High-speed seismic source seismic data wave field separation method and system using random cadzow filtering

Abstract

The application discloses a high-speed rail seismic data wave field separation method and system using random Cadzow filtering, and belongs to the technical field of exploration geophysics. The method comprises the following steps: collecting seismic data of high-speed rail passing time; estimating seismic wave propagation velocity through Radon transformation; correcting the time difference of the same direction wave field event to obtain aligned data; dividing the aligned data into blocks and randomly rearranging; performing Fourier transformation on the rearranged data and constructing a Hankel matrix; performing SVD decomposition and low-rank reconstruction on the Hankel matrix; reconstructing the frequency domain data block through inverse Hankel operation; performing inverse Fourier transformation and recovering the channel sequence; and extracting and averaging the central channel to obtain unidirectional wave field data. The system comprises corresponding functional modules. The application can effectively separate the mixed wave field of the high-speed rail seismic source, improve the data signal-to-noise ratio, and provide a reliable data basis for underground imaging.

Description

Technical Field

[0001] This invention belongs to the field of exploration geophysics technology, specifically relating to a method and system for wavefield separation of high-speed rail source seismic data using random Cadzow filtering. Background Technology

[0002] By the end of 2024, China's high-speed rail network had reached 48,000 kilometers. For underground exploration along high-speed rail lines, traditional seismic exploration methods often use seismic sources that are highly destructive, costly, and difficult to reuse. However, the seismic wavefield generated by the contact between the wheels and rails of a high-speed train during operation can carry rich information about underground structures and physical properties, making high-speed rail a promising new type of mobile combined seismic source. When using high-speed rail seismic sources for seismic exploration, conventional methods typically only use unidirectional wavefield data before the train arrives at the geophone array or after the train leaves the array. However, this type of data has limitations such as weak signal energy and a high signal-to-noise ratio, resulting in poor quality virtual shot gathers that are difficult to use directly for subsequent processing.

[0003] In contrast, while the data from when the train passes the geophone array has high energy and a high signal-to-noise ratio, it inevitably contains wave field interference excited by bridge piers in different directions relative to the geophone array. Therefore, by performing left and right traveling wave separation processing on the data from when the train passes, high signal-to-noise ratio unidirectional wave field data can be effectively extracted, thus providing a more reliable data foundation for constructing high-quality virtual shot gathers and subsequent high-precision underground imaging.

[0004] Currently available wavefield separation methods mainly include:

[0005] Existing technology 1: Performing matrix singular value decomposition for wavefield separation

[0006] Based on the correlation of wavefield data from different channels, a wavefield separation method using matrix singular value decomposition can be used.

[0007] The drawbacks of existing technology 1 are: singular value gaps, poor performance when the gaps are small, often failing to correctly describe and reconstruct highly correlated waves, losing useful waveforms and introducing artifacts.

[0008] Existing technology 2: Velocity filtering in the frequency-wavenumber domain (FK)

[0009] Since different types of waves have different apparent velocities, a common separation method is to perform velocity filtering in the frequency-wavenumber domain (FK). This involves applying a sector window filter to the FK domain data to separate wavefields with the target apparent velocity.

[0010] The disadvantage of prior art 2 is that when spatial sampling is insufficient, the aliasing phenomenon in the Fourier transform leads to the connection of wave field energies with different apparent velocities.

[0011] Existing technology 3: Wavefield separation method based on Radon transform

[0012] By integrating and projecting seismic data along a certain direction (mainly linear, parabolic, hyperbolic, etc.), the spatiotemporal domain data can be transformed into the τ-p domain, which helps to separate different wavefield components by utilizing the differences in slope and velocity.

[0013] The disadvantages of existing technology 3 are: the Radon transform operators are not orthogonal, the inverse transform cannot preserve amplitude information, and the inverse transform is inefficient in solving matrix equations. Summary of the Invention

[0014] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a method and system for wavefield separation of high-speed rail source seismic data using random Cadzow filtering. This invention uses seismic data acquired by a detector array under a high-speed rail viaduct and uses random Cadzow filtering to separate the mixed wavefield in the high-speed rail source seismic data. This solves the technical problem that the mixing of left and right traveling waves in high-speed rail source seismic data leads to poor virtual shot gather quality, low signal-to-noise ratio, and difficulty in directly using it for high-precision underground imaging. This provides a foundation for using high-signal-to-noise ratio high-speed rail source data for underground imaging or seismic exploration.

