A method and system for compressing and reconstructing borehole elastic waves based on piecewise windowing and sparse coding

Abstract

This application discloses a method and system for compressing and reconstructing borehole elastic wave signals based on segmented windowing and sparse coding, relating to the field of geophysical signal processing. The method includes segmenting the original borehole elastic wave time-series signal into multiple signal segments; applying a combined Hann window function to each signal segment for windowing and selecting effective signal segments; obtaining the sparse coefficient vector and atom index corresponding to each effective signal segment based on a pre-built dictionary; calculating the average sparse coefficient vector of all effective signal segments to obtain the average sparsity; reconstructing any effective signal segment according to the average sparsity to obtain a reconstructed effective signal segment; and performing inverse weighting processing using the Hann window function corresponding to the reconstructed effective signal segment to obtain the final reconstructed signal. This application achieves efficient compression and high-fidelity reconstruction of borehole elastic wave time-series signals.

Description

Technical Field

[0001] This application relates to the field of geophysical signal processing, and in particular to a borehole elastic wave compression reconstruction method and system based on segmented windowing and sparse coding. Background Technology

[0002] Elastic wave exploration is a key technology for obtaining information on underground geological structures and mineral resources. During borehole elastic wave data acquisition, especially in drilling exploration scenarios, a large amount of elastic wave data is generated. This data is characterized by high sampling rates, multiple channels, and long-term continuous recording, resulting in a massive data volume that is difficult to store, transmit, and post-process in engineering practice. Therefore, it is essential to efficiently compress and effectively reconstruct the raw elastic wave data.

[0003] Currently, compression and reconstruction methods for borehole elastic wave data mainly include: traditional compression methods, such as wavelet transform-based compression methods. This method first performs wavelet analysis on the input image to extract sparsity across different frequency bands. Then, it compresses the wavelet coefficients through thresholding and quantization operations. Huffman coding is used to further reduce data redundancy. Finally, the compressed image is reconstructed through Huffman decoding and inverse wavelet transform. However, its compression efficiency and reconstruction quality heavily rely on pre-defined fixed basis functions, making it difficult to adaptively match the complex waveform characteristics of elastic wave signals. Furthermore, it is prone to waveform distortion at low bit rates.

[0004] Predictive coding methods: The core of predictive coding methods is to compress signals by utilizing the correlation between preceding and subsequent signals, with adaptive differential pulse code modulation being a typical example. This method first predicts the value of the next sample based on the already encoded signal samples, and then quantizes and encodes only the tiny difference between the true and predicted values. Since the dynamic range of the residual is much smaller than that of the original signal, it can be represented with fewer bits, thus achieving compression. Adaptive mechanisms can adjust the quantization step size according to signal changes to optimize performance, but they heavily rely on the short-time stationarity and linearity of the signal. However, borehole elastic wave signals are affected by complex formations and have strong non-stationary and nonlinear characteristics. Simple linear prediction models often fail, leading to increased residuals and a sharp drop in compression efficiency. While using high-order complex models may improve accuracy, the computational load is too large to meet the requirements of downhole processing.

[0005] Sparse coding methods, such as compression methods based on fixed sparse dictionaries, first construct a general and complete dictionary. At the compression end, each signal is sparsely encoded using this dictionary, and the sparseness and dictionary index are transmitted or stored. At the decompression end, the signal is reconstructed using the same dictionary and the received coefficients. The main problems and drawbacks of this technique are that its performance is highly dependent on the universality of the pre-constructed dictionary. Borehole elastic wave data varies greatly with geological conditions, and a fixed, general dictionary is unlikely to achieve optimal sparse representation in all cases. For signals with structural characteristics significantly different from the dictionary atoms, the reconstruction error is large at the same compression ratio, or the compression ratio cannot be further improved within a specified reconstruction error range. Summary of the Invention

[0006] The purpose of this application is to provide a method and system for compressing and reconstructing borehole elastic waves based on segmented windowing and sparse coding, which realizes efficient compression and high-fidelity reconstruction of borehole elastic wave time-series signals.

