Transverse wave velocity inversion method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a shear wave velocity inversion method and device, electronic equipment and a storage medium, and relates to the technical field of seismic wave inversion. The method comprises the following steps: acquiring a plurality of frequency dispersion energy diagrams from different seismological observation systems in the same observation area, preprocessing the plurality of frequency dispersion energy diagrams, and constructing an image matrix; constructing a target kernel matrix by using the image matrix and a sub-sample set obtained from the image matrix; performing eigenvalue decomposition on the target kernel matrix, and performing image reconstruction according to the obtained maximum eigenvalue of the target kernel matrix to obtain a fusion frequency dispersion energy diagram with higher resolution and higher quality; and performing shear wave velocity inversion by using the fused frequency dispersion energy diagram to obtain a shear wave velocity profile of the observation area, thereby remarkably improving the accuracy and reliability of the shear wave velocity inversion.

Description

Technical Field

[0001] This invention relates to the field of seismic wave inversion technology, and more specifically, to a method, apparatus, electronic device, and storage medium for shear wave velocity inversion. Background Technology

[0002] Shear wave velocity reflects changes in formation stiffness and helps distinguish different geological structures, thus providing important data for earthquake risk assessment, seismic design, resource exploration, and geological hazard monitoring. However, due to the limitations of field observation data relative to the complexity of underground geological structures, the inversion problem is usually ill-conditioned and non-unique, making accurate estimation of shear wave velocity a challenge.

[0003] The key to inversion lies in the accurate extraction of dispersion curves, which contain rich information and are the foundation for achieving high-precision shear wave velocity inversion. The quality of the dispersion energy map directly affects the richness and continuity of the extractable dispersion curves.

[0004] Dispersion energy maps from a single observation system typically suffer from narrow bandwidth coverage and low modal resolution. This limitation significantly affects the quality of dispersion curve extraction, thus restricting the accuracy of inversion results. Although some studies have attempted to expand frequency coverage by manually integrating dispersion energy maps from different observation systems, this method is inefficient and highly subjective, making it difficult to meet the needs of large-scale applications. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the present invention aims to provide a transverse wave velocity inversion method, apparatus, electronic device and storage medium.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows: In a first aspect, the present invention provides a method for transverse wave velocity inversion, the method comprising: Multiple dispersion energy maps from different seismic observation systems for the same observation area are obtained, and the multiple dispersion energy maps are preprocessed to construct an image matrix; A target kernel matrix is ​​constructed using the image matrix and a subset of samples obtained from the image matrix; The target kernel matrix is ​​subjected to eigenvalue decomposition, and the image is reconstructed based on the largest eigenvalue of the target kernel matrix to obtain a fused dispersion energy map; The transverse wave velocity profile of the observed region is obtained by inverting the transverse wave velocity using the fused dispersion energy map.

[0007] Optionally, the step of preprocessing multiple dispersion energy maps to construct an image matrix includes: Each of the dispersion energy maps is normalized, and each normalized dispersion energy map is then vectorized into a column vector to obtain multiple image column vectors. The multiple image column vectors are stacked into an input matrix, and the input matrix is ​​then decentered to obtain the image matrix.

[0008] Optionally, the step of constructing the target kernel matrix using the image matrix and a subset of samples obtained from the image matrix includes: Calculate the kernel similarity matrix between the image matrix and the subset, and use the kernel similarity matrix as the first kernel matrix; Using the aforementioned subsample set, construct a second kernel matrix; The target kernel matrix is ​​obtained based on the first kernel matrix and the second kernel matrix.

[0009] Optionally, the step of obtaining the target kernel matrix based on the first kernel matrix and the second kernel matrix includes: Using the first kernel matrix and the second kernel matrix, a low-rank approximation matrix of the target kernel matrix is ​​constructed; The low-rank approximation matrix is ​​used as the target kernel matrix.

