Seismic signal data processing method and device, electronic equipment and storage medium

Seismic signal data processing method, device, electronic equipment and storage medium technical field

[0001] This application relates to the field of signal processing technology, and in particular to a seismic signal data processing method, device, electronic equipment, and readable storage medium.

Background technique

[0002] The noise in the seismic data will cause errors in subsequent attribute analysis, wave impedance inversion, fracture prediction, etc., thereby seriously affecting the work of underground structure interpretation and geophysical inversion. Since the seismic data collected in the field will inevitably be interfered by noise, it is necessary to increase the signal-to-noise ratio in the signal system, that is, the signal-to-noise ratio.

[0003] In order to improve the signal-to-noise ratio of seismic data, incoherent noise in the collected seismic signal data can be attenuated. The singular spectrum analysis method can detect the low-rank structure of the data. Based on the different performance of the seismic signal and random noise on the singular spectrum, this method can effectively suppress the seismic random noise. However, because the singular spectrum analysis method adopts a quadratic form of the fitting curve and is very sensitive to non-Gaussian interference, when the seismic data is disturbed by unstable noise, the performance of the singular spectrum analysis method is not stable and cannot be identified based on the weighting criterion of absolute difference. And suppression of abnormal values, it is easy to cause a large amount of noise energy to remain or be considered as interference. The denoising effect of seismic data is not obvious, and the signal-to-noise ratio is not high. If n (n>2) iterations are used to obtain better The denoising effect will be due to the greater computational cost required by the iterative steps, especially when the robust function is difficult to approximate, which will result in lower data processing efficiency.

[0004] In view of this, how to suppress seismic noise more efficiently and stably is a technical problem that needs to be solved by those skilled in the art.

Summary of the invention

[0005] This application provides a seismic signal data processing method, device, electronic equipment, and readable storage medium, which can suppress seismic noise more efficiently and stably, and effectively improve the signal-to-noise ratio of seismic signal data.

[0006] In order to solve the above technical problems, embodiments of the present invention provide the following technical solutions:

[0007] One aspect of the embodiments of the present invention provides a seismic signal data processing method, including:

[0008] Use the singular spectrum analysis method to filter the original seismic signal data to obtain the initial filtered data;

[0009] Determine the difference and similarity between the original seismic signal data and the initial filtered data;

[0010] A weighting matrix is calculated based on the difference, the similarity, the preset weighted intensity control parameter and the local similarity threshold; the weighted intensity control parameter is used to predict the energy attenuation of the unstable disturbance, and the local similarity The sexual threshold is used to determine the degree of signal retention;

[0011] The original seismic signal data is filtered again based on the weighting matrix.

[0012] Optionally, the determining the difference and similarity between the original seismic signal data and the initial filtered data includes:

[0013] The absolute deviation calculation relationship is called to calculate the absolute deviation U between the original seismic signal data and the initial filtered data, U=[Uj The absolute deviation calculation relationship is =%-1%, see j for the The absolute deviation corresponds to the element at position (i, j) in the matrix, and djj is the element at position (i, j) in the matrix corresponding to the original seismic signal data

Element, B is the element at position (i, j) in the matrix corresponding to the initial filtering data.

[0014] Optionally, the determining the difference and similarity between the original seismic signal data and the initial filtered data includes:

[0015] The local similarity calculation relational expression is called to calculate the local similarity V, V= between the original seismic signal data and the initial filtered data, and the local similarity calculation relational expression is: _ Attached

[0016] dagger J;

ύ)...) Live ω...

[0017] In the formula, Vj j is the element at position (i, j) in the local similarity corresponding matrix, 3 is the local smoothing window function, t is the current position (i, j) moved T units along i, k Is the current position (i, j) moves k units along j, d_T h is the element at the position (T, k) in the matrix corresponding to the original seismic signal data, and w is the element at the position (T, k) in the matrix corresponding to the initial filtered data.

[0018] Optionally, the calculation of a weighting matrix based on the difference, the similarity, a preset weighted intensity control parameter, and a local similarity threshold includes:

[0019] The weighted element calculation relationship is called to calculate the weighting matrix, and the weighted element calculation relationship is: (group,·/£,group; <sandv.,>

[0020] w..=Eight'JJ;

II

[1, otherwise

[0021] In the formula, Wj j is the element of the weighting matrix at position (i, j), and L j is the element of the absolute deviation corresponding to the difference in the matrix at position (i, j). Is the normalized absolute deviation, Vj,j is the element h of the local similarity corresponding matrix used to reflect the similarity at position (i,j) is the local similarity threshold, and p is the weighted intensity control parameter.

