An underground acoustic wave signal compressive sensing acquisition method and device

Abstract

The present invention provides a method for acquiring downhole acoustic signal by compressive sensing, which includes: obtaining a measurement matrix for sparse sampling of acoustic wave train signals of multiple detectors; performing data acquisition on the original signal through the sampling points determined by the measurement matrix to obtain sparse sampling data; and solving for the original signal based on the compressive sensing principle by combining the measurement matrix and the sparse sampling data. The sampling interval of the present invention is random and does not need to follow the Nyquist sampling theorem, greatly reducing the amount of data acquisition; moreover, sampling and compression can be regarded as being carried out simultaneously, and the Nyquist sampling theorem does not need to be followed during sampling and compression, and the original signal can be restored at the receiving end through a suitable reconstruction algorithm. Therefore, the present invention can avoid many problems in the existing signal processing mode; the present invention can greatly reduce the signal acquisition time and the amount of acquired data without loss of information, improving the efficiency of acoustic logging.

Description

Technical Field

[0001] The present invention relates to the technical field of acoustic logging, and specifically, to a downhole acoustic signal compressive sensing acquisition method and device. Background Art

[0002] With the rapid development of modern electronics and computer technologies, logging instruments are gradually developing towards the directions of arraying, imaging, and combination. The data volume of logging instruments is getting larger and larger, and the accurate, high-speed, and real-time transmission of a large amount of logging data has become a bottleneck restricting the development of logging equipment technology. The new-generation acoustic logging technologies, such as acoustic far-detection logging and three-dimensional acoustic logging, can perform acoustic imaging on the formation structure around the well by collecting the received full-wave train signals and analyzing and processing the received full-wave signals, and obtain the geological structure information beside the well. The data volume of a single depth point is at least 2.4 Mbite. However, the highest transmission rate of the existing 7000-meter logging cable is 1.2 Mbps, the data transmission time is long, the logging speed cannot be increased, resulting in low logging efficiency and increased construction risks.

[0003] In the existing acoustic signal acquisition and processing mode, the signal needs to be sampled, compressed and then transmitted, and the receiving end needs to decompress and restore the original signal. The sampling process must follow the Nyquist sampling theorem (such as Figure 1 )), that is, the sampling rate cannot be less than twice the highest frequency of the signal, so as to ensure that the original signal can be completely restored according to the information obtained by sampling. In reality, the sampling frequency is often higher than twice the highest frequency, reaching 5 to 10 times the highest frequency. As a result, the sampling frequency becomes higher, the sampling data increases, a higher cable transmission rate is required, and the slow increase in the cable transmission rate leads to only a reduction in the logging speed.

[0004] Therefore, the present invention provides a downhole acoustic signal compressive sensing acquisition method and device. Summary of the Invention

[0005] To solve the above problems, the present invention provides a downhole acoustic signal compressive sensing acquisition method, which includes the following steps:

[0006] Obtain a measurement matrix for sparse sampling of multi-detector acoustic wave train signals;

[0007] Perform data acquisition on the original signal through the sampling points determined by the measurement matrix to obtain sparse sampling data;

[0008] Based on the compressive sensing principle, solve and obtain the original signal by combining the measurement matrix and the sparse sampling data.

[0009] According to an embodiment of the present invention, the measurement matrix is obtained through the following steps:

[0010] Step a: Set an initial sampling sequence;

[0011] Step b: Sample the initial sampling sequence point by point in sequence;

[0012] Step c: Determine whether the maximum cross - correlation value between the column vectors of the sensing matrix becomes smaller;

[0013] Step d: If the determination result in step c is yes, update the measurement matrix and then execute step e; if the determination result in step c is no, discard the current sampling point and do not update the measurement matrix;

[0014] Step e: Determine whether the initial sampling sequence has ended;

[0015] Step f: If the determination result in step e is yes, execute step g; if the determination result in step e is no, return to execute step b;

[0016] Step g: Output the measurement matrix.