[0010] Optionally, the step of reconstructing the image based on the largest eigenvalue of the obtained target kernel matrix to obtain the fused dispersion energy map includes: The first principal component eigenvector corresponding to the largest eigenvalue of the target kernel matrix is ​​projected onto the kernel space to obtain the projected features. The image is reconstructed using the projection features to obtain the fused dispersion energy map.

[0011] Optionally, the step of using the fused dispersion energy map to perform shear wave velocity inversion to obtain the shear wave velocity profile of the observed area includes: Extract multimodal dispersion curves from the fused dispersion energy map; The shear wave velocity profile of the observed area is obtained by inverting the shear wave velocity using the multimodal dispersion curve.

[0012] In a second aspect, the present invention provides a transverse wave velocity inversion device, the device comprising: The acquisition module is used to acquire multiple dispersion energy maps from different seismic observation systems for the same observation area; The processing module is used to preprocess multiple dispersion energy maps to construct an image matrix; construct a target kernel matrix using the image matrix and a subset of samples obtained from the image matrix; perform eigenvalue decomposition on the target kernel matrix; and reconstruct the image based on the maximum eigenvalue of the obtained target kernel matrix to obtain a fused dispersion energy map. The inversion module is used to perform shear wave velocity inversion using the fused dispersion energy map to obtain the shear wave velocity profile of the observed area.

[0013] Optionally, the processing module is specifically used to normalize each of the dispersion energy maps, and to vectorize each of the normalized dispersion energy maps into column vectors to obtain multiple image column vectors; to stack the multiple image column vectors into an input matrix, and to decenter the input matrix to obtain the image matrix.

[0014] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing machine-executable instructions executable by the processor, the processor executing the machine-executable instructions to implement the transverse wave velocity inversion method described in the first aspect above.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the transverse wave velocity inversion method as described in the first aspect above.

[0016] The shear wave velocity inversion method, apparatus, electronic device, and storage medium provided in this invention involve acquiring multiple dispersion energy maps from different seismic observation systems for the same observation area, preprocessing these multiple dispersion energy maps to construct an image matrix, constructing a target kernel matrix using the image matrix and a subset of samples obtained from the image matrix, performing eigenvalue decomposition on the target kernel matrix, and reconstructing the image based on the maximum eigenvalue of the obtained target kernel matrix to obtain a fused dispersion energy map, and using the fused dispersion energy map to perform shear wave velocity inversion to obtain the shear wave velocity profile of the observation area. In other words, by fusing dispersion energy maps from multiple source seismic observation systems and combining kernel matrix analysis and eigenvalue decomposition techniques, a higher resolution and higher quality fused dispersion energy map is achieved, thereby significantly improving the accuracy and reliability of shear wave velocity inversion.

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1This figure shows a schematic block diagram of an electronic device provided by an embodiment of the present invention; Figure 2 A schematic flowchart of a shear wave velocity inversion method provided by an embodiment of the present invention is shown; Figure 3 This invention provides a simulated noisy Rayleigh waveform and its corresponding dispersion energy map when the source depth is 0m. Figure 4 The present invention illustrates a simulated noisy Rayleigh waveform and its corresponding dispersion energy map when the source depth is 1.5m, according to an embodiment of the present invention. Figure 5 This invention provides a simulated noisy Rayleigh waveform and its corresponding dispersion energy map for a source depth of 2m, according to an embodiment of the present invention. Figure 6 The diagram shows a fused dispersion energy map obtained from simulated data using the method provided in this embodiment of the invention and a fused dispersion energy map obtained using a conventional method. Figure 7 The image shows a superimposed visualization of the fused dispersion energy map and the theoretical dispersion curve obtained by the method provided in the embodiments of the present invention. Figure 8 This demonstrates how existing methods can be used to obtain inversion results from simulated data; Figure 9 This illustrates obtaining inversion results from simulated data using the method provided in this embodiment of the invention; Figure 10 Four schematic diagrams of observation configurations with different minimum source distances and trace spacings are shown; Figure 11 The measured seismic waveforms and their corresponding dispersion energy maps obtained using observation configuration 1 are shown. Figure 12 The measured seismic waveforms and their corresponding dispersion energy maps obtained using observation configuration 2 are shown. Figure 13 The measured seismic waveforms and their corresponding dispersion energy maps obtained using observation configuration 3 are shown. Figure 14 The measured seismic waveforms and their corresponding dispersion energy maps obtained using observation configuration 4 are shown. Figure 15 The diagram shows a fusion dispersion energy map obtained using the method provided in this embodiment of the invention on measured data and a fusion dispersion energy map obtained using a conventional method. Figure 16 This demonstrates how existing methods can be used to obtain inversion results from measured data; Figure 17 This demonstrates how the method provided in the embodiments of the present invention is used to obtain inversion results from measured data; Figure 18 A functional block diagram of a transverse wave velocity inversion device provided in an embodiment of the present invention is shown.