[0022] Optionally, the performing filtering processing on the original seismic signal data again based on the weighting matrix includes:

[0023] Calculate the Hadamard product of the weighting matrix to the original seismic signal data to obtain a seismic signal modification data;

[0024] Along the time direction, perform forward one-dimensional Fourier transform on the one-time seismic signal modification data to obtain frequency slices;

[0025] The Hankel matrix embedding operation, rank reduction operation, and averaging operation are sequentially performed on the frequency slices to obtain secondary seismic signal modification data;

[0026] Along the time direction, a reverse one-dimensional Fourier transform is performed on the secondary seismic signal modification data to obtain the data processing result of the original seismic signal data.

[0027] Another aspect of the embodiments of the present invention provides a seismic signal data processing device, including:

[0028] The initial filtering module is used to filter the original seismic signal data by using the singular spectrum analysis method to obtain initial filtering data;

[0029] A parameter calculation module for determining the difference and similarity between the original seismic signal data and the initial filtered data;

[0030] A weight calculation module for calculating a weighting matrix based on the difference, the similarity, a preset weighted intensity control parameter, and a local similarity threshold; the weighted intensity control parameter is used to predict the energy of an unstable disturbance Attenuation, where the local similarity threshold is used to determine the degree of signal retention;

[0031] A denoising module for filtering the original seismic signal data again based on the weighting matrix.

[0032] Optionally, the parameter calculation module is further configured to: call an absolute deviation calculation relational expression to calculate the absolute deviation U, U = [%, j] between the original seismic signal data and the initial filtered data, The absolute deviation calculation relationship can be expressed as: = predicty 1 j is the element at position (i, j) in the absolute deviation corresponding matrix, and djj is (i, j) in the original seismic signal data corresponding matrix The element at position, B is the element at position (i, j) in the matrix corresponding to the initial filtering data.

[0033] An embodiment of the present invention also provides an electronic device, including a processor, configured to implement the steps of the seismic signal data processing method described in any one of the preceding items when the processor is used to execute a computer program stored in a memory.

[0034] The embodiment of the present invention finally provides a readable storage medium with a computer program stored on the readable storage medium, and when the computer program is executed by a processor, the data of the seismic signal as described in any of the preceding items is realized Processing method steps.

[0035] The advantage of the technical solution provided by the present application is that the weighting matrix calculated by using the difference and similarity between the primary filtered data and the original seismic signal data can accurately predict the position and energy level of the unstable disturbance , The original seismic signal data is filtered twice based on the weighting matrix, which can eliminate the instability and random noise in the seismic signal data, and effectively improve the signal-to-noise ratio of the seismic signal data. The whole process does not need multiple iterations and does not require large calculation costs. , Can suppress earthquake noise more efficiently and more stably.

[0036] In addition to daggers, the embodiments of the present invention also provide corresponding implementation devices, electronic equipment, and readable storage media for seismic signal data processing methods, further making the method more practical. And the readable storage medium has corresponding advantages.

[0037] It should be understood that the above general description and the following detailed description are only exemplary and cannot limit the present disclosure.

Description of the drawings

[0038] In order to more clearly describe the technical solutions of the embodiments of the present invention or related technologies, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments or related technologies. Obviously, the accompanying drawings in the following description are merely These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0039] FIG. 1 is a schematic flowchart of a seismic signal data processing method provided by an embodiment of the present invention;

[0040] FIG. 2 is a structural diagram of a specific implementation manner of a seismic signal data processing device provided by an embodiment of the present invention;

[0041] FIG. 3 is a structural diagram of a specific implementation manner of an electronic device provided by an embodiment of the present invention.

Detailed ways

[0042] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, not

All examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0043] The terms "first", "second", "third", "fourth", etc. in this application, the claims and the above-mentioned drawings are used to distinguish different objects, not to describe specific Order. In addition, the terms "including" and "having" and any variations of them are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may include unlisted steps or units.

[0044] After introducing the technical solutions of the embodiments of the present invention, various non-limiting implementations of the present application will be described in detail below.

[0045] First, referring to FIG. 1, which is a schematic flowchart of a seismic signal data processing method provided by an embodiment of the present invention. The embodiment of the present invention may include the following content:

[0046] S101: Use a singular spectrum analysis method to filter the original seismic signal data to obtain initial filtered data.

