity and S-wave velocity, ρ is the underground medium density, τ xx ,τ yy ,τ zz represents the three normal stress components at the detection point, τ xy ,τ xz ,τ yz It represents the three shear stress components at the detection point, T is the duration of the observation record and the prediction record, the top “→” of the symbol represents the wave field component of forward propagation, and “←” represents the wave field component of reverse propagation.

[0053] Among them, the normal stress and the derivative with respect to time can be expressed by the spatial derivative of the particle vibration velocity, which can satisfy the following formula:

[0054]

[0055] Step S144, iteratively updating the three-dimensional velocity model according to the update gradient until the waveform residual reaches a set threshold or the number of iterative updates reaches a preset value, stopping the iterative update to determine the elastic parameters of the three-dimensional velocity model, and using the three-dimensional velocity model with the elastic parameters as the result of full waveform inversion.

[0056] Preferably, after iteratively updating the three-dimensional velocity model according to the update gradient, the full waveform inversion method further comprises: suppressing the acquisition footprint of the strong energy at the virtual source and the detection point, and reducing the update gradient at the acquisition footprint.

[0057] The updated gradient can be obtained by equation (1), that is, the P-wave velocity gradient ( and ).

[0058] Preferably, the full waveform inversion method further comprises: smoothing the update gradient. The three-dimensional velocity model is iteratively updated by the smoothed update gradient until the waveform residual reaches a set threshold or the number of iterative updates reaches a preset value, the iterative update is stopped to determine the elastic parameters of the three-dimensional velocity model, and the three-dimensional velocity model with the elastic parameters is used as the result of the full waveform inversion.

[0059] Please refer to Figure 3As can be seen from the above, the velocity inversion of the embodiment of the present invention can be divided into two stages: the first inversion stage, based on the one-dimensional surface wave dispersion inversion, a one-dimensional shear wave velocity (model) is obtained; the second inversion stage, based on the one-dimensional shear wave velocity body, a corresponding three-dimensional velocity model is constructed, and the three-dimensional velocity model is subjected to full waveform inversion to obtain the full waveform inversion result of the area. The embodiment of the present invention is verified by selecting a preset area: the one-dimensional shear wave velocity obtained in the first inversion stage and the three-dimensional velocity model obtained in the second inversion stage, and the corresponding Rayleigh surface wave waveforms are compared. In the second inversion stage, after the full waveform inversion of the three-dimensional velocity model, if the degree of fit between the predicted Rayleigh surface wave and the observed Rayleigh surface wave waveform becomes better (higher waveform consistency), this reflects that the update of the three-dimensional velocity model is effective. At the same time, the reduction of the waveform residual indicates that the entire inversion process converges, and the obtained full waveform inversion result is more accurate.

[0060] Accordingly, the embodiment of the present invention obtains enhanced and high signal-to-noise ratio Rayleigh surface wave observation signals based on the three-station interferometry method for passive source seismic signals; obtains the corresponding one-dimensional shear wave velocity of the central area of ​​the detection point pair through the obtained Rayleigh surface wave observation signals according to the one-dimensional surface wave dispersion inversion; constructs the corresponding three-dimensional velocity model based on the one-dimensional shear wave velocity body; performs full waveform inversion on the three-dimensional velocity model to obtain the full waveform inversion result of the area. The inversion result is not affected by the quality of background noise and has higher accuracy. Furthermore, the three-dimensional velocity model of the embodiment of the present invention is extracted from the one-dimensional surface wave dispersion inversion. Therefore, the Rayleigh surface wave prediction signal formed by the forward simulation of the model can better fit the observed surface wave phase information. The waveform phase difference between the observed surface wave and the predicted surface wave in the low-frequency band involved in the surface wave signal is less than half a cycle, so it will not cause the problem of cycle jump.

[0061] An embodiment of the present invention also provides a control device for full waveform inversion, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the full waveform inversion method based on surface wave dispersion inversion drive.

[0062] An embodiment of the present invention further provides a machine-readable storage medium, on which instructions are stored, and the instructions enable a machine to execute the above-mentioned full waveform inversion method based on surface wave dispersion inversion drive.

[0063] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0064] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0067] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0068] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0069] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0070] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0071] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A full waveform inversion method based on surface wave dispersion inversion drive, characterized in that: The full waveform inversion method comprises: According to the three-station interferometry method, the Rayleigh wave observation signal is obtained through the preset detection points; According to the one-dimensional surface wave dispersion inversion, the corresponding one-dimensional shear wave velocity representing the central area of ​​the detection point pair is obtained through the acquired Rayleigh surface wave observation signal; Based on the one-dimensional shear wave velocity body, construct a corresponding three-dimensional velocity model; and Full waveform inversion is performed on the three-dimensional velocity model to obtain a full waveform inversion result of the area.