[0020] Icons: 100 - Electronic device; 110 - Memory; 120 - Processor; 130 - Communication module; 200 - Shear wave velocity inversion device; 201 - Acquisition module; 202 - Processing module; 203 - Inversion module. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0023] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0024] Please refer to Figure 1 This is a block diagram of an electronic device 100. The electronic device 100 includes a memory 110, a processor 120, and a communication module 130. The memory 110, processor 120, and communication module 130 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0025] The memory 110 is used to store programs or data. The memory 110 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0026] The processor 120 is used to read / write data or programs stored in the memory 110 and to perform corresponding functions.

[0027] The communication module 130 is used to establish a communication connection between the electronic device 100 and other communication terminals through the network, and to send and receive data through the network.

[0028] It should be understood that, Figure 1 The structure shown is only a schematic diagram of the electronic device 100. The electronic device 100 may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented using hardware, software, or a combination thereof.

[0029] To overcome the shortcomings of the prior art, this invention provides a method for inverting shear wave velocity, which will be described in detail below.

[0030] Please refer to Figure 2 The transverse wave velocity inversion method includes steps S101 to S104.

[0031] S101: Obtain multiple dispersion energy maps from different seismic observation systems for the same observation area, and preprocess the multiple dispersion energy maps to construct an image matrix.

[0032] Different earthquake observation systems refer to differences in parameters such as minimum source-receiver spacing, source depth, and receiver array geometry.

[0033] Seismic data were collected under different observation conditions (such as changing the source depth and receiver spacing), and these data were transformed into visualized dispersion energy maps using methods such as frequency-Bessel transform, resulting in N dispersion energy maps.

[0034] The "preprocessing" includes normalization, vectorization, and decentralization. In a possible implementation, the process of "preprocessing multiple dispersion energy maps to construct an image matrix" can be as follows: First, each dispersion energy map is normalized, and each normalized dispersion energy map is then vectorized into multiple image column vectors. Then, the multiple image column vectors are stacked into an input matrix, and the input matrix is ​​decentered to obtain the image matrix.

[0035] In this embodiment of the invention, N The amplitude dispersion energy map is normalized and denoted as... Each dispersion energy map has a dimension of 1. .

[0036] Then, each normalized dispersion energy image Vectorize it into a column vector of length 𝐻𝑊 according to column precedence (column-major order). ,in, Indicates vectorization operation, Representing an image The vectorized form of .

[0037] These column vectors are then stacked to form an input matrix. .

[0038] According to the formula Decenter the input matrix, where, The input matrix after decentralization. It is a column mean vector. It is a column vector The mean, This represents a column vector consisting entirely of 1s.

[0039] The decentralized input matrix As an image matrix.

[0040] S102, construct the target kernel matrix using the image matrix and the subset of samples obtained from the image matrix.

[0041] Among them, from the image matrix Selected from M ( A subset of representative pixels constitutes a sample set. ,in, Each Corresponding to the image matrix A single pixel.

[0042] In a possible implementation, step S102 can be implemented as follows: First, calculate the kernel similarity matrix between the image matrix and the subsample set. Use the kernel similarity matrix as the first kernel matrix and construct the second kernel matrix using the subsample set. Then, based on the first and second kernel matrices, obtain the target kernel matrix.

