Near-surface crack detection method and device and computer program product

Abstract

The invention provides a near-surface crack detection method and apparatus, and a computer program product. The method comprises the steps of obtaining common offset seismic wave field data of a to-be-processed surface region; according to the common offset seismic wave field data, using Fourier transform to construct a frequency space domain seismic data matrix; the sizes of each row and each column of the frequency space domain seismic data matrix are calculated based on the L1 norm, and the row value of each row and the column value of each column are obtained respectively; extracting multiple rows and multiple columns of the frequency space domain seismic data matrix by adopting a random selection method based on the row values and the column values to obtain a target row and a target column; reconstructing the target row and the target column based on a matrix transformation algorithm to obtain an approximate matrix; and performing homing imaging on the approximate matrix by using a pre-stack depth migration algorithm to generate a surface crack distribution image. The method solves the problem that the nonlinear signal component of the crack cannot be effectively extracted and reconstructed in a complex geological environment in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of surface crack detection, and in particular to a near-surface crack detection method, a near-surface crack detection device, a computer-readable storage medium, and a computer program product. Background Art

[0002] In near-surface crack detection, accurate identification and location of cracks and their size are of great significance for engineering geological exploration, geological disaster warning, and underground resource development. Near-surface cracks are usually caused by geological activities, groundwater erosion, or human factors. The presence of cracks often changes the physical properties of underground media, thereby affecting the safety of surface buildings and the stability of geological structures. Therefore, accurate detection of near-surface cracks is crucial for geological exploration and safety assurance.

[0003] Existing detection methods mostly rely on complex seismic exploration technologies such as full waveform inversion and wave field extension. These methods usually require high-quality initial models and high computing power, and are easily interfered by noise, resulting in instability in detection results. Especially under complex geological conditions, signals are easily affected by reflection, refraction and scattering, which increases the difficulty of crack detection. In addition, traditional signal processing methods often rely on accurate estimation of local dip fields and show poor stability when processing signals in inhomogeneous media.

[0004] To overcome these challenges, researchers have proposed a variety of signal decomposition and reconstruction methods to improve the accuracy and stability of crack detection. However, most of these methods rely on data smoothing and noise suppression strategies, but it is still difficult to balance the signal resolution and computational efficiency. How to effectively extract and reconstruct the nonlinear signal components of cracks in complex geological environments is still a major challenge in the current field of geological exploration. Summary of the invention

[0005] The main purpose of the present application is to provide a near-surface crack detection method, a near-surface crack detection device, a computer-readable storage medium and a computer program product, so as to at least solve the problem in the prior art that the nonlinear signal components of cracks cannot be effectively extracted and reconstructed in a complex geological environment.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a near-surface crack detection method is provided, comprising: acquiring common-offset seismic wavefield data of a surface area to be processed; constructing a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix; calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining the row value of each row and the column value of each column respectively; extracting multiple rows and multiple columns of the frequency-space domain seismic data matrix using a random selection method based on the row value and the column value, and obtaining target rows and target columns; reconstructing the target rows and the target columns based on a matrix transformation algorithm, and obtaining an approximate matrix of nonlinear signals in the common-offset seismic wavefield data, wherein the approximate matrix represents nonlinear wavefield data information formed by surface cracks; performing home imaging on the approximate matrix using a prestack depth migration algorithm, and generating a surface crack distribution image, so as to perform crack exploration according to the surface crack distribution image.

[0007] Optionally, obtaining common-offset seismic wavefield data of a surface area to be processed includes: using an artificial seismic source to excite seismic waves on the surface of the surface area to be processed, the artificial seismic source at least including explosives, an air gun or a vibrator; controlling a plurality of seismic detectors to receive seismic wave signals propagated by the seismic waves, the seismic detectors being deployed on the surface; arranging the seismic wave signals received by all the seismic detectors according to a common offset, the common offset referring to the distance from a midpoint between a seismic detector and the artificial seismic source to the seismic detector; and combining all the seismic wave signals having the same common offset to form the common-offset seismic wavefield data.

[0008] Optionally, the size of each row and each column of the frequency-space domain seismic data matrix is ​​calculated based on the L1 norm to obtain the row value of each row and the column value of each column, respectively, including: calculating the size of each row in the frequency-space domain seismic data matrix according to a first formula to obtain the row value corresponding to each row, and the first formula is β m represents the element of the mth row in the frequency space domain seismic data matrix, λ m represents the row value corresponding to the mth row; the size of each column in the frequency space domain seismic data matrix is ​​calculated according to the second formula to obtain the column value corresponding to each column, and the second formula is β n represents the element of the nth column in the frequency space domain seismic data matrix, λ n Indicates the column value corresponding to the nth column.

[0009] Optionally, a random selection method is used based on the row value and the column value to extract multiple rows and columns of the frequency-space domain seismic data matrix to obtain target rows and target columns, including: randomly selecting a group of first elements in a selection interval according to the row value, the selection interval is an open interval from 0 to the maximum row value, the maximum row value is the maximum value of all the row values, and the first element is the numerical value represented by any row in the frequency-space domain seismic data matrix; when the first element satisfies a first selection condition, the row where the first element is located is determined as the target row, and the first selection condition is that the row value corresponding to the row where the first element is located is less than the product of a set coefficient and the maximum row value; randomly selecting a group of second elements in a selection interval according to the column value, the selection interval is an open interval from 0 to the maximum column value, the maximum column value is the maximum value of all the column values, and the second element is the numerical value represented by any column in the frequency-space domain seismic data matrix; when the second element satisfies a second selection condition, the column where the second element is located is determined as the target column, and the second selection condition is that the column value corresponding to the column where the second element is located is less than the product of the set coefficient and the maximum column value.

[0010] Optionally, the target rows and the target columns are reconstructed based on a matrix transformation algorithm to obtain an approximate matrix of the nonlinear signal in the common offset seismic wavefield data, including: performing matrix decomposition on a first matrix and a second matrix respectively to obtain a first basis vector matrix and a second basis vector matrix respectively, the first matrix being a matrix constructed by the target rows, and the second matrix being a matrix constructed by the target columns; multiplying the transpose of the second basis vector matrix, the frequency-space domain seismic data matrix, and the first basis vector matrix to obtain a third matrix; performing singular value decomposition on the third matrix, and retaining the first k singular values ​​and the singular vectors corresponding to the singular values ​​to construct a low-rank matrix; and reconstructing the approximate values ​​of the common offset seismic wavefield data through the low-rank matrix to obtain the approximate matrix.

[0011] Optionally, reconstructing the approximate value of the common offset seismic wavefield data by the low rank matrix to obtain the approximate matrix includes: calculating the approximate matrix according to the low rank matrix and the common offset seismic wavefield data in combination with a third formula, wherein the third formula is: Denotes the approximate matrix, Q m represents the first basis vector matrix, Q n Denotes the second basis vector matrix, B k represents the low-rank matrix, and D represents the common-offset seismic wavefield data.

[0012] Optionally, the approximate matrix is ​​imaged using a pre-stack depth migration algorithm to generate a surface fracture distribution image, including: constructing a medium model, the medium model is used to describe the speed of seismic wave propagation in different underground media; calculating the ray path of the seismic wave from the artificial source to the seismic detector according to the medium model; applying time migration to the approximate matrix according to the ray path to obtain nonlinear wave field data after migration, the time migration refers to adjusting the time axis of the nonlinear wave field data according to the propagation time of the seismic wave to simulate the propagation delay of the seismic wave in the underground medium; and spatially superimposing the nonlinear wave field data after migration to generate the surface fracture distribution image.