[0015] The present invention adopts the following technical solution:

[0016] A wavefield separation method for high-speed rail source seismic data using random Cadzow filtering includes the following steps:

[0017] Seismic data from a geophone array was collected when a high-speed train passed by, and seismic data containing the time period of the high-speed train's passage was extracted. ;

[0018] Regarding the earthquake data Perform Radon transform to estimate the propagation velocity of seismic waves;

[0019] Based on the seismic wave propagation velocity, time difference correction is performed on the same-direction wavefield in the seismic data to obtain aligned data. ;

[0020] For the alignment data The data is divided into blocks to obtain multiple data blocks. ;

[0021] Randomly rearrange each data block to obtain a randomly rearranged data block. ;

[0022] Perform a Fourier transform on each randomly rearranged data block, construct a frequency slice, and then construct the Hankel matrix. ;

[0023] For the Hankel matrix Perform singular value decomposition and retain the first k singular values ​​for low-rank approximation;

[0024] Perform an inverse Hankel operation on the low-rank approximated matrix to reconstruct it into a frequency domain data block;

[0025] The reconstructed frequency domain data block is subjected to inverse Fourier transform and the original channel order is restored to obtain the time domain data block after wavefield separation.

[0026] The center channel of the time-domain data block is extracted and averaged to obtain unidirectional wavefield data.

[0027] Preferably, seismic data for:

[0028]

[0029] in, Indicates the first The data received by each detector Indicates the first The first detector The value of each sampling point.

[0030] Preferably, the seismic data is transferred from the tx domain to... Radon transform in the -p domain for:

[0031]

[0032] in, This is the original earthquake record. For time intercept, Slow speed, For the first Detector position For detector spacing;

[0033] Acquiring earthquake data of -p domain data Then, energy is superimposed along the time intercept direction to construct the slow energy function. .

[0034] Preferably, the data is aligned. for:

[0035]

[0036] in, This is the maximum sampling point offset. Number the road, For the first The offset of the sampling point of the channel relative to the reference channel. This represents the number of sampling points.

[0037] Preferably, the data blocks are randomly rearranged. for:

[0038]

[0039] Where P is a random permutation matrix. This is window data.

[0040] Preferably, the Hankel matrix for:

[0041]

[0042] Among them, 0 <q<m, The design parameters for the Hankel matrix represent the number of rows. For frequency slices, This is the window width.

[0043] Preferably, a rank-1 approximation is achieved by retaining the signal component corresponding to the largest singular value, specifically as follows:

[0044]

[0045] in, This is the best rank-1 approximation matrix of the original data. Let F(S) be the largest singular value of matrix F(S). for , for Preferably, the inverse Hankel operation involves averaging the matrix along its anti-diagonal axis to recover the frequency domain vector.

[0046] Preferably, the time-domain data block after wavefield separation for:

[0047]

[0048] in, For randomly rearranged time-domain data, It is a random permutation matrix.

[0049] Secondly, embodiments of the present invention provide a wavefield separation system for high-speed rail source seismic data using random Cadzow filtering, comprising:

[0050] The data module collects seismic data from the geophone array when the high-speed train passes by, and extracts seismic data containing the time period of the high-speed train's passage. Regarding the earthquake data Perform Radon transform to estimate the propagation velocity of seismic waves;

[0051] The segmentation module performs time difference correction on the same-direction wavefield in the seismic data based on the seismic wave propagation velocity to obtain aligned data. For the aligned data The data is divided into blocks to obtain multiple data blocks. ;

[0052] The rearrangement module randomly rearranges each data block to obtain randomly rearranged data blocks. Perform a Fourier transform on each randomly rearranged data block to construct a frequency slice and then construct the Hankel matrix. ;

[0053] The reconstruction module reconstructs the Hankel matrix. Singular value decomposition is performed and the first k singular values ​​are retained for low-rank approximation. The matrix after low-rank approximation is then subjected to inverse Hankel operation to reconstruct a frequency domain data block.

[0054] The output module performs an inverse Fourier transform on the reconstructed frequency domain data block and restores the original channel order to obtain a time domain data block after wavefield separation; the time domain data block is then extracted and averaged to obtain unidirectional wavefield data.