[0043] Among them, the first kernel matrix The elements in the formula satisfy the following formula:

[0044] In the formula, express The i OK, yes The first in m One sample, This is the kernel function width parameter.

[0045] Second core matrix The elements in the formula satisfy the following formula:

[0046] In the formula, yes The first in j One sample, yes The first in k One sample, This is the kernel function width parameter.

[0047] Optionally, the process of “obtaining the target kernel matrix based on the first kernel matrix and the second kernel matrix” can be: using the first kernel matrix and the second kernel matrix to construct a low-rank approximation matrix of the target kernel matrix; and using the low-rank approximation matrix as the target kernel matrix.

[0048] In this embodiment of the invention, the low-rank approximation matrix of the target kernel matrix satisfies the following formula:

[0049] In the formula, It is a low-rank approximation matrix of the target kernel matrix. It is the first kernel matrix. It is the second core matrix.

[0050] The low-rank approximation matrix of the target kernel matrix As the target kernel matrix .

[0051] S103, perform eigenvalue decomposition on the target kernel matrix, and reconstruct the image based on the largest eigenvalue of the obtained target kernel matrix to obtain the fused dispersion energy map.

[0052] Among them, for the target kernel matrix Eigenvalue decomposition yields eigenvalues ​​that satisfy the following formula: In the formula, For the first k 1 eigenvalue, Eigenvalues The corresponding feature vector.

[0053] In a possible implementation, step S103 can be implemented by: projecting the first principal component eigenvector corresponding to the largest eigenvalue of the target kernel matrix into the kernel space to obtain the projected features; and using the projected features to reconstruct the image to obtain the fused dispersion energy map.

[0054] In this embodiment of the invention, the largest eigenvalue is selected. The corresponding first principal component feature vector Calculate its projection characteristics in kernel space. , ,Will The image is reconstructed into a two-dimensional image to obtain the fused dispersion energy map.

[0055] S104. The transverse wave velocity profile of the observation area is obtained by inverting the transverse wave velocity using the fused dispersion energy map.

[0056] In a possible implementation, step S104 can be implemented by: extracting multimode dispersion curves from the fused dispersion energy map; using the multimode dispersion curves to perform shear wave velocity inversion to obtain the shear wave velocity profile of the observation area.

[0057] The application effects of the shear wave velocity inversion method provided in the embodiments of the present invention will be illustrated below through the following examples.

[0058] Example 1: Numerical experiment corresponding to the method provided in the embodiment of the present invention.

[0059] Step 0: Prepare three surface wave dispersion energy maps.

[0060] Synthetic Rayleigh wave data were generated using the seismological computational program CPS330, based on Table 1. This program simulates the propagation process of seismic waves according to the input Earth model parameters. To simulate different acquisition scenarios, three different observation configurations were designed, using linear geophone arrays with varying source depths. Each array contained 64 receivers, with a minimum source offset of 10 meters and a trace spacing of 2 meters.

[0061] Table 1

[0062] Figure 3 , Figure 4 and Figure 5Simulated Rayleigh wave records at focal depths of 0 m, 1.5 m, and 2.0 m are presented. Based on these waveforms, a dispersion energy map is constructed using the frequency-Bessel transform method, as shown below. Figure 3 , Figure 4 and Figure 5 The three-panel wave dispersion energy diagrams are shown.

[0063] Step 1: Preprocess the three dispersion energy maps generated in Step 0.

[0064] The three dispersion energy maps are normalized and denoted as follows: Each dispersion energy map has a dimension of 1. First, each dispersion energy map... The vector is then vectorized into a column vector of length 𝐻𝑊 in column-major order (column-major order), where, Represents the dispersion energy diagram The vectorized form, This indicates a vectorization operation.

[0065] Then, these column vectors are stacked into an input matrix. This involves expanding the input matrix into a column vector in column order. According to the formula... For each dispersion energy map, decentralize it, where, The input matrix after decentralization. It is a column mean vector. It is a column vector The mean, This represents a column vector consisting entirely of 1s.