[0017] According to an embodiment of the present invention, the maximum cross - correlation value is obtained through the following formula:

[0018]

[0019] where, μ represents the maximum cross - correlation value; Θ represents the sensing matrix; Θ i represents the i - th column vector of the sensing matrix; Θ j represents the j - th column vector of the sensing matrix; H represents the transpose.

[0020] According to an embodiment of the present invention, the measurement matrix is updated through the following formula:

[0021] Φ=argminμ

[0022] where, Φ represents the measurement matrix; μ represents the maximum cross - correlation value of the sensing matrix.

[0023] According to an embodiment of the present invention, the sensing matrix is obtained through the following formula:

[0024] Θ=ΦΨ

[0025] where, Θ represents the sensing matrix; Φ represents the measurement matrix; Ψ represents the sparse basis matrix.

[0026] According to an embodiment of the present invention, the method includes:

[0027] Obtain sparse coefficients based on the measurement matrix, the sparse sampling data, and the sparse basis matrix;

[0028] The original signal is obtained by combining the sparse coefficients with the sparse basis matrix.

[0029] According to an embodiment of the present invention, the sparse coefficients are obtained by the following formula:

[0030] y = ΦΨS

[0031] where y represents the sparse sampling data; Φ represents the measurement matrix; Ψ represents the sparse basis matrix; and S represents the sparse coefficients.

[0032] According to an embodiment of the present invention, the original signal is obtained by the following formula:

[0033] X = ΨS

[0034] where X represents the original signal; Ψ represents the sparse basis matrix; and S represents the sparse coefficients.

[0035] According to another aspect of the present invention, there is also provided a storage medium, which contains a series of instructions for executing the method steps described in any one of the above.

[0036] According to another aspect of the present invention, there is also provided a downhole acoustic wave signal compressive sensing acquisition device, which executes the downhole acoustic wave signal compressive sensing acquisition method described in any one of the above. The device includes:

[0037] A measurement matrix module, which is used to obtain the measurement matrix for sparse sampling of the multi-detector acoustic wave train signal;

[0038] A sparse sampling data module, which is used to perform data acquisition on the original signal through the sampling points determined by the measurement matrix to obtain sparse sampling data;

[0039] An original signal module, which is used to combine the measurement matrix and the sparse sampling data, and solve to obtain the original signal based on the principle of compressive sensing.

[0040] The sampling interval of the downhole acoustic wave signal compressive sensing acquisition method and device provided by the present invention is random and does not need to follow the Nyquist sampling theorem, which greatly reduces the amount of data acquisition; and sampling and compression can be regarded as being carried out simultaneously. When sampling and compressing, it does not need to follow the Nyquist sampling theorem, and the original signal can be restored at the receiving end through a suitable reconstruction algorithm. Therefore, the present invention can avoid many problems in the existing signal processing mode, such as the problems of large amount of data, large storage space, high hardware complexity, large amount of calculation caused by too large sampling rate, and data waste and resource waste problems during the compression process; the present invention can greatly reduce the signal acquisition time and the amount of acquired data without loss of information, and improve the efficiency of acoustic logging.

[0041] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the specification, claims and drawings. Description of the Drawings

[0042] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0043] Figure 1 The schematic diagram of equally spaced acquisition of acoustic signals in the prior art is shown;

[0044] Figure 2 The flowchart of a method for compressive sensing acquisition of downhole acoustic signals according to an embodiment of the present invention is shown;

[0045] Figure 3 The flowchart of a method for obtaining a measurement matrix according to an embodiment of the present invention is shown;

[0046] Figure 4 The schematic diagram of randomly spaced acquisition of acoustic signals according to an embodiment of the present invention is shown;

[0047] Figure 5 The block diagram of a device for compressive sensing acquisition of downhole acoustic signals according to an embodiment of the present invention is shown;

[0048] Figure 6 The schematic diagram of an acquisition matrix for acquiring all points according to an embodiment of the present invention (taking N = 7 as an example); and

[0049] Figure 7 The schematic diagram of an acquisition matrix for acquiring partial points according to an embodiment of the present invention (taking only the 1st / 3rd / 6th / 7th points as an example) is shown.