[0007] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a borehole elastic wave compression reconstruction method based on segmented windowing and sparse coding, including: The original borehole elastic wave time sequence signal is segmented to obtain multiple signal segments; A combined Hann window function is used to window each signal segment and filter out the effective signal segments; the combined Hann window function is composed of a weighted fusion of a full-length Hann window and a central local Hann window; Based on a pre-built dictionary, the orthogonal matching pursuit algorithm is used to solve the sparse representation for each valid signal segment, and the corresponding sparse coefficient vector and atom index are obtained. Based on the sparse coefficient vector corresponding to each valid signal segment, the average sparse coefficient vector of all valid signal segments is calculated to obtain the average sparsity. For any valid signal segment, based on the average sparsity, the valid signal segment is reconstructed according to the sparse coefficient vector and atom index corresponding to the valid signal segment to obtain the reconstructed valid signal segment. The reconstructed valid signal segment is then subjected to inverse weighting using the Hann window function corresponding to the reconstructed valid signal segment to obtain the processed reconstructed valid signal segment. Based on each processed reconstructed valid signal segment, the segments are superimposed according to their starting positions in the original borehole elastic wave time series signal to obtain the final reconstructed signal.

[0008] Secondly, this application provides a borehole elastic wave compression and reconstruction system based on segmented windowing and sparse coding, comprising: The signal segment acquisition module is used to segment the original borehole elastic wave time sequence signal to obtain multiple signal segments; The windowing and filtering module is used to perform windowing processing on each signal segment using a combined Hann window function and to filter out valid signal segments; the combined Hann window function is composed of a weighted fusion of a full-length Hann window and a central local Hann window; The sparse module is used to solve the sparse representation of each valid signal segment based on a pre-built dictionary using the orthogonal matching pursuit algorithm, and obtain the corresponding sparse coefficient vector and atom index; The average sparsity acquisition module is used to calculate the average value of the sparse coefficient vectors of all valid signal segments based on the sparse coefficient vector corresponding to each valid signal segment, and obtain the average sparsity. The reconstructed effective signal acquisition module is used to reconstruct any effective signal segment based on the average sparsity, the sparse coefficient vector and the atom index corresponding to the effective signal segment, and to obtain the reconstructed effective signal segment. The module then performs inverse weighting processing using the Hann window function corresponding to the reconstructed effective signal segment to obtain the processed reconstructed effective signal segment. The final reconstructed signal acquisition module is used to superimpose each processed reconstructed valid signal segment according to its starting position in the original borehole elastic wave time sequence signal to obtain the final reconstructed signal.

[0009] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method and system for compressing and reconstructing borehole elastic wave data based on segmented windowing and sparse coding. It combines Hann window weighted fusion and energy thresholding to select effective segments, then adaptively determines the average sparsity based on an orthogonal matching pursuit algorithm to achieve intelligent compression. Finally, it ensures seamless and smooth reconstruction of the signal through inverse weighting within the same window and overlap normalization stitching. This application achieves efficient compression and high-fidelity reconstruction of borehole elastic wave data, significantly improving the processing efficiency and application value of borehole elastic wave data. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating a borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding, provided as an embodiment of this application; Figure 2A 4×4 dictionary atom diagram of a borehole elastic wave compression reconstruction method based on segmented windowing and sparse coding provided in an embodiment of this application; Figure 3 This is a schematic diagram comparing the original borehole elastic wave timing signal with the final reconstructed signal. Figure 4 A schematic diagram of the functional modules of a borehole elastic wave compression and reconstruction system based on segmented windowing and sparse coding, provided for another embodiment of this application; Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] To make the objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] In one exemplary embodiment, such as Figure 1 As shown, a borehole elastic wave compression reconstruction method based on segmented windowing and sparse coding is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method includes steps 101 to 106. Wherein: Step 101: The original borehole elastic wave time sequence signal is segmented to obtain multiple signal segments.

[0015] Step 102: Apply a combined Hann window function to each signal segment and filter out the valid signal segments; the combined Hann window function is composed of a weighted fusion of a full-length Hann window and a central local Hann window.