[0066] Step 2: Select 3000 feature samples for constructing the target kernel matrix.

[0067] From the decentralized input matrix A subset of 3000 representative pixels was constructed. , of which each Corresponding to the decentralized matrix A single pixel. and The kernel similarity between them constitutes the sub-kernel matrix Its elements are defined as follows:

[0068] in, express The i OK, yes The first in m One sample, kernel function width parameter .

[0069] Similarly, the kernel matrix formed between the subsets Defined as .

[0070] in, yes The first in j One sample, yes The first in k One sample, kernel function width parameter .

[0071] Step 3: Generate the fused dispersion energy map.

[0072] Based on the calculations in step 2 and The low-rank approximation of the target kernel matrix can be obtained by... We obtained, among which, It is a low-rank approximation of the target kernel matrix.

[0073] Will As the target kernel matrix ,right Eigenvalue decomposition yields eigenvalues ​​that satisfy the following formula: ,in, For the first k 1 eigenvalue, Eigenvalues The corresponding feature vector.

[0074] Select the largest eigenvalue The corresponding first principal component feature vector Calculate its projection feature representation in kernel space. , The image is then reconstructed into a two-dimensional image to obtain the fused dispersion energy map.

[0075] like Figure 6 As shown, comparing the fused dispersion energy map obtained by the method provided in this embodiment with the results of existing methods, it can be found that the fused dispersion energy map obtained by existing methods has lower resolution and signal-to-noise ratio than the fused dispersion energy map obtained by the method provided in this embodiment. In contrast, the fused dispersion energy map obtained by the method provided in this embodiment exhibits better energy concentration, thus providing a more reliable basis for the subsequent extraction of multimodal dispersion curves.

[0076] Figure 7The superposition results of the fused dispersion energy map obtained by the method provided in the embodiments of the present invention and the theoretical dispersion curve are shown. It can be clearly seen that the energy distribution in the fused dispersion energy map obtained by the method provided in the embodiments of the present invention is highly consistent with the theoretical dispersion curve, verifying the accuracy and effectiveness of the method provided in the embodiments of the present invention.

[0077] Figure 8 This demonstrates the inversion results obtained by inverting the fused dispersion energy map based on existing methods. Figure 9 The inversion results obtained by inverting the fused dispersion energy map based on the method provided in the embodiments of the present invention are shown. The comparison shows that the inversion results corresponding to the method provided in the embodiments of the present invention are more accurate than the inversion results obtained by existing methods.

[0078] Example 2: Field test experiment corresponding to the method provided in the embodiment of the present invention.

[0079] This example demonstrates active surface wave measurements at site A using four observation configurations. All four configurations include 48 detectors, such as... Figure 10 As shown, configuration 1 is an array with a minimum source distance of 3 meters and a channel spacing of 1 meter; configuration 2 is an array with a source distance of 15 meters and a channel spacing of 1 meter; configuration 3 is an array with a source distance of 3.5 meters and a channel spacing of 2 meters; and configuration 4 is an array with a source distance of 4 meters and a channel spacing of 2 meters.

[0080] Figure 11 , 12 Figures 13 and 14 show seismic waveforms recorded by four linear array observation configurations, respectively. Based on these waveforms, the corresponding dispersion energy maps were obtained using the frequency-Bassel transform method, as shown below. Figure 11 , 12 As shown in Figures 13 and 14, different energy maps exhibit significant differences in frequency concentration and modal clarity, reflecting the varying capabilities of different observation configurations in resolving subsurface structures.

[0081] Will Figure 11 , 12 The four energy maps in 13 and 14 are used as input images. The kernel function's width parameter σ and the number of representative pixels are also considered. M The fused dispersion energy maps obtained using the method provided in this embodiment of the invention, with empirical settings of 0.8 and 5000 respectively, are shown below. Figure 15 As shown.