[0055] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described method for separating the wavefield of high-speed rail source seismic data using random Cadzow filtering.

[0056] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described method for separating the wavefield of high-speed rail source seismic data using random Cadzow filtering.

[0057] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the above-described method for separating the wavefield of high-speed rail source seismic data using random Cadzow filtering.

[0058] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described method for separating the wavefield of high-speed rail source seismic data using random Cadzow filtering.

[0059] Compared with the prior art, the present invention has at least the following beneficial effects:

[0060] A wavefield separation method for high-speed rail source seismic data using stochastic Cadzow filtering is proposed. This method systematically achieves effective separation of left and right traveling waves through steps including Radon transform wave velocity estimation, time difference correction, data segmentation, stochastic rearrangement, Hankel matrix construction, low-rank approximation, and reconstruction. Combining the velocity estimation advantages of Radon transform with the anti-interference capabilities of Cadzow filtering, this method significantly improves the accuracy and signal-to-noise ratio of wavefield separation, making it suitable for the complex wavefield environment unique to high-speed rail sources.

[0061] Furthermore, the specific mathematical representation of the seismic data matrix was clarified, providing a clear data structure foundation for subsequent processing. The data format was standardized, facilitating computer implementation and algorithm standardization, thus enhancing the repeatability and practicality of the method.

[0062] Furthermore, the specific mathematical form of the Radon transform and the construction method of the slowness energy function were defined, providing a theoretical basis for wave velocity estimation. The bimodal distribution of the energy function clearly distinguishes between left and right traveling waves, improving the accuracy and reliability of velocity estimation.

[0063] Furthermore, the first and last channels are selected as reference channels for the left and right traveling waves respectively, which conforms to the wave field propagation law; zero-padding at the boundaries avoids data truncation and ensures the integrity of time difference correction; data translation operation makes the phase axis of the same wave field horizontally aligned, providing a regular data foundation for subsequent block processing and wave field separation, and improving the efficiency and accuracy of subsequent filtering processing.

[0064] Furthermore, reflection filling can effectively avoid boundary effects during data segmentation and reduce the loss of wave field information caused by edge truncation. By setting the window half-width and sliding step size for segmentation, localized data processing is achieved, which not only preserves the correlation of local wave fields but also reduces the complexity of overall data processing, facilitating the efficient execution of subsequent random rearrangement and matrix construction operations.

[0065] Furthermore, while maintaining the continuity of the horizontal phase axis of the target wavefield, the coherence of the interfering wavefield can be effectively disrupted. Because the coherence of the interfering wavefield is disrupted, it is easier to identify and remove in subsequent Hankel matrix construction and SVD decomposition, thereby improving the wavefield separation effect and solving the problem of effectively distinguishing between interfering and target waves in traditional methods.

[0066] Furthermore, the near-square Hankel matrix exhibits better numerical stability in singular value decomposition, which can improve the accuracy of the low-rank approximation. By recombining one-dimensional frequency slices into a two-dimensional matrix, the wave field separation problem is transformed into a low-rank matrix reconstruction problem, providing a suitable mathematical framework for achieving wave field separation using SVD decomposition.

[0067] Furthermore, based on the difference in singular values ​​between the target wavefield and the interfering wavefield, only the target wavefield component that contributes the most is retained. Compared to methods that retain multiple singular values, this method can more efficiently eliminate interfering wavefields and avoid introducing artifacts due to retaining too many singular values. The construction of the rank-1 approximation matrix simplifies the calculation process while ensuring that the main energy and waveform characteristics of the target wavefield are preserved, thus improving the efficiency and purity of wavefield separation.

[0068] Furthermore, accurately restoring the spatial channel order of the original data ensures that the processed data still maintains the spatial positional relationship with the actual detector array, avoiding spatial positioning errors in subsequent virtual shot collection construction and underground imaging due to channel order confusion, and guaranteeing the practicality of the output data of the technical solution.

[0069] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0070] In summary, this invention effectively separates the mixed wave field of high-speed rail seismic sources by standardizing data description, accurately estimating wave velocity, and standardizing data processing procedures. It solves the problems of difficult interference removal and low accuracy in traditional methods, improves the data signal-to-noise ratio, and provides reliable support for the construction of high-quality virtual shot sets and high-precision underground imaging.