[0016] Step 103: Based on the pre-built dictionary, the orthogonal matching pursuit algorithm is used to solve the sparse representation for each valid signal segment, and the corresponding sparse coefficient vector and atom index are obtained. For example... Figure 2 The diagram shown is a schematic of a 4×4 dictionary atom.

[0017] Step 104: Based on the sparse coefficient vector corresponding to each valid signal segment, calculate the average value of the sparse coefficient vectors of all valid signal segments to obtain the average sparsity.

[0018] Step 105: For any valid signal segment, based on the average sparsity and the sparse coefficient vector and atom index corresponding to the valid signal segment, the valid signal segment is reconstructed to obtain a reconstructed valid signal segment. The reconstructed valid signal segment is then subjected to inverse weighting using the Hann window function corresponding to the reconstructed valid signal segment to obtain the processed reconstructed valid signal segment.

[0019] Step 106: Based on each processed reconstructed valid signal segment, superimpose them according to their starting positions in the original borehole elastic wave time sequence signal to obtain the final reconstructed signal.

[0020] In another exemplary embodiment of this application, the first improvement is a multi-scale segmented windowing strategy based on energy criteria, as shown below.

[0021] Step 101 specifically includes: using the formula The original borehole elastic wave time sequence signal is subjected to overlap segmentation processing to obtain multiple signal segments of equal length with a preset overlap rate between adjacent segments; among them, The preset signal segment length is for the original borehole elastic wave timing signal. Overlap rate is the ratio of the overlapping portion of adjacent signal segments to the complete signal segment. The step size is the interval between each signal segment; To ensure rounding down, It is an integer; it is guaranteed that... The integer represents the original borehole elastic wave timing signal. The length is .

[0022] Generate the first The sample index range corresponding to each signal segment is: .

[0023] in, For the first The index of the starting position of each signal segment. For the first The index of the end position of a signal segment. If there are insufficient remaining samples at the end... Then, zero-padding is applied to the signal segment to make it reach the required length. The padding here ensures that each signal segment has a consistent length, facilitating subsequent matrix operations and dictionary matching.

[0024] Step 102 specifically includes: applying a combined Hann window function to each signal segment, calculating the local energy of each windowed signal segment, and selecting valid signal segments based on a preset energy threshold. The purpose of windowing is to reduce spectral leakage caused by abrupt changes at the signal segment edges and to achieve a smooth transition during the overlapping and splicing stage. To improve the adaptability to borehole elastic waves of different frequency bands, two Hann window functions are used: a full-length Hann window and a center Hann window. The full-length Hann window enhances the smoothness of signal segment boundaries, while the center Hann window emphasizes the local internal features of the signal segment. In step 102, the following formula is used to express the... A windowed signal segment: .

[0025] in, For full-length Hann windows, A local Hann window centered on the center. For the first A windowed signal segment The weights are for the full-length Hann window. The weights of the local Hann window at the center. For element-wise multiplication, For the first A signal segment, This is the index of the signal segment.

[0026] Subsequently, the calculation of the first A windowed signal segment The local energy, i.e., the standard deviation And the energy threshold can be set. Execute the Validity determination of the first windowed signal segment: A windowed signal segment Only when Only then does it participate in sparse coding and signal reconstruction. This mechanism ensures that the dictionary can better represent the real and effective bands, and the sparse representation structure is more stable.

[0027] In another exemplary embodiment of this application, the second improvement is: an adaptive sparsity selection mechanism, as detailed below.

[0028] In step 104, instead of using a uniform fixed average sparsity, the number of non-zero values ​​in the sparse coefficient vector corresponding to all valid signal segments is counted: .in, For the first Sparsity corresponding to each valid signal segment For the first A sparse coefficient vector corresponding to each valid signal segment.

[0029] Calculate the average of the sparse coefficient vectors of all valid signal segments and round down: .in, Round down to the nearest integer to ensure It is an integer. The average sparsity of the effective signal segments is used as the uniform sparsity in the compression stage. This represents the total number of valid signal segments. The final compression process uses... .

[0030] This mechanism takes into account the different waveform sparsity at different locations in the formation, and the fixed sparsity is not suitable. In this application, the sparsity used during compression is automatically determined according to the sparsity characteristics of the data itself, so that the optimal trade-off between the compression ratio and the reconstruction fidelity is automatically achieved, avoiding manual parameter tuning and improving the generalization under different formation conditions.