[0082] It can be observed that the fused dispersive energy map exhibits three dispersive modes, demonstrating better continuity and resolution over a wider frequency range. Furthermore, background noise and fragmented energy distribution present in the original image are effectively suppressed, resulting in a significant improvement in the signal-to-noise ratio of the fused dispersive energy map. Compared to dispersive energy maps from a single observation configuration, the fused dispersive energy map exhibits a more coherent energy distribution and clearer modal features, verifying the effectiveness of the method provided in this embodiment of the invention in practical applications.

[0083] In contrast. Figure 15 The paper also presents a fused dispersion energy map obtained using existing methods. From a visual perspective, the fused dispersion energy map obtained by existing methods suffers from reduced resolution and poor noise suppression, further highlighting the advantages of the fused dispersion energy map obtained by existing methods in field data processing.

[0084] Figure 16 This demonstrates the inversion results obtained by inverting the fused dispersion energy map obtained from measured data using existing methods. Figure 17 The inversion results are shown, obtained by inverting the fused dispersion energy map obtained from measured data using the method provided in this embodiment of the invention. The results show that, although Figure 16 and Figure 17 The inversion dispersion curves can all fit the measured dispersion curves well, but the inversion results of the fused dispersion energy map obtained by the method provided in this embodiment of the invention can fit the geological conditions better. The fused dispersion energy map obtained by the method provided in this embodiment of the invention can obtain more accurate shear wave velocity inversion results.

[0085] Compared with the prior art, the embodiments of the present invention have the following technical effects: (1) Obtain the dispersion energy map from different seismic observation systems, and normalize, vectorize and decentralize the dispersion energy map to unify the scale differences between different observation systems, eliminate the influence of noise and retain the effective signal.

[0086] (2) The kernel function is used to calculate the similarity between the image matrix and the subsample set (first kernel matrix) and the relationship within the subsample set (second kernel matrix), capturing nonlinear structural information, enhancing the ability to express complex relationships between images, constructing a low-rank approximation matrix of the target kernel matrix, and retaining the principal component information that best represents the essential structure of the data.

[0087] (3) By performing eigenvalue decomposition on the target kernel matrix, extracting the first principal component eigenvector corresponding to the largest eigenvalue, and then projecting it onto the kernel space and reconstructing the image, the dominant energy distribution pattern in the image can be effectively preserved. The resulting fused dispersion energy map has higher resolution and modal clarity, making the extracted multimodal dispersion curves more complete and continuous, thereby improving the accuracy and stability of transverse wave velocity inversion.

[0088] To perform the corresponding steps in the above embodiments and various possible methods, an implementation of the shear wave velocity inversion device 200 is given below. Optionally, the shear wave velocity inversion device 200 can adopt the above-described... Figure 1 The device structure of the electronic device is shown. Further, please refer to... Figure 18 , Figure 18 This is a functional block diagram of a shear wave velocity inversion device 200 provided in an embodiment of the present invention. It should be noted that the basic principle and technical effects of the shear wave velocity inversion device 200 provided in this embodiment are the same as those in the above embodiments. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above embodiments. The shear wave velocity inversion device 200 includes: The acquisition module 201 is used to acquire multiple dispersion energy maps from different seismic observation systems for the same observation area.

[0089] The processing module 202 is used to preprocess multiple dispersion energy maps to construct an image matrix; construct a target kernel matrix using the image matrix and a subset of samples obtained from the image matrix; perform eigenvalue decomposition on the target kernel matrix; and reconstruct the image based on the maximum eigenvalue of the obtained target kernel matrix to obtain a fused dispersion energy map.

[0090] Inversion module 203 is used to perform shear wave velocity inversion using the fused dispersion energy map to obtain the shear wave velocity profile of the observation area.

[0091] Optionally, the processing module 202 is specifically used to normalize each dispersion energy map, and to vectorize each normalized dispersion energy map into multiple image column vectors; to stack the multiple image column vectors into an input matrix, and to decenter the input matrix to obtain the image matrix.