[0013] According to another aspect of the present application, a near-surface crack detection device is provided, the device comprising: an acquisition unit, used to acquire common-offset seismic wavefield data of a surface area to be processed; a construction unit, used to construct a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, the frequency-space domain seismic data matrix being in the form of a Hankel matrix; a calculation unit, used to calculate the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtain the row value of each row and the column value of each column respectively; a selection and extraction unit, used to extract multiple rows and multiple columns of the frequency-space domain seismic data matrix using a random selection method based on the row value and the column value, and obtain target rows and target columns; a reconstruction unit, used to reconstruct the target rows and the target columns based on a matrix transformation algorithm, and obtain an approximate matrix of nonlinear signals in the common-offset seismic wavefield data matrix, the approximate matrix representing nonlinear wavefield data information approximately formed by surface cracks; an imaging unit, used to perform homing imaging on the approximate matrix using a pre-stack depth migration algorithm, and generate a surface crack distribution image, so as to perform crack exploration according to the surface crack distribution image.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

[0015] According to another aspect of the present application, a computer program product is provided, comprising computer instructions, wherein when the computer instructions are executed by a processor, any one of the methods described above is implemented.

[0016] By applying the technical solution of the present application, in a near-surface crack detection method, first, common-offset seismic wavefield data of a surface area to be processed are obtained; then, a frequency-space domain seismic data matrix is ​​constructed by Fourier transform according to the common-offset seismic wavefield data, and the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix; then, the size of each row and each column of the frequency-space domain seismic data matrix is ​​calculated based on the L1 norm, and the row value of each row and the column value of each column are obtained respectively; then, based on the row value and the column value, multiple rows and multiple columns of the frequency-space domain seismic data matrix are extracted by a random selection method to obtain target rows and target columns; then, the target rows and the target columns are reconstructed based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common-offset seismic wavefield data, and the approximate matrix represents nonlinear wavefield data information formed by surface cracks; finally, the approximate matrix is ​​imaged by a prestack depth migration algorithm to generate a surface crack distribution image, so as to perform crack exploration according to the surface crack distribution image. The present application obtains high-frequency seismic data on the surface and represents it as a complex matrix in the frequency-space domain, i.e., a frequency-space domain seismic data matrix; extracts key columns and rows in the frequency-space domain seismic data matrix by a random selection method to construct a matrix; reconstructs the selected columns and rows based on a matrix transformation algorithm to obtain an approximate matrix; further optimizes the approximate matrix and the original high-frequency seismic data through iterative calculation technology, and finally generates a high-precision surface crack distribution profile, i.e., a surface crack distribution image. This method can effectively reduce noise interference and improve the accuracy of crack imaging, and is suitable for geological exploration, earthquake monitoring, and engineering safety assessment. The present application solves the problem that the prior art cannot effectively extract and reconstruct the nonlinear signal components of cracks in a complex geological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A hardware structure block diagram of a mobile terminal for executing a near-surface crack detection method provided in an embodiment of the present application is shown;

[0018] Figure 2 A schematic flow chart of a near-surface crack detection method provided according to an embodiment of the present application is shown;

[0019] Figure 3 A structural block diagram of a near-surface crack detection device provided according to an embodiment of the present application is shown.

[0020] The above drawings include the following reference numerals:

[0021] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0022] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] As introduced in the background technology, most of the existing technologies rely on data smoothing and noise suppression strategies, but it is still difficult to take into account both signal resolution and computational efficiency. In order to solve the problem that the nonlinear signal components of fractures cannot be effectively extracted and reconstructed in a complex geological environment, the embodiments of the present application provide a near-surface fracture detection method, a near-surface fracture detection device, a computer-readable storage medium and a computer program product.

[0026] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a near-surface crack detection method according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0028] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the near-surface fracture detection method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a near-surface crack detection method running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Figure 2 FIG. 1 is a flow chart of a near-surface crack detection method according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0031] Step S201, obtaining common-offset seismic wavefield data of the surface area to be processed.

[0032] Specifically, it is first necessary to collect seismic wave field data of the surface area to be processed, which can usually be obtained through seismic exploration or seismic monitoring instruments. These data include information such as seismic wave propagation time and seismic wave amplitude. Data preprocessing: Preprocess the collected seismic wave field data, including denoising, time domain filtering, frequency domain filtering, etc., to improve the quality and accuracy of the data. Common offset processing is a seismic data processing method. By performing common offset processing on seismic wave field data, the reflection interface information of the underground medium can be obtained. Finally, the above-mentioned common offset seismic wave field data is obtained, and the above-mentioned common offset seismic wave field data is a data matrix in the time-space domain.

[0033] Step S202: constructing a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix.

[0034] Specifically, the above-mentioned common offset seismic wave field data in the time-space domain form is subjected to Fourier transformation, and the time domain data is converted into frequency domain data. Fourier transformation can be efficiently calculated by the fast Fourier transform (FFT) algorithm. The frequency domain data after Fourier transformation is organized into a matrix form, in which the number of rows represents different frequency points and the number of columns represents different spatial positions (common offsets). The frequency space domain seismic data matrix is ​​subjected to Hankel transformation, and the frequency space domain data is converted into the form of a Hankel matrix. Hankel transformation is a special matrix transformation, S=H(D), where S represents the transformation result of the common offset seismic wave field data D, that is, the real-time frequency space domain seismic data matrix, and H represents the above-mentioned Hankel matrix transformation. The final Hankel matrix represents the distribution of frequency space domain seismic data in space, which can be used for further seismic data processing and analysis.

[0035] Step S203, calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining the row value of each row and the column value of each column respectively.

[0036] Specifically, the L1 norm refers to the sum of the absolute values ​​of each element in the matrix. Calculating the L1 norm of each row and column can get the size of the row or column. For the seismic data matrix in the frequency space domain, calculating the L1 norm of each row and column can help analyze the importance of each frequency component in different spatial positions, so as to better understand the characteristics and laws of seismic data. In the future, by comparing the sizes of different row values ​​or column values, the frequency components or spatial positions with important influences can be found, which is helpful for the analysis and interpretation of seismic data.

[0037] Step S204, extracting multiple rows and multiple columns of the frequency-space domain seismic data matrix using a random selection method based on the row values ​​and the column values ​​to obtain target rows and target columns.

[0038] Specifically, retaining important rows and columns based on the above row values ​​and the above column values ​​can reduce the dimension of the data and improve the data processing efficiency. At the same time, according to the size of different row values ​​or column values, the frequency components or spatial positions that have an important impact on the analysis and prediction of seismic data can be selected to obtain the above target rows and target columns.

[0039] Step S205, reconstructing the target rows and the target columns based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common offset seismic wavefield data, wherein the approximate matrix represents nonlinear wavefield data information formed by surface fractures.

[0040] Specifically, a corresponding matrix is ​​constructed according to the target row and target column, and a matrix transformation algorithm is used for reconstruction. Some common matrix transformation algorithms can be used, such as singular value decomposition (SVD), wavelet transform, etc. The reconstructed matrix is ​​approximated by nonlinear signals to obtain an approximate matrix. Some nonlinear transformation methods can be used, such as power transform, logarithmic transform, etc. The approximate matrix finally obtained represents the nonlinear wave field data information formed by surface cracks. It should be noted that by introducing the L1 norm selection mechanism and the matrix transformation algorithm, the influence of noise can be effectively reduced and the robustness of the signal can be improved. Based on the nonlinear transformation of the matrix, this method can quickly extract the linear signal components in the data and accurately reconstruct the nonlinear signal. It is suitable for high-precision crack detection under complex geological conditions, effectively improving the detection accuracy and calculation efficiency, and has a wider application prospect.