[0031] In another exemplary embodiment of this application, the third improvement is: fragment reconstruction based on Hann window inverse weighting, as detailed below.

[0032] In step 105, based on the average sparsity, based on the first... The sparse coefficient vector and atom index corresponding to the effective signal segment, for the th effective signal segment The effective signal segment is reconstructed, and the following formula is used to represent the first effective signal segment. One reconstructed valid signal segment: .

[0033] in, For the first The first reconstructed valid signal segment One sample point, This is the sparse coefficient vector corresponding to the effective signal segment. For the first The first reconstructed valid signal segment corresponding to the first One non-zero sparse coefficient, For the first in the pre-built dictionary The first atom One sample point, This is a preset signal segment length for the original borehole elastic wave time series signal. The function of this formula is to linearly combine dictionary atoms according to sparse coefficients to achieve an approximate reconstruction of the effective signal segment.

[0034] In another exemplary embodiment of this application, in step 105, the reverse weighting process for the reconstructed valid signal segment is represented by the following formula: .

[0035] in, For element-wise multiplication, The first after windowing The first reconstructed valid signal segment One sample point, For the first The first reconstructed valid signal segment One sample point, For the full-length Hann window, the first Each weight, This is a preset signal segment length for the original borehole elastic wave timing signal. This method ensures that the reconstructed effective signal segments have the same amplitude envelope during the splicing process, avoiding the obvious splicing edge problem common in ordinary overlap-add methods.

[0036] In another exemplary embodiment of this application, step 106 specifically includes steps 201 to 202. Wherein: Step 201: All processed reconstructed valid signal segments are superimposed according to their starting positions in the original borehole elastic wave time series signal to obtain a global signal array, and the window function weights at the corresponding positions are accumulated.

[0037] Step 202: Based on the window function weights at the corresponding positions and the global signal array, normalize the global signal array according to position to obtain the final reconstructed signal, such as... Figure 3 As shown, this is a schematic diagram comparing the original borehole elastic wave timing signal with the final reconstructed signal.

[0038] In another exemplary embodiment of this application, in step 201, the processed reconstructed valid signal segments are superimposed in the global signal array according to their original segment positions, while maintaining a cumulative window weight array, and the window function weights of the corresponding positions in the global signal array and the cumulative array are expressed by the following formula: .

[0039] .

[0040] in, For the m-th sample in the global signal array, The cumulative window function weights at corresponding positions are the first... One sample point, To cover samples The set of indices of all processed reconstructed valid signal segments. For the first The starting index of the reconstructed effective signal segment in the global signal array after windowing processing. The first one after adding a window One reconstructed valid signal segment, The weights of the sample points corresponding to the window function.

[0041] In another exemplary embodiment of this application, in step 202, the accumulated global signal array is normalized to eliminate the influence of amplitude superposition in overlapping regions, and the normalization process is expressed by the following formula: .

[0042] in, For the final reconstructed signal, For the m-th sample in the global signal array, The cumulative window function weights at corresponding positions are the first... Each sample point is used. All processed and reconstructed valid signal segments are superimposed, and the window function weights at corresponding locations are accumulated to achieve seamless and smooth reconstruction in overlapping regions through normalization.

[0043] This application first preprocesses the original borehole elastic wave timing signal, and sets the signal segment length according to the borehole elastic wave timing signal to be processed. overlap rate Full-length Hann window, central local Hann window, threshold dictionary matrix sparse reconstruction target sparsity Orthogonal matching pursuit algorithm parameters, initialization of the cumulative global signal array and cumulative window weight array This provides the basic variables for subsequent algorithms.

[0044] Secondly, through overlap rate and signal segment length Calculate step size For the original borehole elastic wave timing signal according to The signal is extracted by sliding, and then multi-scale windowing is applied to each signal segment to obtain the windowed signal segment. Calculation after adding a window Local energy, i.e., standard deviation And thresholds can be set. Valid signal segments were obtained by performing validity checks. .