[0071] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0072] Figure 1 This is a flowchart of the present invention;

[0073] Figure 2 This is a map showing seismic data received by the geophone array when the high-speed train passes by.

[0074] Figure 3 The epicenter of the high-speed rail earthquake Domain data graph;

[0075] Figure 4 Energy-slowness function graph of seismic waves excited by high-speed rail source;

[0076] Figure 5 This is a chart of right-aligned data after time-shift correction;

[0077] Figure 6 The images show the data obtained after Cadzow filtering, where (a) is the right traveling wave field data and (b) is the left traveling wave field data.

[0078] Figure 7The image is a virtual shot set, in which (a) is the original high-speed rail source seismic data, (b) is the right-traveling wave field data after wavefield separation, and (c) is the weak amplitude right-traveling wave seismic data after the train passes.

[0079] Figure 8 The following are dispersion spectra extracted using different virtual shot sets: (a) is the dispersion spectrum extracted using the original high-speed rail source seismic data, (b) is the dispersion spectrum extracted using the right traveling wave field data after wavefield separation, and (c) is the dispersion spectrum extracted using the weak amplitude right traveling wave seismic data after the train passes.

[0080] Figure 9 A schematic diagram of a computer device provided in an embodiment of the present invention;

[0081] Figure 10 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0082] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0083] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0084] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0085] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0086] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0087] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0088] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0089] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0090] This invention provides a wavefield separation method for high-speed rail source seismic data using random Cadzow filtering, providing a more reliable data foundation for constructing high-quality virtual shot gathers and subsequent high-precision subsurface imaging. First, the invention uses Radon transform to estimate wave velocity and performs time difference correction on seismic data during high-speed rail transit. Then, the corrected data is divided into blocks and randomly rearranged to construct a Hankel matrix for low-rank decomposition. Finally, high-quality unidirectional wavefield data is extracted through inverse transform and gather stacking.

[0091] Please see Figure 1 This invention discloses a wavefield separation method for high-speed rail source seismic data using random Cadzow filtering, comprising the following steps:

[0092] S1. Seismic data is collected by a geophone array deployed along the viaduct, and seismic data containing the time period when the high-speed train passes through is extracted.

[0093] Geophones are installed parallel to the high-speed railway viaduct, and seismic data of a specific high-speed train's location as it passes are extracted from the data received by the geophones. For data containing... Each detector has [number] detectors. Two-dimensional seismic records with a sampling number of points , represented as:

[0094] (1)

[0095] in, Indicates the first The data received by each detector Indicates the first The first detector The value of each sampling point.

[0096] S2. Use Radon transform to estimate the seismic wave velocity generated by the high-speed rail source;

[0097] By using Radon transform for tilt superposition, the wavefield components propagating along a specific direction are projected as... The energy accumulation points in the -p domain, based on the slowness corresponding to each energy point. The propagation velocity of the corresponding wavefield can then be calculated. Seismic data is transferred from the tx domain to... The Radon transform in the -p domain is defined as:

[0098] (2)

[0099] in, This is the original earthquake record. For time intercept, Slow speed, For the first Detector position For detector spacing.

[0100] Acquiring earthquake data of -p domain data Then, the energy is superimposed along the time intercept direction to construct the slow energy function:

[0101] (3)

[0102] For high-speed rail source earthquake data, this function exhibits a bimodal distribution, where... The region corresponds to a right-traveling wave. The region corresponds to a left-traveling wave, and the wave propagation velocity is obtained from the location of the energy extremum:

[0103] (4)

[0104] S3. Time difference correction is performed on the phase axis of the same-direction wavefield in the seismic data of the high-speed rail source.

[0105] In a uniformly distributed detector array, the channel spacing is set to... ,common Channel data, sampling frequency It is 500Hz.

[0106] Depending on the direction of wave field propagation, refer to the channel (the first one). The selection of the track follows these principles: the right-hand traveling wave field (the wave propagating along the direction of train travel) is taken from the first track ( The left-hand traveling wave field (the wave opposite to the direction of train travel) takes the last path ( ).

[0107] No. The waveform received by the channel detector relative to the reference channel (the first channel) Sampling point offset of (channel) for:

[0108] (5)

[0109] in, This refers to the propagation speed of seismic waves.