[0041] Step S206, using a pre-stack depth migration algorithm to perform homing imaging on the approximate matrix to generate a surface fracture distribution image, so as to perform fracture exploration based on the surface fracture distribution image.

[0042] Specifically, the nonlinear wave field data is imaged by the prestack depth migration algorithm to obtain the fine positioning results of the surface cracks, that is, the above-mentioned surface crack distribution image. The prestack depth migration algorithm includes the Kirchhoff migration algorithm and other algorithms. Specifically, the approximate matrix is ​​used as the input data and processed by the prestack depth migration algorithm. First, the input data is preprocessed, including denoising, profile picking, etc.; then the prestack depth migration algorithm is processed to obtain the image of the underground structure; the obtained underground structure image is subjected to crack detection. The cracks are usually manifested as discontinuities in the underground structure. The cracks can be detected by edge detection algorithms or other crack detection algorithms; finally, a surface crack distribution image is generated. The crack position information obtained by the crack detection algorithm can be registered with the underground structure image to finally generate a surface crack distribution image.

[0043] In this embodiment, first, common-offset seismic wavefield data of the surface area to be processed are obtained; then, a frequency-space domain seismic data matrix is ​​constructed based on the common-offset seismic wavefield data using Fourier transform, and the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix; then, the size of each row and each column of the frequency-space domain seismic data matrix is ​​calculated based on the L1 norm, and the row value of each row and the column value of each column are obtained respectively; then, based on the row value and the column value, multiple rows and multiple columns of the frequency-space domain seismic data matrix are extracted using a random selection method to obtain target rows and target columns; then, the target rows and the target columns are reconstructed based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common-offset seismic wavefield data, and the approximate matrix represents nonlinear wavefield data information formed by surface fractures; finally, the approximate matrix is ​​imaged using a prestack depth migration algorithm to generate a surface fracture distribution image, so as to perform fracture exploration based on the surface fracture distribution image. The present application obtains high-frequency seismic data on the surface and represents it as a complex matrix in the frequency-space domain, i.e., a frequency-space domain seismic data matrix; extracts key columns and rows in the frequency-space domain seismic data matrix by a random selection method to construct a matrix; reconstructs the selected columns and rows based on a matrix transformation algorithm to obtain an approximate matrix; further optimizes the approximate matrix and the original high-frequency seismic data through iterative calculation technology, and finally generates a high-precision surface crack distribution profile, i.e., a surface crack distribution image. This method can effectively reduce noise interference and improve the accuracy of crack imaging, and is suitable for geological exploration, earthquake monitoring, and engineering safety assessment. The present application solves the problem that the prior art cannot effectively extract and reconstruct the nonlinear signal components of cracks in a complex geological environment.

[0044] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the near-surface crack detection method of the present application will be described in detail below in combination with specific embodiments.

[0045] In order to improve the resolution and accuracy of the data, in an optional implementation manner, the above step S201 includes:

[0046] Step S2011, using an artificial seismic source to excite seismic waves on the surface of the above-mentioned surface area to be processed, wherein the above-mentioned artificial seismic source at least includes explosives, an air gun or a vibrator;

[0047] Step S2012, controlling a plurality of seismic detectors to receive seismic wave signals transmitted by the seismic waves, the seismic detectors being deployed on the surface of the earth;

[0048] Step S2013, sorting the seismic wave signals received by all the seismic detectors according to a common offset, where the common offset refers to the distance from the midpoint between the seismic detector and the artificial seismic source to the seismic detector;

[0049] Step S2014: combining all the seismic wave signals having the same common offset to form the common offset seismic wave field data.

[0050] In the above embodiment, a grid of seismic detectors is deployed at a suitable location in the surface area to be processed, and the location of an artificial source (such as explosives, air guns or vibrators) is set. By controlling the release of energy from the artificial source, seismic waves are stimulated to propagate underground and reflect, refract and propagate on the surface. The seismic detector receives the seismic wave signal, records and transmits it to the data processing center. The data processing center sorts the received seismic wave signals according to the common offset, that is, calculates the distance from the midpoint between the seismic detector and the artificial source to the seismic detector, and combines the seismic wave signals with the same common offset. The combined data forms common offset seismic wave field data, and underground structure information such as underground rock formations, ore bodies, groundwater, etc. can be obtained through further processing and analysis. By sorting and combining seismic wave signals to obtain common offset seismic wave field data, the resolution and accuracy of the data can be improved, the characteristics of the underground structure can be better revealed, and important geological information can be provided for underground exploration.

[0051] In order to improve the accuracy and reliability of seismic data processing and interpretation, in an optional implementation manner, the above step S203 includes:

[0052] Step S2031, calculate the size of each row in the frequency space domain seismic data matrix according to the first formula to obtain the row value corresponding to each row. The first formula is: β m represents the element of the mth row in the above frequency space domain seismic data matrix, λ m Indicates the above row value corresponding to the mth row;

[0053] Step S2032, calculating the size of each column in the frequency-space domain seismic data matrix according to the second formula to obtain the column value corresponding to each column. The second formula is: β n represents the element of the nth column in the above frequency space domain seismic data matrix, λ n Indicates the above column value corresponding to the nth column.

[0054] In the above embodiment, for each row of the frequency-space domain seismic data matrix, its L1 norm is calculated according to the above first formula, that is, all the elements of the row are summed after taking the absolute value, and the row value of each row is obtained. For each column of the frequency-space domain seismic data matrix, its L1 norm is calculated according to the above second formula, that is, all the elements of the column are summed after taking the absolute value, and the column value of each column is obtained. The distribution of seismic data in different frequencies and spaces can be understood through row values ​​and column values, which helps to improve the accuracy and reliability of seismic data processing and interpretation.

[0055] In order to reduce the dimension of data and improve data processing efficiency, in an optional implementation, the above step S204 includes:

[0056] Step S2041, randomly selecting a group of first elements in a selection interval according to the row value, the selection interval is an open interval from 0 to the maximum row value, the maximum row value is the maximum value of all the row values, and the first element is a value represented by any row in the frequency space domain seismic data matrix;

[0057] Step S2042, when the first element satisfies a first selection condition, determining the row where the first element is located as the target row, and the first selection condition is that the row value corresponding to the row where the first element is located is less than the product of a set coefficient and the maximum row value;

[0058] Step S2043, randomly selecting a group of second elements in a selection interval according to the column value, the selection interval is an open interval from 0 to the maximum column value, the maximum column value is the maximum value of all the column values, and the second element is a value represented by any column in the frequency-space domain seismic data matrix;

[0059] Step S2044, when the second element satisfies the second selection condition, the column where the second element is located is determined as the target column, and the second selection condition is that the column value corresponding to the column where the second element is located is less than the product of the set coefficient and the maximum column value.

[0060] In the above embodiment, the row value λ is calculated m The maximum value of That is, the maximum row value mentioned above is distributed in the interval (0, λ max1) randomly selects a row of elements, namely the first element mentioned above, and the row value corresponding to the row where the first element is located is recorded as λ' m When λ' is satisfied m <σλ max1 , then select the mth row. Similarly, calculate the column value λ n The maximum value of That is, the maximum row value mentioned above is distributed in the interval (0, λ max2 ) randomly selects a column of elements, namely the second element mentioned above, and the row value corresponding to the column where the second element is located is recorded as λ n '. When λ is satisfied n '<σλ max2 When , the nth column is selected as the target column. When one column is selected, there is only one target column. When multiple columns are selected, there are multiple target columns. Frequency components or time / space positions that have an important impact on seismic data analysis and prediction can be selected according to the size of different row values ​​or column values. By retaining important rows or columns, the dimension of the data can be reduced and the data processing efficiency can be improved.