[0047] In this step, the singular spectrum analysis method is any existing method for studying nonlinear time series data, which can construct a trajectory matrix according to the observed time series, and decompose and reproduce the trajectory matrix. Structure, so as to extract signals representing different components of the original time series, such as noise signals, periodic signals, etc. The filtering process of the original address signal data based on the singular spectrum analysis method may include:

[0048] The singular spectrum analysis method includes Hankel matrix embedding operation, rank reduction operation, and anti-angle averaging operation. If D (x, 3) is the frequency slice of the original seismic signal data, where x = 1,2,3,a =1,2,3,...is the number of sampling points in the space and frequency directions. The singular spectrum analysis method first embeds the frequency slice D(x, a) into the Hankel matrix to obtain a Hankel matrix with the following size (nx-m+l) Xm. The value of each element of the Hankel matrix and the subdiagonal direction of the matrix The values of the upper elements are equal. m is a predefined integer, and the value of m should make the Hankel matrix f approximate to a square matrix, for example, mountain =&amp;-&amp;/2, where] "represents the integer part of the parameter. The Hankel matrix f can be expressed as:

<td></td><td>/ D(\,1)</td><td>Ο(2,ω)</td><td>...D(ηι,ω),</td>

<td>[0049] Two redundant (D®)</td><td>D(2,a))</td><td>Ο(3,ω)</td><td>...D( m + ϊ,ω)</td>

<td></td><td>k-plus + 1 )</td><td>-Add + 2m)</td><td></td>

[0050] The singular spectrum analysis method assumes that the seismic data can be regarded as a superposition of K plane waves, so the rank of the Hankel matrix y is K. The addition of noise will increase the rank of f. Therefore, the singular spectrum analysis method eliminates noise by reducing the rank of f, that is, performing singular value decomposition (SVD):

[0051]

<img file="CN113687421A_D0001.tif" />

[0052] &amp;(H.)=H^w = Qiong(V;);

[0053] In the formula, () n represents the Hermitian transpose of the matrix. :L:, and VL represent the first K largest singular values of the matrix f and the related K singular vectors. May &lt;" means the low-rank approximation of f. Finally, the singular spectrum analysis method is the inverse of the matrix hundred

_ (\ The diagonal is averaged based on the relationship D3 = 2 N3 to restore the filtered data. In the formula, 2 represents the averaging operator. The filtering method of the singular spectrum analysis method can be based on Β'=%(%( Where (D'))) realize, ba, bird, Ρ_nRespectively represent the averaging operator, the rank reduction operator and the Hankel operator.

[0054] S102: Determine the difference and similarity between the original seismic signal data and the initial filtered data.

[0055] In this step, the difference refers to the deviation between the original seismic signal data and the initial filtered data, and the similarity refers to the degree of similarity between the original seismic signal data and the initial filtered data. Any parameter that reflects the degree of deviation and similarity between data can be used for representation, and this application does not make any limitation on this.

[0056] S103: Calculate a weighting matrix based on differences, similarities, preset weighted intensity control parameters, and local similarity thresholds.

[0057] In order to accurately predict the position and energy level of the unstable disturbance, this application will pre-set a number of parameters, including but not limited to the weighted intensity control parameter P that can be used to predict the energy attenuation of the unstable disturbance, which is used to determine the signal retention The degree of local similarity threshold τ, a constant k that depends on the number of plane waves contained in the processing window, is used to determine the constant value of the intensity of the unstable disturbance. The denoising intensity of this embodiment decreases with the decrease of Ke and people. Enhance, increase with the increase of P. The reference value of the parameter Ph and the person may be, for example, 5, 0.6, and 4, respectively. In actual processing, its value can fluctuate up or down according to specific issues, which does not affect the realization of this application. According to the similarity and difference between the initial filtered data and the original seismic signal, combined with the parameters that determine the energy attenuation and signal retention of the unstable disturbance, a weighting matrix that can predict the position and energy level of the unstable disturbance can be obtained.

[0058] S104: Filter the original seismic signal data again based on the weighting matrix.

[0059] In this step, the original seismic signal data is processed again based on the weighting matrix calculated in the previous step. The Hadamard product of the weighting matrix to the original seismic signal data can be calculated first, that is, the weighting matrix and the matrix corresponding to the original seismic signal data The corresponding location elements are multiplied to obtain a seismic signal modification data. In the time direction, a forward one-dimensional Fourier transform is performed on the modified seismic signal data to obtain frequency slices. The Hankel matrix embedding operation, rank reduction operation and averaging operation can be performed in sequence on the frequency slice according to the method of S101, and the modified data of the secondary seismic signal can be obtained. In the time direction, reverse one-dimensional Fourier transform is performed on the modified data of the secondary seismic signal to obtain the final seismic signal data, which can be output as the data processing result of the original seismic signal data.