[0110] When performing translational time difference correction, to avoid data truncation during shifting, the original data needs to be extended to its boundaries. The maximum offset is then calculated. for:

[0111] (6)

[0112] For raw data Fill the upper and lower boundaries with zeros to construct an extended matrix. For the first Data, shifted downwards along the time direction Sample points

[0113] (7)

[0114] Time difference correction is applied to each data point to obtain aligned data with horizontal phase axes. .

[0115] S4. Divide the time-difference corrected high-speed rail mobile source seismic data into blocks;

[0116] Define window parameters, including the number of channels expanded on each side (i.e., the window half-width w) and the sliding step size s. To avoid boundary effects caused by truncation, reflective padding is applied to each side of the original data:

[0117] (8)

[0118] Where, m= , The reflection fill data are for the left and right sides, respectively. The data block for the i-th window is:

[0119] (9)

[0120] Among them, the central position Slide by step size s:

[0121] (10)

[0122] S5. Randomly rearrange the high-speed rail source seismic data with horizontal phase axes after segmentation.

[0123] For each window data The columns are randomly rearranged. This operation, while ensuring the continuity of the horizontal phase axis, effectively disrupts the coherence of interfering wavefield data, achieving better wavefield separation. The resulting random window is obtained after the random rearrangement. :

[0124] (11)

[0125] Where P is a random permutation matrix.

[0126] S6. Calculate the frequency slices of randomly rearranged high-speed rail source seismic data and construct the Hankel matrix;

[0127] Seismic records after random rearrangement Perform a Fourier transform along the time axis to convert the time-domain signal received by each detector into a frequency-domain representation.

[0128] Define the Fourier transform operator FX domain seismic records Represented as:

[0129] (12)

[0130] (13)

[0131] Frequency slices are provided , ,in, The width is the window width. The one-dimensional frequency slice is... By performing higher-order recombination, a two-dimensional Hankel matrix is ​​constructed.

[0132] (14)

[0133] Among them, 0 < q < m, take to make the constructed Hankel matrix closest to a square matrix.

[0134] S7. Perform singular value decomposition and reconstruction on the Hankel matrix to achieve low-rank approximation;

[0135] For the data in the f-x domain, the Hankel matrix formed by a certain fixed frequency The estimated effective rank number does not exceed the number of dips of the data , that is:

[0136] (15)

[0137] Therefore, singular value decomposition can be used to achieve the effect of wavefield separation by retaining the wavefield corresponding to the main eigenvalues. The specific steps include:

[0138] S701. Perform singular value decomposition on the Hankel matrix constructed for each frequency slice:

[0139] [[ID=3));

[0140] Among them, is a unitary matrix, is the conjugate transpose, is a diagonal matrix, and its diagonal elements are singular values, are the left and right singular vectors, r is the rank of the matrix. Due to the orthogonality between eigenvectors, the product of two eigenvectors can form a basis of the th weighted eigenimage , and its rank is 1.

[0141] S702. The role of the eigenimage in restoring is proportional to the size of the eigenvalue associated with the eigenimage. Therefore, the first eigenimages can be selected to perform rank reduction approximation on the input . The rank reduction approximation of the matrix and is expressed as:

[0142]

[0143] The input seismic record​The data exhibits horizontal phase axis characteristics. By segmenting and randomly shuffling the data, the coherence and phase axis of the interfering wavefield are disrupted. Therefore, by retaining only the signal component corresponding to the largest singular value, a rank-1 approximation matrix is ​​constructed to achieve a reduced-rank approximation.

[0144] (18)

[0145] S8. Reconstruct the frequency domain data block by performing an inverse Hankel operation on the matrix after low-rank approximation.

[0146] The matrix after low-rank approximation Generally, it does not satisfy the Hankel structure and requires modification. Anti-Hankel reconstruction is achieved by performing anti-diagonal averaging to recover the frequency domain vector. Define the anti-diagonal averaging operator. :

[0147] (19)

[0148] Each element The arithmetic mean of the anti-diagonal elements:

[0149] (20)

[0150] in, For the first The number of elements on each anti-diagonal line.