[0061] In order to effectively reduce the influence of noise and effectively adapt to high-precision crack detection under complex geological conditions, in an optional implementation, the above step S205 includes:

[0062] Step S2051, performing matrix decomposition on the first matrix and the second matrix respectively to obtain a first basis vector matrix and a second basis vector matrix respectively, wherein the first matrix is ​​a matrix constructed by the target rows, and the second matrix is ​​a matrix constructed by the target columns;

[0063] Step S2052, multiplying the transpose of the second basis vector matrix, the frequency-space domain seismic data matrix and the first basis vector matrix to obtain a third matrix;

[0064] Step S2053, performing singular value decomposition on the third matrix, and retaining the first k singular values ​​and the singular vectors corresponding to the singular values ​​to construct a low-rank matrix;

[0065] Step S2054: reconstruct the approximate value of the common-offset seismic wavefield data through the low-rank matrix to obtain the approximate matrix.

[0066] In the above embodiment, the selected columns and rows are reconstructed based on the matrix transformation algorithm. Assuming that the matrix constructed by the selected target row is M and the matrix constructed by the target column is N, QR decomposition is performed on the target row and the target column respectively, and the first basis vector matrix Q is obtained. m And the second basis vector matrix Q n , where the QR decomposition formula is M = Q m R mand N = Q n R n , the second basis vector matrix Q n The transpose of the frequency space domain seismic data matrix S and the first basis vector Q m The matrices are multiplied to obtain the third matrix B, and the calculation formula is Perform singular value decomposition on the third matrix B. The singular value decomposition formula is B = UΣV T , retain the first k singular values ​​and their corresponding singular vectors, and construct a low-rank matrix B k ,in Through the low-rank matrix B k , the approximate value of D for reconstructing the common offset seismic wave field data That is, the approximate matrix. Approximate the nonlinear wave field information formed by surface cracks. The purpose of this process is to reduce the dimension of the common offset seismic wave field data through matrix decomposition and singular value decomposition, which can effectively reduce the impact of noise and improve the robustness of the signal. It can quickly extract linear signal components through matrix nonlinear transformation and reconstruct nonlinear signals through stable iterative algorithms, effectively adapting to high-precision crack detection under complex geological conditions. The reconstructed approximate matrix can more clearly display the characteristics of the seismic wave field data, which helps seismic prospectors to more accurately analyze underground structures and judge geological conditions.

[0067] In order to accurately reconstruct the nonlinear signal, in an optional implementation manner, the above step S2054 includes:

[0068] Step S20541, the approximate matrix is ​​calculated based on the low-rank matrix and the common offset seismic wave field data in combination with a third formula, where the third formula is: Denotes the above approximate matrix, Q m represents the first basis vector matrix, Q n represents the second basis vector matrix, B k represents the above low-rank matrix, and D represents the above common-offset seismic wavefield data.

[0069] In the above embodiment, in order to quickly extract the linear signal components in the common offset seismic wave field data and accurately reconstruct the nonlinear signal, the low rank matrix B k The common offset seismic wave field data is brought into the third formula to calculate and obtain the nonlinear wave field information of the approximate surface crack formation, that is, the approximate matrix. In the third formula, Q n B k Q mIt represents the linear signal component in the common offset seismic wave field data. The above approximate matrix represents the reconstructed nonlinear signal, and the nonlinear signal represents the cracks in the surface area.

[0070] In order to improve the precise positioning of the surface cracks, in an optional implementation manner, the above step S206 includes:

[0071] Step S2061, constructing a medium model, wherein the medium model is used to describe the propagation speed of seismic waves in different underground media;

[0072] Step S2062, calculating the ray path of the seismic wave from the artificial source to the seismic detector according to the medium model;

[0073] Step S2063, applying time shift to the approximate matrix according to the ray path to obtain nonlinear wavefield data after shifting, wherein the time shift refers to adjusting the time axis of the nonlinear wavefield data according to the propagation time of the seismic wave to simulate the propagation delay of the seismic wave in the underground medium;

[0074] Step S2064: spatially superimpose the nonlinear wave field data after migration to generate the surface fracture distribution image.

[0075] In the above embodiment, the Kirchhoff migration algorithm is used to perform homing imaging on the nonlinear wave field data to obtain the fine positioning result of the surface cracks. In the fine positioning of the surface cracks, the algorithm can extract the position and characteristics of the cracks from the nonlinear wave field data. The following is the detailed process of the Kirchhoff migration algorithm to achieve fine positioning of the surface cracks: The Kirchhoff migration algorithm requires a velocity model of the underground medium, which describes the speed of the seismic wave propagating in different underground media. This model can be an initial model based on known information from geological exploration, or it can be a model that is continuously optimized through an iterative process. According to the medium model, the propagation path of the seismic wave from the source point to the receiving point, that is, the above-mentioned ray path, is calculated. This involves calculating the propagation time of the seismic wave in different speed media. The calculation of the ray path is the core of the Kirchhoff migration algorithm because it determines how the wave field data is offset and superimposed. For each seismic wave field data point, a time offset is applied according to the calculated ray path. Time offset refers to adjusting the time axis of the wave field data according to the propagation time of the seismic wave to simulate the propagation delay of the wave in the underground medium. The offset wave field data is superimposed in space. This step is to construct a continuous underground image, in which the amplitude of each point is obtained by superimposing multiple offset data points. The stacking process needs to take into account the coherence and interference effects of seismic waves. During the stacking process, an imaging condition needs to be determined, which usually involves filtering and gain control of the stacking results to improve the resolution and signal-to-noise ratio of the image. Finally, the obtained imaging results are analyzed and interpreted to identify the location, size and morphology of surface cracks. If the imaging results are not ideal, it may be necessary to return to the definition step of the medium model, optimize the model, and then repeat the above process until a satisfactory crack location result is obtained, that is, the above-mentioned surface crack distribution image.

[0076] It should be noted that the Kirchhoff migration algorithm is a technology used for seismic data processing and imaging. It reconstructs the image of the underground structure by simulating the propagation path of seismic waves in the underground medium. The Kirchhoff migration algorithm is a computationally intensive process that requires a lot of computing resources and time, especially when processing large-scale data sets. With the development of computing technology, especially the application of parallel computing and high-performance computing, the application of the Kirchhoff migration algorithm in practice has become more extensive and efficient.

[0077] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0078] The embodiment of the present application also provides a near-surface crack detection device. It should be noted that the near-surface crack detection device of the embodiment of the present application can be used to execute the near-surface crack detection method provided by the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0079] The following is an introduction to the near-surface crack detection device provided in an embodiment of the present application.

[0080] Figure 3 is a structural block diagram of a near-surface crack detection device according to an embodiment of the present application. Figure 3 As shown, the device comprises:

[0081] The acquisition unit 10 is used to acquire common-offset seismic wavefield data of the surface area to be processed.

[0082] Specifically, it is first necessary to collect seismic wave field data of the surface area to be processed, which can usually be obtained through seismic exploration or seismic monitoring instruments. These data include information such as seismic wave propagation time and seismic wave amplitude. Data preprocessing: Preprocess the collected seismic wave field data, including denoising, time domain filtering, frequency domain filtering, etc., to improve the quality and accuracy of the data. Common offset processing is a seismic data processing method. By performing common offset processing on seismic wave field data, the reflection interface information of the underground medium can be obtained. Finally, the above-mentioned common offset seismic wave field data is obtained, and the above-mentioned common offset seismic wave field data is a data matrix in the time-space domain.