[0050] In the drawings, the same components are denoted by the same reference numerals. Additionally, the drawings are not drawn to actual scale. Detailed Description of the Embodiments

[0051] To make the objectives, technical solutions and advantages of the present invention clearer, the following further details the embodiments of the present invention with reference to the drawings.

[0052] Figure 2 The flowchart of a method for compressive sensing acquisition of downhole acoustic signals according to an embodiment of the present invention is shown.

[0053] As Figure 2As shown, in step S201, a measurement matrix for sparse sampling of a multi-detector acoustic wave train signal is obtained.

[0054] In one embodiment, Figure 3 Fig. shows a flowchart of a method for obtaining a measurement matrix according to an embodiment of the present invention.

[0055] As Figure 3 shown, in step a, an initial sampling sequence is set.

[0056] Specifically, all sampling points obtained by equally spaced sampling of the wave train received by one receiver in sequence are set as the initial sampling sequence (dimension N*1, generally 200 < N < 2500), and the initial measurement matrix for collecting these N points is set as an N*N identity matrix. Taking N = 7 as an example, the measurement matrix for collecting 7 points is as Figure 6 shown. An N*N sparse transformation matrix (which can be a discrete cosine transform basis or a wavelet transform basis) is set, and the measurement matrix is multiplied by the sparse transformation matrix to obtain a sensing matrix.

[0057] As Figure 3 shown, in step b, the initial sampling sequence is sampled point by point in sequence.

[0058] As Figure 3 shown, in step c, it is judged whether the maximum cross-correlation value between the column vectors of the sensing matrix becomes smaller.

[0059] Specifically, first, the first sampling point is selected, the first row elements are selected from the initial measurement matrix as the measurement matrix, this 1*N measurement matrix is multiplied by the aforementioned N*N sparse transformation matrix to obtain a 1*N sensing matrix, and the maximum cross-correlation value between the column vectors of the sensing matrix is calculated; then the second sampling point is collected, the second row elements are selected from the initial measurement matrix and added as the last row elements of the aforementioned measurement matrix, this 2*N measurement matrix is multiplied by the aforementioned N*N sparse transformation matrix to obtain a 2*N sensing matrix, the maximum cross-correlation value between the column vectors of the new sensing matrix is calculated and compared with the maximum cross-correlation value of the old sensing matrix. If the maximum cross-correlation value becomes smaller, this sampling point is retained and this new measurement matrix is saved; otherwise, the old measurement matrix is restored; new sampling points are continuously collected in sequence. For each added sampling point, the maximum cross-correlation value between the column vectors of the sensing matrix after adding this sampling point is calculated to judge whether the maximum cross-correlation value between the column vectors of the sensing matrix becomes smaller.

[0060] As Figure 3 shown, in step d, if the judgment result in step c is yes, the measurement matrix is updated and then step e is executed; if the judgment result in step c is no, the current sampling point is discarded and the measurement matrix is not updated.

[0061] Specifically, to ensure appropriate uniformity of sampling and avoid large blank areas in the collected data, it is necessary to appropriately constrain the sampling points. The number of continuously skipped sampling points should not be more than a preset number (for example, 4). Taking the preset number as 4 as an example, if the 4th sampling point also needs to be skipped, then the one with the smallest maximum cross-correlation value among the column vectors of the corresponding sensing matrix is selected from these 4 sampling points and retained, and the sampling matrix is updated.

[0062] As Figure 3 shown, in step e, it is determined whether the initial sampling sequence has ended sampling;

[0063] As Figure 3 shown, in step f, if the judgment result in step e is yes, then step g is executed; if the judgment result in step e is no, then return to execute step b;

[0064] As Figure 3 shown, in step g, the measurement matrix is output.

[0065] The adjacent two points in the initial sampling sequence are equidistant. In the present invention, some sampling points are skipped during sampling by the method as Figure 3 shown (such as Figure 7 ), and which points to skip is completely determined by whether the maximum cross-correlation value between the column vectors of the sensing matrix becomes smaller. Therefore, the sampling interval is random and not artificially set, and there is no need to follow the Nyquist sampling theorem, which greatly reduces the data acquisition volume.