[0151] After reconstructing and combining all frequencies, the frequency slices of the high-speed rail source seismic data after wavefield separation are obtained. The details are as follows:

[0152] (twenty one)

[0153] S9. Perform an inverse Fourier transform to convert the frequency domain data to the time domain and restore the original channel sequence;

[0154] Frequency data of high-speed rail source earthquake data after wavefield separation Performing inverse Fourier transform column by column yields randomly rearranged time-domain data:

[0155] (twenty two)

[0156] The next step is to perform randomized recovery using a random permutation matrix. The inverse mapping restores the original spatial column order, yielding the final wavefield-separated time-domain data. :

[0157] (twenty three)

[0158] S10. Perform center channel extraction and averaging, and cyclically process to obtain unidirectional wavefield data.

[0159] For the time-domain block data (including the center channel and its adjacent channels) obtained by wave field separation, random noise is suppressed by accumulating the signals of adjacent channels to improve the signal-to-noise ratio of the center channel data.

[0160] For the central lane position :

[0161] (twenty four)

[0162] Among them, Gaussian weights The value decreases as the track spacing increases.

[0163] Repeat steps S5 to S9 for all data blocks to finally obtain the separated unidirectional wavefield data. With right-traveling wave field For example:

[0164] (25)

[0165] (26)

[0166] In another embodiment of the present invention, a wavefield separation system for high-speed rail source seismic data using random Cadzow filtering is provided. This system can be used to implement the above-mentioned wavefield separation method for high-speed rail source seismic data using random Cadzow filtering. Specifically, the wavefield separation system for high-speed rail source seismic data using random Cadzow filtering includes a data module, a block module, a rearrangement module, a reconstruction module, and an output module.

[0167] The data module collects seismic data from the geophone array when the high-speed train passes by, and extracts seismic data containing the time period during which the high-speed train passes. Regarding the earthquake data Perform Radon transform to estimate the propagation velocity of seismic waves;

[0168] The segmentation module performs time difference correction on the same-direction wavefield in the seismic data based on the seismic wave propagation velocity to obtain aligned data. For the aligned data The data is divided into blocks to obtain multiple data blocks. ;

[0169] The rearrangement module randomly rearranges each data block to obtain randomly rearranged data blocks. Perform a Fourier transform on each randomly rearranged data block to construct a frequency slice and then construct the Hankel matrix. ;

[0170] The reconstruction module reconstructs the Hankel matrix. Singular value decomposition is performed and the first k singular values ​​are retained for low-rank approximation. The matrix after low-rank approximation is then subjected to inverse Hankel operation to reconstruct a frequency domain data block.

[0171] The output module performs an inverse Fourier transform on the reconstructed frequency domain data block and restores the original channel order to obtain a time domain data block after wavefield separation; the time domain data block is then extracted and averaged to obtain unidirectional wavefield data.

[0172] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or function. The processor described in this embodiment can be used for the operation of a wavefield separation method for high-speed rail source seismic data using random Cadzow filtering, including:

[0173] Seismic data from a geophone array was collected when a high-speed train passed by, and seismic data containing the time period of the high-speed train's passage was extracted. Regarding the earthquake data Perform Radon transform to estimate seismic wave propagation velocity; then, based on this seismic wave propagation velocity, perform time difference correction on the co-directional wavefield in the seismic data to obtain aligned data. ; for the aligned data The data is divided into blocks to obtain multiple data blocks. Randomly rearrange each data block to obtain a randomly rearranged data block. Perform a Fourier transform on each randomly rearranged data block, construct a frequency slice, and then construct the Hankel matrix. ; for the Hankel matrix Singular value decomposition is performed, and the first k singular values ​​are retained for low-rank approximation. The matrix after low-rank approximation is subjected to inverse Hankel operation to reconstruct a frequency domain data block. The reconstructed frequency domain data block is subjected to inverse Fourier transform and the original channel order is restored to obtain a time domain data block after wavefield separation. The center channel is extracted and averaged from the time domain data block to obtain unidirectional wavefield data.

[0174] Please see Figure 9 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the method for estimating the concentration of radioactive iodine species in the containment vessel after an accident, as described in this embodiment. To avoid repetition, this will not be elaborated upon here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the high-speed rail source seismic data wavefield separation system using random Cadzow filtering, as described in this embodiment. To avoid repetition, this will not be elaborated upon here.

[0175] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 9 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0176] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0177] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device 60.