[0083] The construction unit 20 is used to construct a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix.

[0084] Specifically, the above-mentioned common offset seismic wave field data in the time-space domain form is subjected to Fourier transformation, and the time domain data is converted into frequency domain data. Fourier transformation can be efficiently calculated by the fast Fourier transform (FFT) algorithm. The frequency domain data after Fourier transformation is organized into a matrix form, in which the number of rows represents different frequency points and the number of columns represents different spatial positions (common offsets). The frequency space domain seismic data matrix is ​​subjected to Hankel transformation, and the frequency space domain data is converted into the form of a Hankel matrix. Hankel transformation is a special matrix transformation, S=H(D), where S represents the transformation result of the common offset seismic wave field data D, that is, the real-time frequency space domain seismic data matrix, and H represents the above-mentioned Hankel matrix transformation. The final Hankel matrix represents the distribution of frequency space domain seismic data in space, which can be used for further seismic data processing and analysis.

[0085] The calculation unit 30 is used to calculate the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtain the row value of each row and the column value of each column respectively.

[0086] Specifically, the L1 norm refers to the sum of the absolute values ​​of each element in the matrix. Calculating the L1 norm of each row and column can get the size of the row or column. For the seismic data matrix in the frequency space domain, calculating the L1 norm of each row and column can help analyze the importance of each frequency component in different spatial positions, so as to better understand the characteristics and laws of seismic data. In the future, by comparing the sizes of different row values ​​or column values, the frequency components or spatial positions with important influences can be found, which is helpful for the analysis and interpretation of seismic data.

[0087] The selection and extraction unit 40 is used to extract multiple rows and multiple columns of the frequency-space domain seismic data matrix by a random selection method based on the row value and the column value to obtain target rows and target columns.

[0088] Specifically, retaining important rows and columns based on the above row values ​​and the above column values ​​can reduce the dimension of the data and improve the data processing efficiency. At the same time, according to the size of different row values ​​or column values, the frequency components or spatial positions that have an important impact on the analysis and prediction of seismic data can be selected to obtain the above target rows and target columns.

[0089] The reconstruction unit 50 is used to reconstruct the target rows and the target columns based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common offset seismic wavefield data, wherein the approximate matrix represents nonlinear wavefield data information formed by surface fractures.

[0090] Specifically, a corresponding matrix is ​​constructed according to the target row and target column, and a matrix transformation algorithm is used for reconstruction. Some common matrix transformation algorithms can be used, such as singular value decomposition (SVD), wavelet transform, etc. The reconstructed matrix is ​​approximated by nonlinear signals to obtain an approximate matrix. Some nonlinear transformation methods can be used, such as power transform, logarithmic transform, etc. The approximate matrix finally obtained represents the nonlinear wave field data information formed by surface cracks. It should be noted that by introducing the L1 norm selection mechanism and the matrix transformation algorithm, the influence of noise can be effectively reduced and the robustness of the signal can be improved. Based on the nonlinear transformation of the matrix, this method can quickly extract the linear signal components in the data and accurately reconstruct the nonlinear signal. It is suitable for high-precision crack detection under complex geological conditions, effectively improving the detection accuracy and calculation efficiency, and has a wider application prospect.

[0091] The imaging unit 60 is used to perform homing imaging on the above approximate matrix using a prestack depth migration algorithm to generate a surface fracture distribution image, so as to perform fracture exploration based on the surface fracture distribution image.

[0092] Specifically, the nonlinear wave field data is imaged by the prestack depth migration algorithm to obtain the fine positioning results of the surface cracks, that is, the above-mentioned surface crack distribution image. The prestack depth migration algorithm includes the Kirchhoff migration algorithm and other algorithms. Specifically, the approximate matrix is ​​used as the input data and processed by the prestack depth migration algorithm. First, the input data is preprocessed, including denoising, profile picking, etc.; then the prestack depth migration algorithm is processed to obtain the image of the underground structure; the obtained underground structure image is subjected to crack detection. The cracks are usually manifested as discontinuities in the underground structure. The cracks can be detected by edge detection algorithms or other crack detection algorithms; finally, a surface crack distribution image is generated. The crack position information obtained by the crack detection algorithm can be registered with the underground structure image to finally generate a surface crack distribution image.

[0093] In this embodiment, an acquisition unit is used to acquire common-offset seismic wavefield data of a surface area to be processed; a construction unit is used to construct a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix; a calculation unit is used to calculate the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtain the row value of each row and the column value of each column respectively; a selection and extraction unit is used to extract multiple rows and multiple columns of the frequency-space domain seismic data matrix using a random selection method based on the row value and the column value, and obtain target rows and target columns; a reconstruction unit is used to reconstruct the target rows and the target columns based on a matrix transformation algorithm, and obtain an approximate matrix of nonlinear signals in the common-offset seismic wavefield data, wherein the approximate matrix represents nonlinear wavefield data information formed by surface cracks; an imaging unit is used to perform homing imaging on the approximate matrix using a prestack depth migration algorithm, and generate a surface crack distribution image, so as to perform crack exploration according to the surface crack distribution image. The present application obtains high-frequency seismic data on the surface and represents it as a complex matrix in the frequency-space domain, i.e., a frequency-space domain seismic data matrix; extracts key columns and rows in the frequency-space domain seismic data matrix by a random selection method to construct a matrix; reconstructs the selected columns and rows based on a matrix transformation algorithm to obtain an approximate matrix; further optimizes the approximate matrix and the original high-frequency seismic data through iterative calculation technology, and finally generates a high-precision surface crack distribution profile, i.e., a surface crack distribution image. This method can effectively reduce noise interference and improve the accuracy of crack imaging, and is suitable for geological exploration, earthquake monitoring, and engineering safety assessment. The present application solves the problem that the prior art cannot effectively extract and reconstruct the nonlinear signal components of cracks in a complex geological environment.

[0094] In order to improve the resolution and accuracy of the data, in an optional implementation manner, the acquisition unit includes:

[0095] An excitation module, used for exciting seismic waves on the surface of the above-mentioned surface area to be processed by using an artificial seismic source, wherein the above-mentioned artificial seismic source at least includes explosives, an air gun or a vibrator;

[0096] A control module, used for controlling a plurality of seismic detectors to receive seismic wave signals transmitted by the seismic waves, wherein the seismic detectors are deployed on the surface of the earth;

[0097] A sorting module, used for sorting the seismic wave signals received by all the seismic detectors according to a common offset, where the common offset refers to the distance from the midpoint between the seismic detector and the artificial seismic source to the seismic detector;

[0098] The combination module is used to combine all the seismic wave signals having the same common offset to form the common offset seismic wave field data.

[0099] In the above embodiment, a grid of seismic detectors is deployed at a suitable location in the surface area to be processed, and the location of an artificial source (such as explosives, air guns or vibrators) is set. By controlling the release of energy from the artificial source, seismic waves are stimulated to propagate underground and reflect, refract and propagate on the surface. The seismic detector receives the seismic wave signal, records and transmits it to the data processing center. The data processing center sorts the received seismic wave signals according to the common offset, that is, calculates the distance from the midpoint between the seismic detector and the artificial source to the seismic detector, and combines the seismic wave signals with the same common offset. The combined data forms common offset seismic wave field data, and underground structure information such as underground rock formations, ore bodies, groundwater, etc. can be obtained through further processing and analysis. By sorting and combining seismic wave signals to obtain common offset seismic wave field data, the resolution and accuracy of the data can be improved, the characteristics of the underground structure can be better revealed, and important geological information can be provided for underground exploration.