[0045] Furthermore, for each valid signal segment The optimal sparse representation is obtained by using the orthogonal matching pursuit algorithm to solve for the sparse coefficients. Dictionary atomic index .

[0046] Finally, the sparsity returned by the orthogonal matching pursuit algorithm is used to reconstruct each valid signal segment, resulting in... This is a segment reconstructed from an effective signal.

[0047] Based on the same inventive concept, this application also provides a borehole elastic wave compression and reconstruction system based on segmented windowing and sparse coding for implementing the above-mentioned borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the borehole elastic wave compression and reconstruction system based on segmented windowing and sparse coding provided below can be found in the limitations of the borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding described above, and will not be repeated here.

[0048] In one exemplary embodiment, such as Figure 4 As shown, a borehole elastic wave compression and reconstruction system based on segmented windowing and sparse coding is provided, comprising: The signal segment acquisition module 401 is used to segment the original borehole elastic wave timing signal to obtain multiple signal segments.

[0049] The windowing and filtering module 402 is used to perform windowing processing on each signal segment using a combined Hann window function and to filter out valid signal segments; the combined Hann window function is composed of a weighted fusion of a full-length Hann window and a central local Hann window.

[0050] The sparse module 403 is used to solve the sparse representation of each valid signal segment based on a pre-built dictionary using the orthogonal matching pursuit algorithm, and obtain the corresponding sparse coefficient vector and atom index.

[0051] The average sparsity acquisition module 404 is used to calculate the average value of the sparse coefficient vectors of all valid signal segments based on the sparse coefficient vector corresponding to each valid signal segment, so as to obtain the average sparsity.

[0052] The reconstructed valid signal acquisition module 405 is used to reconstruct any valid signal segment based on the average sparsity, the sparse coefficient vector and the atom index corresponding to the valid signal segment, to obtain a reconstructed valid signal segment, and to perform inverse weighting processing using the Hann window function corresponding to the reconstructed valid signal segment to obtain a processed reconstructed valid signal segment.

[0053] The final reconstructed signal acquisition module 406 is used to superimpose each processed reconstructed valid signal segment according to its starting position in the original borehole elastic wave timing signal to obtain the final reconstructed signal.

[0054] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 5 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores the raw borehole elastic wave timing signals. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding.

[0055] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0056] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A borehole elastic wave compression and reconstruction method based on piecewise windowing and sparse coding, characterized in that, The method includes: The original borehole elastic wave time sequence signal is segmented to obtain multiple signal segments; A combined Hann window function is used to window each signal segment and filter out the effective signal segments; the combined Hann window function is composed of a weighted fusion of a full-length Hann window and a central local Hann window; Based on a pre-built dictionary, the orthogonal matching pursuit algorithm is used to solve the sparse representation for each valid signal segment, and the corresponding sparse coefficient vector and atom index are obtained. Based on the sparse coefficient vector corresponding to each valid signal segment, the average sparse coefficient vector of all valid signal segments is calculated to obtain the average sparsity. For any valid signal segment, based on the average sparsity, the valid signal segment is reconstructed according to the sparse coefficient vector and atom index corresponding to the valid signal segment to obtain the reconstructed valid signal segment. The reconstructed valid signal segment is then subjected to inverse weighting using the Hann window function corresponding to the reconstructed valid signal segment to obtain the processed reconstructed valid signal segment. Based on each processed reconstructed valid signal segment, the segments are superimposed according to their starting positions in the original borehole elastic wave time series signal to obtain the final reconstructed signal.

2. The borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding according to claim 1, characterized in that, The original borehole elastic wave time sequence signal is segmented to obtain multiple signal segments, specifically including: Using formula The original borehole elastic wave time sequence signal is subjected to overlap segmentation processing to obtain multiple signal segments of equal length with a preset overlap rate between adjacent segments; among them, The preset signal segment length is for the original borehole elastic wave timing signal. The overlap rate, Step size, To ensure rounding down, It is an integer.

3. The borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding according to claim 1, characterized in that, A combined Hann window function is used to window each signal segment, and valid signal segments are selected. Specifically, this includes: A combined Hann window function is used to window each signal segment, and the local energy of each windowed signal segment is calculated. Valid signal segments are then selected based on a preset energy threshold.