[0178] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0179] Please see Figure 10 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0180] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0181] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0182] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0183] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0184] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0185] Example 4

[0186] This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0187] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0188] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0189] One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the wavefield separation method for high-speed rail source seismic data using random Cadzow filtering in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps:

[0190] Seismic data from a geophone array was collected when a high-speed train passed by, and seismic data containing the time period of the high-speed train's passage was extracted. Regarding the earthquake data Perform Radon transform to estimate seismic wave propagation velocity; then, based on this seismic wave propagation velocity, perform time difference correction on the co-directional wavefield in the seismic data to obtain aligned data. ; for the aligned data The data is divided into blocks to obtain multiple data blocks. Randomly rearrange each data block to obtain a randomly rearranged data block. Perform a Fourier transform on each randomly rearranged data block, construct a frequency slice, and then construct the Hankel matrix. ; for the Hankel matrix Singular value decomposition is performed, and the first k singular values ​​are retained for low-rank approximation. The matrix after low-rank approximation is subjected to inverse Hankel operation to reconstruct a frequency domain data block. The reconstructed frequency domain data block is subjected to inverse Fourier transform and the original channel order is restored to obtain a time domain data block after wavefield separation. The center channel is extracted and averaged from the time domain data block to obtain unidirectional wavefield data.

[0191] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0192] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0193] Taking the data reception of a detector array arranged at 1.38m intervals directly below the bridge pier as an example.

[0194] Please see Figure 2 This data represents seismic data received by a geophone array as a high-speed train passes by. The sampling frequency is 500Hz, and there are 17 geophones. The data captures vibrations as the high-speed train passes by.

[0195] Figure 3 and Figure 4 These are the results obtained when estimating the seismic wave velocity generated by the high-speed rail source using the Radon transform. Domain data and energy-slowness function, where the peak value of the function corresponds to a slowness of 0.00624 s / m.

[0196] Figure 5The right-aligned data after time-shift correction is shown in the figure. The horizontal axis represents the detector distance (in meters), and the vertical axis represents time (in seconds). Based on the right-traveling wave propagation velocity estimated by Radon transform, the sampling point offset of each detector channel relative to the reference channel is calculated using the first channel as the reference channel. After zero-padding the original data at the boundaries, the data of each channel is translated and corrected along the time axis. The phase axis of the right-traveling wave in the figure is now horizontal, indicating that the time-shift correction effect is good, eliminating the time delay caused by the difference in wave field propagation distance, and providing a regular and effective data foundation for subsequent wave field separation processing steps such as data segmentation, random rearrangement, and Cadzow filtering.

[0197] Figure 6 The images show the right and left traveling wave field data obtained after Cadzow filtering. It can be seen that the right traveling wave field contains good low-frequency and high-frequency details simultaneously. The wave field separation effect is verified by combining the virtual shot set and dispersion spectrum.

[0198] Figure 7 The virtual shot gathers are constructed using the original high-speed rail source seismic data, the right-traveling wave field data after wavefield separation, and the weak-amplitude right-traveling wave seismic data after the train passes, respectively. The mixing of left and right traveling waves in the original data severely affects the quality of the virtual shot gathers. The virtual shot gathers constructed by this method are of high quality, while the virtual shot gathers constructed from the data after the train passes are of poor quality due to the weak effective signal energy and the influence of noise.

[0199] Figure 8 For use Figure 7 The dispersion spectrum plotted from the virtual shot set data shown in this paper demonstrates the high reliability of the dispersion spectrum plotted from the data obtained using this method.

[0200] In summary, this invention provides a wavefield separation method and system for high-speed rail source seismic data using random Cadzow filtering. This method separates the mixed left and right traveling waves in high-speed rail source seismic data, solving the problem of multi-directional wavefield interference and providing a data foundation for subsequent seismic exploration using high signal-to-noise ratio high-speed rail source data. First, this invention uses Radon transform to estimate wave velocity and performs time difference correction on seismic data during high-speed rail transit. Then, the corrected data is divided into blocks and randomly rearranged to construct a Hankel matrix for low-rank decomposition. Finally, high-quality unidirectional wavefield data is extracted through inverse transform and gather stacking. Compared to conventional wavefield separation methods, the separation results of this invention better preserve the effective signal amplitude, more effectively separate unidirectional wavefields, and suppress noise interference. The separation results can be directly used for high-quality virtual shot gather construction and subsequent processing.