[0100] In order to improve the accuracy and reliability of seismic data processing and interpretation, in an optional implementation, the above-mentioned computing unit includes:

[0101] The first calculation module is used to calculate the size of each row in the frequency space domain seismic data matrix according to the first formula to obtain the row value corresponding to each row. The first formula is: β m represents the element of the mth row in the above frequency space domain seismic data matrix, λ m Indicates the above row value corresponding to the mth row;

[0102] The second calculation module is used to calculate the size of each column in the frequency space domain seismic data matrix according to the second formula to obtain the column value corresponding to each column. The second formula is: β n represents the element of the nth column in the above frequency space domain seismic data matrix, λ n Indicates the above column value corresponding to the nth column.

[0103] In the above embodiment, for each row of the frequency-space domain seismic data matrix, its L1 norm is calculated according to the above first formula, that is, all the elements of the row are summed after taking the absolute value, and the row value of each row is obtained. For each column of the frequency-space domain seismic data matrix, its L1 norm is calculated according to the above second formula, that is, all the elements of the column are summed after taking the absolute value, and the column value of each column is obtained. The distribution of seismic data in different frequencies and spaces can be understood through row values ​​and column values, which helps to improve the accuracy and reliability of seismic data processing and interpretation.

[0104] In order to reduce the dimension of data and improve data processing efficiency, in an optional implementation manner, the selection and extraction unit includes:

[0105] A first selection module is used to randomly select a group of first elements in a selection interval according to the row value, the selection interval is an open interval from 0 to a maximum row value, the maximum row value is a maximum value among all the row values, and the first element is a value represented by any row in the frequency space domain seismic data matrix;

[0106] A first determination module is used to determine the row where the first element is located as the target row when the first element satisfies a first selection condition, and the first selection condition is that the row value corresponding to the row where the first element is located is less than the product of a set coefficient and the maximum row value;

[0107] A second selection module is used to randomly select a group of second elements in a selection interval according to the column value, the selection interval is an open interval from 0 to the maximum column value, the maximum column value is the maximum value of all the column values, and the second element is a value represented by any column in the frequency space domain seismic data matrix;

[0108] The second determination module is used to determine the column where the second element is located as the target column when the second element meets the second selection condition, and the second selection condition is that the column value corresponding to the column where the second element is located is less than the product of the set coefficient and the maximum column value.

[0109] In the above embodiment, the row value λ is calculated m The maximum value of That is, the maximum row value mentioned above is distributed in the interval (0, λ max1 ) randomly selects a row of elements, namely the first element mentioned above, and the row value corresponding to the row where the first element is located is recorded as λ' m When λ' is satisfied m <σλ max1 , then select the mth row. Similarly, calculate the column value λ n The maximum value of That is, the maximum row value mentioned above is distributed in the interval (0, λ max2 ) randomly selects a column of elements, namely the second element mentioned above, and the row value corresponding to the column where the second element is located is recorded as λ′ n When λ′ is satisfied n <σλ max2When , the nth column is selected as the target column. When one column is selected, there is only one target column. When multiple columns are selected, there are multiple target columns. Frequency components or time / space positions that have an important impact on seismic data analysis and prediction can be selected according to the size of different row values ​​or column values. By retaining important rows or columns, the dimension of the data can be reduced and the data processing efficiency can be improved.

[0110] In order to effectively reduce the influence of noise and effectively adapt to high-precision crack detection under complex geological conditions, in an optional implementation, the reconstruction unit includes:

[0111] A first decomposition module is used to perform matrix decomposition on the first matrix and the second matrix respectively to obtain a first basis vector matrix and a second basis vector matrix respectively, wherein the first matrix is ​​a matrix constructed by the target rows, and the second matrix is ​​a matrix constructed by the target columns;

[0112] A third calculation module is used to multiply the transpose of the second basis vector matrix, the frequency-space domain seismic data matrix and the first basis vector matrix to obtain a third matrix;

[0113] A second decomposition module is used to perform singular value decomposition on the third matrix, and retain the first k singular values ​​and the singular vectors corresponding to the singular values ​​to construct a low-rank matrix;

[0114] The reconstruction module is used to reconstruct the approximate value of the common offset seismic wave field data through the low-rank matrix to obtain the approximate matrix.

[0115] In the above embodiment, the selected columns and rows are reconstructed based on the matrix transformation algorithm. Assuming that the matrix constructed by the selected target row is M and the matrix constructed by the target column is N, QR decomposition is performed on the target row and the target column respectively, and the first basis vector matrix Q is obtained. m And the second basis vector matrix Q n , where the QR decomposition formula is M = Q m R m and N = Q n R n , the second basis vector matrix Q n The transpose of the frequency space domain seismic data matrix S and the first basis vector Q m The matrices are multiplied to obtain the third matrix B, and the calculation formula is Perform singular value decomposition on the third matrix B. The singular value decomposition formula is B = UΣV T , retain the first k singular values ​​and their corresponding singular vectors, and construct a low-rank matrix B k ,in Through the low-rank matrix B k, the approximate value of D for reconstructing the common offset seismic wave field data That is, the approximate matrix. Approximate the nonlinear wave field information formed by surface cracks. The purpose of this process is to reduce the dimension of the common offset seismic wave field data through matrix decomposition and singular value decomposition, which can effectively reduce the impact of noise and improve the robustness of the signal. It can quickly extract linear signal components through matrix nonlinear transformation and reconstruct nonlinear signals through stable iterative algorithms, effectively adapting to high-precision crack detection under complex geological conditions. The reconstructed approximate matrix can more clearly display the characteristics of the seismic wave field data, which helps seismic prospectors to more accurately analyze underground structures and judge geological conditions.

[0116] In order to accurately reconstruct the nonlinear signal, in an optional implementation manner, the reconstruction module includes:

[0117] The calculation submodule is used to calculate the approximate matrix according to the low-rank matrix and the common offset seismic wave field data in combination with a third formula. The third formula is: Denotes the above approximate matrix, Q m represents the first basis vector matrix, Q n represents the second basis vector matrix, B k represents the above low-rank matrix, and D represents the above common-offset seismic wavefield data.

[0118] In the above embodiment, in order to quickly extract the linear signal components in the common offset seismic wave field data and accurately reconstruct the nonlinear signal, the low rank matrix B k The common offset seismic wave field data is brought into the third formula to calculate and obtain the nonlinear wave field information of the approximate surface crack formation, that is, the approximate matrix. In the third formula, Q n B k Q m It represents the linear signal component in the common offset seismic wave field data. The above approximate matrix represents the reconstructed nonlinear signal, and the nonlinear signal represents the cracks in the surface area.

[0119] In order to improve the fine positioning of surface cracks, in an optional implementation, the imaging unit includes:

[0120] A construction module, used for constructing a medium model, wherein the medium model is used for describing the speed at which seismic waves propagate in different underground media;

[0121] A fourth calculation module, used for calculating the ray path of the seismic wave from the artificial source to the seismic detector according to the medium model;

[0122] An offset module, used for applying a time offset to the approximate matrix according to the ray path to obtain offset nonlinear wavefield data, wherein the time offset refers to adjusting the time axis of the nonlinear wavefield data according to the propagation time of the seismic wave to simulate the propagation delay of the seismic wave in the underground medium;

[0123] The stacking module is used to spatially stack the nonlinear wave field data after migration to generate the surface fracture distribution image.