4. The borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding according to claim 3, characterized in that, The following formula is used to represent the first... A windowed signal segment: ； in, For full-length Hann windows, A local Hann window centered on the center. For the first A windowed signal segment The weights are for the full-length Hann window. The weights of the local Hann window at the center. For element-wise multiplication, For the first A signal segment, Step size, The preset signal segment length is for the original borehole elastic wave timing signal. This is the index of the signal segment.

5. The borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding according to claim 1, characterized in that, The following formula is used to represent the first... One reconstructed valid signal segment: ； in, For the first The first reconstructed valid signal segment One sample point, This is the sparse coefficient vector corresponding to the effective signal segment. For the first The first reconstructed valid signal segment corresponding to the first One non-zero sparse coefficient, For the first in the pre-built dictionary The atom of the first atom One sample point, The preset signal segment length for the original borehole elastic wave timing signal.

6. The borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding according to claim 1, characterized in that, The following formula represents the inverse weighting process for reconstructing valid signal segments: ； in, For element-wise multiplication, The first one after adding a window The first reconstructed valid signal segment One sample point, For the first The first reconstructed valid signal segment One sample point, For the full-length Hann window, the first Each weight, The preset signal segment length for the original borehole elastic wave timing signal.

7. The borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding according to claim 1, characterized in that, Based on each processed and reconstructed valid signal segment, they are superimposed according to their starting positions in the original borehole elastic wave time series signal to obtain the final reconstructed signal, which specifically includes: All processed reconstructed valid signal segments are superimposed according to their starting positions in the original borehole elastic wave time series signal to obtain a global signal array, and the window function weights at the corresponding positions are accumulated. Based on the window function weights at the corresponding positions and the global signal array, the global signal array is normalized according to position to obtain the final reconstructed signal.

8. The borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding according to claim 7, characterized in that, The following formula represents the window function weights at corresponding positions in the global signal array and the accumulated values: ； ； in, For the m-th sample in the global signal array, The cumulative window function weights at corresponding positions are the first... One sample point, To cover samples The set of indices of all processed reconstructed valid signal segments. For the first The starting index of the reconstructed effective signal segment in the global signal array after windowing processing. The first one after adding a window One reconstructed valid signal segment, The weights of the sample points corresponding to the window function.

9. The borehole elastic wave compression and reconstruction method based on segmented windowing and sparse coding according to claim 7, characterized in that, The normalization process is represented by the following formula: ； in, For the final reconstructed signal, For the m-th sample in the global signal array, The cumulative window function weights at corresponding positions are the first... 1 sample point.

10. A borehole elastic wave compression and reconstruction system based on piecewise windowing and sparse coding, employing the borehole elastic wave compression and reconstruction method based on piecewise windowing and sparse coding as described in any one of claims 1-9, characterized in that, The system includes: The signal segment acquisition module is used to segment the original borehole elastic wave time sequence signal to obtain multiple signal segments; The windowing and filtering module is used to perform windowing processing on each signal segment using a combined Hann window function and to filter out valid signal segments; the combined Hann window function is composed of a weighted fusion of a full-length Hann window and a central local Hann window; The sparse module is used to solve the sparse representation of each valid signal segment based on a pre-built dictionary using the orthogonal matching pursuit algorithm, and obtain the corresponding sparse coefficient vector and atom index; The average sparsity acquisition module is used to calculate the average value of the sparse coefficient vectors of all valid signal segments based on the sparse coefficient vector corresponding to each valid signal segment, and obtain the average sparsity. The reconstructed effective signal acquisition module is used to reconstruct any effective signal segment based on the average sparsity, the sparse coefficient vector and the atom index corresponding to the effective signal segment, and to obtain the reconstructed effective signal segment. The module then performs inverse weighting processing using the Hann window function corresponding to the reconstructed effective signal segment to obtain the processed reconstructed effective signal segment. The final reconstructed signal acquisition module is used to superimpose each processed reconstructed valid signal segment according to its starting position in the original borehole elastic wave time sequence signal to obtain the final reconstructed signal.

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