[0201] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0202] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0203] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0204] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0205] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0206] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0207] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random-access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0208] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0209] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0210] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0211] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A wavefield separation method for high-speed rail source seismic data using random Cadzow filtering, characterized in that, Includes the following steps: Seismic data from a geophone array was collected when a high-speed train passed by, and seismic data containing the time period of the high-speed train's passage was extracted. ; Regarding the earthquake data Perform Radon transform to estimate the propagation velocity of seismic waves; Based on the seismic wave propagation velocity, time difference correction is performed on the same-direction wavefield in the seismic data to obtain aligned data. ; For the alignment data The data is divided into blocks to obtain multiple data blocks. ; Randomly rearrange each data block to obtain a randomly rearranged data block. ; Perform a Fourier transform on each randomly rearranged data block, construct a frequency slice, and then construct the Hankel matrix. ; For the Hankel matrix Perform singular value decomposition and retain the first k singular values ​​for low-rank approximation; Perform an inverse Hankel operation on the low-rank approximated matrix to reconstruct it into a frequency domain data block; The reconstructed frequency domain data block is subjected to inverse Fourier transform and the original channel order is restored to obtain the time domain data block after wavefield separation. The center channel of the time-domain data block is extracted and averaged to obtain unidirectional wavefield data.

2. The wavefield separation method for high-speed rail source seismic data using random Cadzow filtering according to claim 1, characterized in that, earthquake data for: in, The number of sampling points. This represents the total number of channels in the detector array. Indicates the first The first detector The value of each sampling point.

3. The method for wavefield separation of high-speed rail source seismic data using random Cadzow filtering according to claim 1, characterized in that, earthquake data from -x domain to Radon transform in the -p domain for: in, This is the original earthquake record. For time intercept, Slow speed, For the first Detector position For detector spacing, This represents the total number of channels in the detector array; Acquiring earthquake data of After the -p domain data, energy is superimposed along the time intercept direction to construct the slow energy function. .

4. The method for wavefield separation of high-speed rail source seismic data using random Cadzow filtering according to claim 1, characterized in that, Randomly rearrange data blocks for: Where P is a random permutation matrix. This is window data.

5. The method for wavefield separation of high-speed rail source seismic data using random Cadzow filtering according to claim 1, characterized in that, Hankel matrix for: Among them, 0 <q<m, The design parameters for the Hankel matrix represent the number of rows. For frequency slices, This is the window width.

6. The method for wavefield separation of high-speed rail source seismic data using random Cadzow filtering according to claim 1, characterized in that, By retaining the signal component corresponding to the largest singular value, a rank-1 approximation matrix is ​​constructed to achieve rank reduction approximation, specifically as follows: in, This is the best rank-1 approximation matrix of the original data. Let F(S) be the largest singular value. for The corresponding left singular vector, for The corresponding right singular vector.

7. The method for wavefield separation of high-speed rail source seismic data using random Cadzow filtering according to claim 1, characterized in that, The inverse Hankel operation is to perform an anti-diagonal averaging of the matrix to recover the frequency domain vector.

8. The method for wavefield separation of high-speed rail source seismic data using random Cadzow filtering according to claim 1, characterized in that, The time-domain data block after wavefield separation is as follows: in, For randomly rearranged time-domain data, It is a random permutation matrix.

9. A wavefield separation system for high-speed rail source seismic data using random Cadzow filtering, characterized in that, include: The data module collects seismic data from the geophone array when the high-speed train passes by, and extracts seismic data containing the time period of the high-speed train's passage. Regarding the earthquake data Perform Radon transform to estimate the propagation velocity of seismic waves; The segmentation module performs time difference correction on the same-direction wavefield in the seismic data based on the seismic wave propagation velocity to obtain aligned data. For the aligned data The data is divided into blocks to obtain multiple data blocks. ; The rearrangement module randomly rearranges each data block to obtain randomly rearranged data blocks. Perform a Fourier transform on each randomly rearranged data block to construct a frequency slice and then construct the Hankel matrix. ; The reconstruction module reconstructs the Hankel matrix. Singular value decomposition is performed and the first k singular values ​​are retained for low-rank approximation. The matrix after low-rank approximation is then subjected to inverse Hankel operation to reconstruct a frequency domain data block. The output module performs an inverse Fourier transform on the reconstructed frequency domain data block and restores the original channel order to obtain a time domain data block after wavefield separation; the time domain data block is then extracted and averaged to obtain unidirectional wavefield data.

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