[0124] In the above embodiment, the Kirchhoff migration algorithm is used to perform homing imaging on the nonlinear wave field data to obtain the fine positioning result of the surface cracks. In the fine positioning of the surface cracks, the algorithm can extract the position and characteristics of the cracks from the nonlinear wave field data. The following is the detailed process of the Kirchhoff migration algorithm to achieve fine positioning of the surface cracks: The Kirchhoff migration algorithm requires a velocity model of the underground medium, which describes the speed of the seismic wave propagating in different underground media. This model can be an initial model based on known information from geological exploration, or it can be a model that is continuously optimized through an iterative process. According to the medium model, the propagation path of the seismic wave from the source point to the receiving point, that is, the above-mentioned ray path, is calculated. This involves calculating the propagation time of the seismic wave in different speed media. The calculation of the ray path is the core of the Kirchhoff migration algorithm because it determines how the wave field data is offset and superimposed. For each seismic wave field data point, a time offset is applied according to the calculated ray path. Time offset refers to adjusting the time axis of the wave field data according to the propagation time of the seismic wave to simulate the propagation delay of the wave in the underground medium. The offset wave field data is superimposed in space. This step is to construct a continuous underground image, in which the amplitude of each point is obtained by superimposing multiple offset data points. The stacking process needs to take into account the coherence and interference effects of seismic waves. During the stacking process, an imaging condition needs to be determined, which usually involves filtering and gain control of the stacking results to improve the resolution and signal-to-noise ratio of the image. Finally, the obtained imaging results are analyzed and interpreted to identify the location, size and morphology of surface cracks. If the imaging results are not ideal, it may be necessary to return to the definition step of the medium model, optimize the model, and then repeat the above process until a satisfactory crack location result is obtained, that is, the above-mentioned surface crack distribution image.

[0125] It should be noted that the Kirchhoff migration algorithm is a technology used for seismic data processing and imaging. It reconstructs the image of the underground structure by simulating the propagation path of seismic waves in the underground medium. The Kirchhoff migration algorithm is a computationally intensive process that requires a lot of computing resources and time, especially when processing large-scale data sets. With the development of computing technology, especially the application of parallel computing and high-performance computing, the application of the Kirchhoff migration algorithm in practice has become more extensive and efficient.

[0126] The above-mentioned near-surface crack detection device includes a processor and a memory, and the above-mentioned acquisition unit, construction unit and calculation unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in any combination.

[0127] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the problem that the nonlinear signal components of the fracture cannot be effectively extracted and reconstructed in a complex geological environment in the prior art can be solved by adjusting the kernel parameters.

[0128] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0129] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the near-surface crack detection method.

[0130] Specifically, the near-surface crack detection method includes:

[0131] Step S201, obtaining common offset seismic wave field data of the surface area to be processed;

[0132] Step S202, constructing a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix;

[0133] Step S203, calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining the row value of each row and the column value of each column respectively;

[0134] Step S204, extracting multiple rows and multiple columns of the frequency-space domain seismic data matrix using a random selection method based on the row values ​​and the column values ​​to obtain target rows and target columns;

[0135] Step S205, reconstructing the target row and the target column based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common offset seismic wavefield data, wherein the approximate matrix represents nonlinear wavefield data information formed by surface fractures;

[0136] Step S206, using a pre-stack depth migration algorithm to perform homing imaging on the approximate matrix to generate a surface fracture distribution image, so as to perform fracture exploration based on the surface fracture distribution image.

[0137] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the near-surface crack detection method is executed when the program is run.

[0138] Specifically, the near-surface crack detection method includes:

[0139] Step S201, obtaining common offset seismic wave field data of the surface area to be processed;

[0140] Step S202, constructing a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix;

[0141] Step S203, calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining the row value of each row and the column value of each column respectively;

[0142] Step S204, extracting multiple rows and multiple columns of the frequency-space domain seismic data matrix using a random selection method based on the row values ​​and the column values ​​to obtain target rows and target columns;

[0143] Step S205, reconstructing the target row and the target column based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common offset seismic wavefield data, wherein the approximate matrix represents nonlinear wavefield data information formed by surface fractures;

[0144] Step S206, using a pre-stack depth migration algorithm to perform homing imaging on the approximate matrix to generate a surface fracture distribution image, so as to perform fracture exploration based on the surface fracture distribution image.

[0145] An embodiment of the present invention provides a surface crack detection system, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, at least the following steps are implemented:

[0146] Step S201, obtaining common offset seismic wave field data of the surface area to be processed;

[0147] Step S202, constructing a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix;

[0148] Step S203, calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining the row value of each row and the column value of each column respectively;

[0149] Step S204, extracting multiple rows and multiple columns of the frequency-space domain seismic data matrix using a random selection method based on the row values ​​and the column values ​​to obtain target rows and target columns;

[0150] Step S205, reconstructing the target row and the target column based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common offset seismic wavefield data, wherein the approximate matrix represents nonlinear wavefield data information formed by surface fractures;

[0151] Step S206, using a pre-stack depth migration algorithm to perform homing imaging on the approximate matrix to generate a surface fracture distribution image, so as to perform fracture exploration based on the surface fracture distribution image.

[0152] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0153] Step S201, obtaining common offset seismic wave field data of the surface area to be processed;

[0154] Step S202, constructing a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix;

[0155] Step S203, calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining the row value of each row and the column value of each column respectively;

[0156] Step S204, extracting multiple rows and multiple columns of the frequency-space domain seismic data matrix using a random selection method based on the row values ​​and the column values ​​to obtain target rows and target columns;

[0157] Step S205, reconstructing the target row and the target column based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common offset seismic wavefield data, wherein the approximate matrix represents nonlinear wavefield data information formed by surface fractures;

[0158] Step S206, using a pre-stack depth migration algorithm to perform homing imaging on the approximate matrix to generate a surface fracture distribution image, so as to perform fracture exploration based on the surface fracture distribution image.

[0159] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0160] 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.

[0161] 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.

[0162] 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 comprising 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.

[0163] 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 The steps for the functions specified in one or more boxes.

[0164] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0165] 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.

[0166] 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 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.

[0167] 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.

[0168] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0169] 1) The near-surface crack detection method of the present application comprises the following steps: first, obtaining common-offset seismic wavefield data of a surface area to be processed; then, constructing a frequency-space domain seismic data matrix by Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix; then, calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining the row value of each row and the column value of each column respectively; then, extracting multiple rows and multiple columns of the frequency-space domain seismic data matrix by a random selection method based on the row value and the column value, and obtaining target rows and target columns; then, reconstructing the target rows and the target columns based on a matrix transformation algorithm, and obtaining an approximate matrix of nonlinear signals in the common-offset seismic wavefield data, wherein the approximate matrix represents nonlinear wavefield data information formed by surface cracks; finally, performing home imaging on the approximate matrix by using a prestack depth migration algorithm, and generating a surface crack distribution image, so as to perform crack exploration according to the surface crack distribution image. The present application obtains high-frequency seismic data on the surface and represents it as a complex matrix in the frequency-space domain, i.e., a frequency-space domain seismic data matrix; extracts key columns and rows in the frequency-space domain seismic data matrix by a random selection method to construct a matrix; reconstructs the selected columns and rows based on a matrix transformation algorithm to obtain an approximate matrix; further optimizes the approximate matrix and the original high-frequency seismic data through iterative calculation technology, and finally generates a high-precision surface crack distribution profile, i.e., a surface crack distribution image. This method can effectively reduce noise interference and improve the accuracy of crack imaging, and is suitable for geological exploration, earthquake monitoring, and engineering safety assessment. The present application solves the problem that the prior art cannot effectively extract and reconstruct the nonlinear signal components of cracks in a complex geological environment.