[0066] Specifically, according to the theory of compressive sensing, the smaller μ is, the higher the probability that the signal can be reconstructed after random interval sampling. Therefore, the algorithm as Figure 3 shown is used to determine the positions of the sampling points, and the maximum cross-correlation value is reduced by changing the distribution of the sampling points.

[0067] In one embodiment, the maximum cross-correlation value is obtained by the following formula:

[0068]

[0069] where μ represents the maximum cross-correlation value; Θ represents the sensing matrix; Θ i represents the i-th column vector of the sensing matrix; Θ j represents the j-th column vector of the sensing matrix; H represents the transpose.

[0070] In one embodiment, the measurement matrix is updated by the following formula:

[0071] Φ = argmin μ

[0072] where Φ represents the measurement matrix; μ represents the maximum cross-correlation value of the sensing matrix.

[0073] In one embodiment, the sensing matrix is obtained through the following formula:

[0074] Θ = ΦΨ

[0075] where Θ represents the sensing matrix; Φ represents the measurement matrix; Ψ represents the sparse basis matrix.

[0076] As Figure 2 shown, in step S202, the original signal is sampled through the sampling points determined by the measurement matrix to obtain sparse sampling data. Specifically, the sampling interval determined by the measurement matrix is random (such as Figure 4 ), and it does not need to follow the Nyquist sampling theorem, greatly reducing the amount of data acquisition.

[0077] Specifically, the sampling points are determined by the measurement matrix Φ, and the sparse sampling data y is obtained through data acquisition. The compressive sensing sampling process of step S202 can be expressed as:

[0078] y = ΦX

[0079] where X represents the original signal, which is a one-dimensional signal with a length of N, y represents the sparse sampling data, that is, the observed value obtained after compressive sampling, which is also a one-dimensional sampling value with a length of M (generally M << N), and Φ represents the measurement matrix (measurement matrix), which is an M×N matrix. In this way, the compressive sampling value can be directly obtained without first sampling and then compressing. The compressive sensing problem to be solved by the present invention is to solve the underdetermined equation system y = ΦX to obtain the original signal X based on the known measurement value y and the measurement matrix Φ.

[0080] In one embodiment, let X = ΨS, where Ψ is the sparse basis matrix and S is the sparse coefficient. The equation y = ΦX then becomes:

[0081] y = ΦΨS

[0082] Solve for S when y, Φ, and Ψ are known. Combine Φ and Ψ into a matrix, which is called the sensing matrix Θ. That is:

[0083] Θ = ΦΨ

[0084] Then the equation y = ΦΨS becomes:

[0085] y = ΘS

[0086] Solve for S when y and Θ are known. After solving for S, the original signal X can be recovered from X = ΨS.

[0087] In one embodiment, Ψ can select the Hadamard orthogonal basis. The selection of Φ needs to follow the Restricted Isometry Property (RIP), also known as the RIP property, which can ensure that all the key information in the original signal X is contained in y, so that X can be successfully recovered from y. The equivalent condition of RIP is that the observation matrix and the sparse basis matrix Ψ are incoherent. According to the compressive sensing theory, the smaller μ is, the higher the probability that the signal can be reconstructed after random interval sampling. Therefore, an algorithm as shown in Figure 3 is used to determine the positions of the sampling points, and the maximum cross-correlation value is reduced by changing the distribution of the sampling points.

[0088] As shown in Figure 2 , in step S203, based on the measurement matrix and the sparse sampling data, the original signal is solved based on the compressive sensing principle.

[0089] In one embodiment, step S203 specifically includes the following steps:

[0090] Step S2031: Obtain the sparse coefficients based on the measurement matrix, the sparse sampling data, and the sparse basis matrix. Specifically, the sparse coefficients are obtained through the following formula:

[0091] y = ΦΨS

[0092] where y represents the sparse sampling data; Φ represents the measurement matrix; Ψ represents the sparse basis matrix; S represents the sparse coefficients.