2. The full waveform inversion method based on surface wave dispersion inversion drive according to claim 1 is characterized in that: The preset detection points are arranged according to the arrangement of the one-dimensional array, and the one-dimensional surface wave dispersion inversion is performed to obtain the corresponding one-dimensional shear wave velocity through the obtained Rayleigh surface wave observation signal, including: Sequentially extracting the dispersion relation curve of the phase velocity of the Rayleigh surface wave observation signal obtained between each two detection points versus frequency; and The obtained dispersion relation curve is subjected to one-dimensional surface wave dispersion inversion to obtain the one-dimensional shear wave velocity.

3. The full waveform inversion method based on surface wave dispersion inversion drive according to claim 2 is characterized in that: The step of constructing a corresponding three-dimensional velocity model based on the one-dimensional shear wave velocity comprises: constructing a three-dimensional shear wave velocity body according to the one-dimensional shear wave velocity; Constructing a three-dimensional longitudinal wave velocity body according to a preset longitudinal and transverse wave velocity ratio and the three-dimensional transverse wave velocity body; and The three-dimensional velocity model is constructed by using the three-dimensional shear wave velocity body and the three-dimensional longitudinal wave velocity body.

4. The full waveform inversion method based on surface wave dispersion inversion drive according to claim 3 is characterized in that: The step of constructing a three-dimensional shear wave velocity body according to the one-dimensional shear wave velocity comprises: According to the preset distance, the dispersion relation curves of multiple detection points are selected, and a two-dimensional shear wave velocity surface is formed by one-dimensional velocity combination and interpolation; and Based on the formed two-dimensional shear wave velocity surface, replication and expansion are performed in an observation direction perpendicular to the two-dimensional shear wave velocity surface to construct the three-dimensional shear wave velocity body.

5. The full waveform inversion method based on surface wave dispersion inversion drive according to claim 1 is characterized in that: The performing full waveform inversion on the three-dimensional velocity model to obtain a full waveform inversion result of the region includes: Constructing a virtual seismic source through the preset detection points; Perform elastic wave field forward simulation through the virtual seismic source, the detection point and the three-dimensional velocity model to obtain a Rayleigh surface wave prediction signal received at the preset detection point; Calculating the waveform residual between the Rayleigh surface wave prediction signal and the Rayleigh surface wave observation signal, performing back propagation on the waveform residual based on the elastic wave equation, and constructing an update gradient of the elastic parameters of the three-dimensional velocity model according to the forward propagation wave field and the reverse propagation wave field; and The three-dimensional velocity model is iteratively updated according to the update gradient until the waveform residual reaches a set threshold or the number of iterative updates reaches a preset value, and the iterative update is stopped to determine the elastic parameters of the three-dimensional velocity model, and the three-dimensional velocity model with the elastic parameters is used as the result of full waveform inversion.

6. The full waveform inversion method based on surface wave dispersion inversion drive according to claim 5 is characterized in that: The step of constructing a virtual seismic source through the preset detection points includes: The type of the virtual source is set to a directional force source in a direction perpendicular to the ground, The Delta function is selected as the source wavelet.

7. The full waveform inversion method based on surface wave dispersion inversion drive according to claim 5 is characterized in that: Before calculating the waveform residual between the Rayleigh wave prediction signal and the Rayleigh wave observation signal, the full waveform inversion method further includes: The Rayleigh wave prediction signal and the Rayleigh wave observation signal are filtered within an effective frequency band of the observation signal wave.

8. The full waveform inversion method based on surface wave dispersion inversion drive according to claim 5 is characterized in that: Before iteratively updating the three-dimensional velocity model according to the update gradient, the full waveform inversion method further includes: The calculation formula of the objective function f of full waveform inversion relative to the update gradient is constructed by the following formula: Among them, Vp is the three-dimensional longitudinal wave velocity body, Vs is the three-dimensional shear wave velocity body, represents the Laplace operator, and They represent the gradient of the objective function to the P-wave velocity and S-wave velocity, ρ is the underground medium density, τ xx ,τ yy ,τ zz represents the three normal stress components at the detection point, τ xy ,τ xz ,τ yz represents the three shear stress components at the detection point, T is the duration of the observation record and the prediction record, the top of the symbol "→" represents the wave field component of forward propagation, and "←" represents the wave field component of reverse propagation.

9. The full waveform inversion method based on surface wave dispersion inversion drive according to claim 5 is characterized in that: Before iteratively updating the three-dimensional velocity model according to the update gradient, the full waveform inversion method further includes: The updated gradient is smoothed.

10. The full waveform inversion method based on surface wave dispersion inversion drive according to claim 5, characterized in that: After iteratively updating the three-dimensional velocity model according to the update gradient, the full waveform inversion method further includes: The strong energy at the virtual source and the detection point is subjected to acquisition footprint suppression, and the update gradient is reduced at the acquisition footprint.

11. A control device for full waveform inversion, characterized in that: The control device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the full waveform inversion method based on surface wave dispersion inversion drive according to any one of claims 1 to 10.

12. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores instructions, which enable the machine to execute the full waveform inversion method based on surface wave dispersion inversion drive according to any one of claims 1-10.

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