[0170] 2) The near-surface crack detection device of the present application comprises an acquisition unit for acquiring the common-offset seismic wavefield data of the surface area to be processed; a construction unit for constructing a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix; a calculation unit for calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining the row value of each row and the column value of each column respectively; a selection and extraction unit for extracting multiple rows and multiple columns of the frequency-space domain seismic data matrix using a random selection method based on the row value and the column value, and obtaining target rows and target columns; a reconstruction unit for reconstructing the target rows and the target columns based on a matrix transformation algorithm, and obtaining an approximate matrix of nonlinear signals in the common-offset seismic wavefield data, wherein the approximate matrix represents nonlinear wavefield data information formed by surface cracks; an imaging unit for performing homing imaging on the approximate matrix using a prestack depth migration algorithm, and generating a surface crack distribution image, so as to perform crack exploration according to the surface crack distribution image. The present application obtains high-frequency seismic data on the surface and represents it as a complex matrix in the frequency-space domain, i.e., a frequency-space domain seismic data matrix; extracts key columns and rows in the frequency-space domain seismic data matrix by a random selection method to construct a matrix; reconstructs the selected columns and rows based on a matrix transformation algorithm to obtain an approximate matrix; further optimizes the approximate matrix and the original high-frequency seismic data through iterative calculation technology, and finally generates a high-precision surface crack distribution profile, i.e., a surface crack distribution image. This method can effectively reduce noise interference and improve the accuracy of crack imaging, and is suitable for geological exploration, earthquake monitoring, and engineering safety assessment. The present application solves the problem that the prior art cannot effectively extract and reconstruct the nonlinear signal components of cracks in a complex geological environment.

[0171] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A near-surface crack detection method, characterized in that: include: Acquire common offset seismic wavefield data of the surface area to be processed; Constructing a frequency-space domain seismic data matrix using Fourier transform according to the common offset seismic wave field data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix; Calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining a row value of each row and a column value of each column respectively; Based on the row value and the column value, a random selection method is used to extract multiple rows and multiple columns of the frequency space domain seismic data matrix to obtain target rows and target columns; Reconstructing the target row and the target column based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common offset seismic wave field data, wherein the approximate matrix represents nonlinear wave field data information formed by surface cracks; The approximate matrix is ​​imaged by using a prestack depth migration algorithm to generate a surface fracture distribution image, so as to perform fracture exploration according to the surface fracture distribution image.

2. The method according to claim 1, characterized in that Acquire common offset seismic wavefield data of the surface area to be processed, including: Exciting seismic waves on the surface of the surface area to be processed by using an artificial seismic source, wherein the artificial seismic source comprises at least explosives, an air gun or a vibrator; controlling a plurality of seismic detectors to receive seismic wave signals propagated by the seismic wave, the seismic detectors being deployed on the ground surface; Arrange the seismic wave signals received by all the seismic detectors according to a common offset, where the common offset refers to the distance from the midpoint between the seismic detector and the artificial seismic source to the seismic detector; All the seismic wave signals having the same common offset are combined to form the common offset seismic wave field data.

3. The method according to claim 1, characterized in that Calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining the row value of each row and the column value of each column respectively, including: The size of each row in the frequency-space domain seismic data matrix is ​​calculated according to the first formula to obtain the row value corresponding to each row. The first formula is: β m represents the element of the mth row in the frequency space domain seismic data matrix, λ m Represents the row value corresponding to the mth row; The size of each column in the frequency-space domain seismic data matrix is ​​calculated according to the second formula to obtain the column value corresponding to each column. The second formula is: β n represents the element of the nth column in the frequency space domain seismic data matrix, λ n Indicates the column value corresponding to the nth column.

4. The method according to claim 1, characterized in that: Based on the row value and the column value, a random selection method is used to extract multiple rows and multiple columns of the frequency space domain seismic data matrix to obtain target rows and target columns, including: A group of first elements are randomly selected in a selection interval according to the row value, the selection interval is an open interval from 0 to a maximum row value, the maximum row value is a maximum value among all the row values, and the first element is a value represented by any row in the frequency space domain seismic data matrix; In the case where the first element satisfies a first selection condition, determining the row where the first element is located as the target row, wherein the first selection condition is that the row value corresponding to the row where the first element is located is less than the product of a set coefficient and the maximum row value; A group of second elements are randomly selected in a selection interval according to the column value, the selection interval is an open interval from 0 to a maximum column value, the maximum column value is a maximum value among all the column values, and the second element is a value represented by any column in the frequency space domain seismic data matrix; When the second element satisfies a second selection condition, the column where the second element is located is determined as the target column, and the second selection condition is that the column value corresponding to the column where the second element is located is less than the product of the set coefficient and the maximum column value.

5. The method according to claim 1, characterized in that The target row and the target column are reconstructed based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common offset seismic wave field data, including: Performing matrix decomposition on the first matrix and the second matrix respectively to obtain a first basis vector matrix and a second basis vector matrix respectively, wherein the first matrix is ​​a matrix constructed by the target rows, and the second matrix is ​​a matrix constructed by the target columns; Multiplying the transpose of the second basis vector matrix, the frequency-space domain seismic data matrix, and the first basis vector matrix to obtain a third matrix; Performing singular value decomposition on the third matrix, and retaining the first k singular values ​​and the singular vectors corresponding to the singular values ​​to construct a low-rank matrix; The approximate value of the common-offset seismic wavefield data is reconstructed by the low-rank matrix to obtain the approximate matrix.

6. The method according to claim 5, characterized in that Reconstructing the approximate value of the common offset seismic wave field data by the low rank matrix to obtain the approximate matrix includes: The approximate matrix is ​​calculated based on the low-rank matrix and the common offset seismic wave field data in combination with a third formula, and the third formula is: Denotes the approximate matrix, Q m represents the first basis vector matrix, Q n Denotes the second basis vector matrix, B k represents the low-rank matrix, and D represents the common-offset seismic wavefield data.

7. The method according to claim 1, characterized in that The approximate matrix is ​​imaged using a prestack depth migration algorithm to generate a surface fracture distribution image, including: Constructing a medium model, wherein the medium model is used to describe the speed at which seismic waves propagate in different underground media; Calculating the ray path of the seismic wave from the artificial source to the seismic detector according to the medium model; Applying time migration to the approximate matrix according to the ray path to obtain nonlinear wavefield data after migration, wherein the time migration refers to adjusting the time axis of the nonlinear wavefield data according to the propagation time of the seismic wave to simulate the propagation delay of the seismic wave in the underground medium; The nonlinear wave field data after migration are spatially superimposed to generate the surface fracture distribution image.

8. A near-surface crack detection device, characterized in that: The device comprises: An acquisition unit, used for acquiring common offset seismic wave field data of a surface area to be processed; A construction unit, configured to construct a frequency-space domain seismic data matrix using Fourier transform according to the common-offset seismic wavefield data, wherein the frequency-space domain seismic data matrix is ​​in the form of a Hankel matrix; A calculation unit, used for calculating the size of each row and each column of the frequency-space domain seismic data matrix based on the L1 norm, and obtaining a row value of each row and a column value of each column respectively; A selection extraction unit is used to extract multiple rows and multiple columns of the frequency space domain seismic data matrix by a random selection method based on the row value and the column value to obtain target rows and target columns; A reconstruction unit, configured to reconstruct the target row and the target column based on a matrix transformation algorithm to obtain an approximate matrix of nonlinear signals in the common offset seismic wave field data, wherein the approximate matrix represents nonlinear wave field data information formed by surface cracks; An imaging unit is used to perform homing imaging on the approximate matrix using a prestack depth migration algorithm to generate a surface fracture distribution image, so as to perform fracture exploration according to the surface fracture distribution image.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images