[0093] Step S2032: Obtain the original signal by combining the sparse coefficients with the sparse basis matrix.

[0094] Specifically, the original signal is obtained through the following formula:

[0095] X = ΨS

[0096] where X represents the original signal; Ψ represents the sparse basis matrix; S represents the sparse coefficients.

[0097] The sampling interval of the compressive sensing method adopted by the present invention is random and does not need to follow the Nyquist sampling theorem, which greatly reduces the amount of data acquisition. In the present invention, sampling and compression can be regarded as being carried out simultaneously, and the Nyquist sampling theorem does not need to be followed during sampling and compression. At the receiving end, the original signal can be recovered through a suitable reconstruction algorithm, so the problems in the existing signal processing mode can be avoided, that is, the problems of large amount of data, large storage space, high hardware complexity, large amount of calculation caused by too large sampling rate, as well as data waste and resource waste problems during the compression process.

[0098] The compressive sensing method adopted by the present invention can reduce the hardware consumption and energy consumption underground and improve the high-temperature performance of downhole instruments. The present invention meets two prerequisite conditions for compressive sensing applications. The first prerequisite condition is that the signal has sparsity in a certain domain; the second prerequisite condition is that the sampled data has incoherence. Specifically, the acoustic logging signal is sparse in the frequency domain, so it meets the prerequisite condition that the signal has sparsity in a certain domain; the present invention reduces the maximum cross-correlation value by changing the distribution of sampling points to ensure the incoherence of the sampled data, so it meets the prerequisite condition that the sampled data has incoherence. The downhole acoustic signal compressive sensing acquisition method and device provided by the present invention can perform data sampling and raw data recovery.

[0099] The downhole acoustic signal compressive sensing acquisition method and device provided by the present invention can also cooperate with a computer-readable storage medium. A computer program is stored on the storage medium, and the computer program is executed to run a downhole acoustic signal compressive sensing acquisition method. The computer program can run computer instructions, and the computer instructions include computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate forms, etc.

[0100] The computer-readable storage medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0101] It should be noted that the content included in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0102] Figure 5 The structural block diagram of a downhole acoustic signal compressive sensing acquisition device according to an embodiment of the present invention is shown.

[0103] As Figure 5 shown, the downhole acoustic signal compressive sensing acquisition device 500 includes a measurement matrix module 501, a sparse acquisition data module 502, and a raw signal module 503.

[0104] The measurement matrix module 501 is used to obtain the measurement matrix for sparse sampling of the multi-detector acoustic wave train signal.

[0105] The sparse sampling data module 502 is used to perform data acquisition on the original signal through the sampling points determined by the measurement matrix to obtain sparse sampling data.

[0106] The original signal module 503 is used to combine the measurement matrix and the sparse sampling data, and solve for the original signal based on the principle of compressive sensing.

[0107] In summary, for the downhole acoustic signal compressive sensing acquisition method and device provided by the present invention, the sampling interval is random and does not need to follow the Nyquist sampling theorem, greatly reducing the amount of data acquisition; moreover, sampling and compression can be regarded as being carried out simultaneously, and the Nyquist sampling theorem does not need to be followed during sampling compression. At the receiving end, the original signal can be restored through a suitable reconstruction algorithm. Therefore, the present invention can avoid many problems in the existing signal processing mode, such as the problems of large data volume, large storage space, high hardware complexity, large computation amount caused by too large sampling rate, as well as data waste and resource waste problems during the compression process; the present invention can greatly reduce the signal acquisition time and the amount of acquired data without loss of information, improving the efficiency of acoustic logging.

[0108] It should be understood that the embodiments disclosed in the present invention are not limited to the specific structures, processing steps or materials disclosed herein, but should extend to equivalent alternatives of these features understood by those of ordinary skill in the relevant art. It should also be understood that the terms used herein are only for the purpose of describing specific embodiments and do not mean to limit.

[0109] In the description of the present invention, unless otherwise specified, the meaning of "a plurality of" is two or more; the orientation or positional relationships indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0110] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood in specific situations.