[0092] Optionally, the above modules can be stored in the form of software or firmware. Figure 1 The memory 110 shown is either stored in or embedded in the operating system (OS) of the electronic device 100, and can be used by... Figure 1 The processor 120 executes the program. Meanwhile, the data and program code required to execute the above modules can be stored in the memory 110.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0094] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0095] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0096] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for inverting shear wave velocity, characterized in that, The method includes: Multiple dispersion energy maps from different seismic observation systems for the same observation area are obtained, and the multiple dispersion energy maps are preprocessed to construct an image matrix; A target kernel matrix is ​​constructed using the image matrix and a subset of samples obtained from the image matrix; The target kernel matrix is ​​subjected to eigenvalue decomposition, and the image is reconstructed based on the largest eigenvalue of the target kernel matrix to obtain a fused dispersion energy map; The transverse wave velocity profile of the observed region is obtained by inverting the transverse wave velocity using the fused dispersion energy map.

2. The shear wave velocity inversion method as described in claim 1, characterized in that, The step of preprocessing multiple dispersion energy maps to construct an image matrix includes: Each of the dispersion energy maps is normalized, and each normalized dispersion energy map is then vectorized into a column vector to obtain multiple image column vectors. The multiple image column vectors are stacked into an input matrix, and the input matrix is ​​then decentered to obtain the image matrix.

3. The shear wave velocity inversion method as described in claim 1, characterized in that, The step of constructing the target kernel matrix using the image matrix and the subset of samples obtained from the image matrix includes: Calculate the kernel similarity matrix between the image matrix and the subset, and use the kernel similarity matrix as the first kernel matrix; Using the aforementioned subsample set, construct a second kernel matrix; The target kernel matrix is ​​obtained based on the first kernel matrix and the second kernel matrix.

4. The shear wave velocity inversion method as described in claim 3, characterized in that, The step of obtaining the target kernel matrix using the first kernel matrix and the second kernel matrix includes: Using the first kernel matrix and the second kernel matrix, a low-rank approximation matrix of the target kernel matrix is ​​constructed; The low-rank approximation matrix is ​​used as the target kernel matrix.

5. The shear wave velocity inversion method as described in claim 1, characterized in that, The steps for reconstructing the image based on the largest eigenvalue of the obtained target kernel matrix to obtain the fused dispersion energy map include: The first principal component eigenvector corresponding to the largest eigenvalue of the target kernel matrix is ​​projected onto the kernel space to obtain the projected features. The image is reconstructed using the projection features to obtain the fused dispersion energy map.

6. The shear wave velocity inversion method as described in claim 1, characterized in that, The step of using the fused dispersion energy map to perform shear wave velocity inversion to obtain the shear wave velocity profile of the observation area includes: Extract multimodal dispersion curves from the fused dispersion energy map; The shear wave velocity profile of the observed area is obtained by inverting the shear wave velocity using the multimodal dispersion curve.

7. A transverse wave velocity inversion device, characterized in that, The device includes: The acquisition module is used to acquire multiple dispersion energy maps from different seismic observation systems for the same observation area; The processing module is used to preprocess multiple dispersion energy maps to construct an image matrix; construct a target kernel matrix using the image matrix and a subset of samples obtained from the image matrix; perform eigenvalue decomposition on the target kernel matrix; and reconstruct the image based on the maximum eigenvalue of the obtained target kernel matrix to obtain a fused dispersion energy map. The inversion module is used to perform shear wave velocity inversion using the fused dispersion energy map to obtain the shear wave velocity profile of the observed area.

8. The transverse wave velocity inversion device as described in claim 7, characterized in that, The processing module is specifically used to normalize each of the dispersion energy maps, and to vectorize each of the normalized dispersion energy maps into column vectors to obtain multiple image column vectors; to stack the multiple image column vectors into an input matrix, and to decenter the input matrix to obtain the image matrix.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing machine-executable instructions that can be executed by the processor to implement the transverse wave velocity inversion method according to any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the transverse wave velocity inversion method as described in any one of claims 1-6.

Images