[0111] As used herein, the term "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the phrases "one embodiment" or "an embodiment" that appear throughout the specification do not necessarily all refer to the same embodiment.

[0112] The embodiments of the present invention are provided for purposes of illustration and description, and are not intended to be exhaustive or to limit the invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are chosen and described in order to best explain the principles of the invention and its practical application, and to enable those of ordinary skill in the art to understand the invention so as to design various embodiments with various modifications suitable for a particular purpose.

[0113] Although the embodiments disclosed in the present invention are as described above, the above content is only an embodiment adopted for the convenience of understanding the present invention, and is not intended to limit the present invention. Any person skilled in the art within the technical field to which the present invention pertains may make any modifications and variations in the form and details of the implementation without departing from the spirit and scope disclosed by the present invention. However, the scope of patent protection of the present invention shall still be subject to the scope defined by the appended claims.

Claims

1. A downhole acoustic signal compressive sensing acquisition method, characterized in that, The method includes the following steps: Obtain a measurement matrix for sparse sampling of multi-detector acoustic wave train signals; Perform data acquisition on the original signal through the sampling points determined by the measurement matrix to obtain sparse sampling data; Combine the measurement matrix and the sparse sampling data, and solve for the original signal based on the principle of compressive sensing; Obtain the measurement matrix through the following steps: Step a: Set an initial sampling sequence; Step b: Sample the initial sampling sequence point by point in order; Step c: Determine whether the maximum cross-correlation value between the column vectors of the sensing matrix becomes smaller; Step d: If the judgment result of step c is yes, update the measurement matrix and then execute step e. If the judgment result of step c is no, discard the current sampling point and do not update the measurement matrix, where the sampling points are constrained so that the continuously skipped sampling points do not exceed a preset number; Step e: Determine whether the initial sampling sequence has ended; Step f: If the judgment result of step e is yes, execute step g. If the judgment result of step e is no, return to execute step b; Step g: Output the measurement matrix; Update the measurement matrix through the following formula: Φ=argminμ where Φ represents the measurement matrix; μ represents the maximum cross-correlation value of the sensing matrix; Obtain the maximum cross-correlation value through the following formula: where, μ represents the maximum cross-correlation value; Θ represents the sensing matrix; Θ i represents the i-th column vector of the sensing matrix; Θ j represents the j-th column vector of the sensing matrix; H represents the transpose.

2. The downhole acoustic signal compressive sensing acquisition method according to claim 1, characterized in that Obtain the sensing matrix through the following formula: Θ=ΦΨ where Θ represents the sensing matrix; Φ represents the measurement matrix; Ψ represents the sparse basis matrix.

3. The downhole acoustic signal compressive sensing acquisition method according to claim 1, characterized in that The method includes: Obtain sparse coefficients based on the measurement matrix, the sparse sampling data, and the sparse basis matrix; Obtain the original signal by combining the sparse coefficients and the sparse basis matrix.

4. The downhole acoustic signal compressive sensing acquisition method according to claim 3, characterized in that Obtain the sparse coefficients through the following formula: y=ΦΨS where y represents the sparse sampling data; Φ represents the measurement matrix; Ψ represents the sparse basis matrix; S represents the sparse coefficients.

5. The downhole acoustic signal compressive sensing acquisition method according to claim 3, characterized in that Obtain the original signal through the following formula: X=ΨS where X represents the original signal; Ψ represents the sparse basis matrix; S represents the sparse coefficients.

6. A storage medium, characterized in that, It includes a series of instructions for executing the steps of the downhole acoustic signal compressive sensing acquisition method described in any one of claims 1-5.

7. An underground acoustic signal compressive sensing acquisition device, characterized in that Execute the downhole acoustic signal compressive sensing acquisition method described in any one of claims 1-5. The device includes: A measurement matrix module for obtaining a measurement matrix for sparse sampling of multi-detector acoustic wave train signals; A sparse sampling data module for performing data acquisition on the original signal through the sampling points determined by the measurement matrix to obtain sparse sampling data; An original signal module for combining the measurement matrix and the sparse sampling data and solving for the original signal based on the principle of compressive sensing.

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