[0060] In the technical solution provided by the embodiment of the present invention, the weighting matrix calculated by using the difference and similarity between the primary filtered data and the original seismic signal data can accurately predict the position and performance of the unstable disturbance. Level, the original seismic signal data is filtered twice based on the weighting matrix, which can eliminate the instability and random noise in the seismic signal data, and effectively improve the signal-to-noise ratio of the seismic signal data. The whole process does not require multiple iterations and large calculations. Cost, which can suppress seismic noise more efficiently and more stably.

[0061] It should be noted that there is no strict order of execution between the steps in this application. As long as they conform to the logical order, these steps can be executed at the same time or in a certain preset order. FIG. 1 is just a This kind of schematic method does not mean that it can only be in this order of execution.

[0062] In the foregoing embodiment, there is no limitation on how to perform steps S102 and S103. In this embodiment, a calculation method of the weighting matrix is provided, which may include the following steps:

[0063] First, the absolute deviation calculation relation can be called to calculate the difference between the original seismic signal data and the initial filtered data.

Absolute deviation U, U= [u..], the absolute deviation calculation relation can be expressed as: w..=male-4,·, Ba· is the element at position (i, j) in the absolute deviation corresponding matrix, nine Is the element at position (i, j) in the matrix corresponding to the original seismic signal data, and 4 is the element at position (i, j) in the matrix corresponding to the initial filtered data.

[0064] Second, the local similarity calculation relational expression can be called to calculate the local similarity V, V=M, j between the original seismic signal data and the initial filtered data, and the local similarity calculation relational expression can be expressed as: time 65]% Two hm million 21; ^ωΐ-τ,}-Ατ.κ ^ωϊ-τ,}-κ^τ,κ)

[0066] In the formula, Vj j is the element at position (i, j) in the local similarity corresponding matrix, 3 is the local smoothing window function, t is the current position (i, j) moved by T units along i, k Is the current position (i, j) move k units along j, d_T k is the element at the position (t,k) in the matrix corresponding to the original seismic signal data, and B is the element ΐM at the position (t,k) in the matrix corresponding to the initial filtered data. 3 can choose bell function, trigonometric function or rectangular function. In this embodiment, the local similarity can also be calculated using a shaping regularization method, where the smoothness can be determined by a shaping operator.

[0067] Finally, the weighted element calculation relationship can be called to calculate the weighted matrix used to represent the position and amplitude of the unstable disturbance. The grid points (i, j) of the original seismic signal data are contaminated by the unstable disturbance at time, Wj The smaller the value of j, the stronger the disturbance. The calculation relation of weighted elements can be expressed as:'(u-. /, U-. <Sandv..> jin

[0068] scold / = repair / 3 ";

[1, otherwise

[0069] In the formula, Wj j is the element at the position (i, j) of the weighting matrix, and L j is the absolute deviation U used to reflect the difference. The element at the position (i, j) in the corresponding matrix is not normalized. The calculation method of the absolute deviation of the transformation can be w =----(med ianlU-med i an(U)1), v" is the local similarity corresponding moment 0.6745 used to reflect the similarity^{v 1} &lt;Sichuan], the element E at the position (i, j) in the J matrix is the local similarity threshold, and P is the weighted intensity control parameter.

[0070] In this embodiment, the position and amplitude of the unstable disturbance can be predicted through the final calculation, and the original seismic signal data can be filtered again based on the position and amplitude of the predicted stable disturbance, which can remove it to the greatest extent. Noise improves the signal-to-noise ratio of the original seismic signal data.

[0071] The embodiment of the present invention also provides a corresponding device for the seismic signal data processing method, which further makes the method more practical. Among them, the device can be described separately from the perspective of functional modules and the perspective of hardware. The seismic signal data processing device provided by the embodiment of the present invention is introduced below. The seismic signal data processing device described below and the seismic signal data processing method described above can be referred to each other.

[0072] Based on the perspective of the functional modules, refer to FIG. 2. FIG. 2 is a structural diagram of a seismic signal data processing device provided by an embodiment of the present invention in a specific implementation manner. The device may include:

[0073] The initial filtering module 201 is used to filter the original seismic signal data by using the singular spectrum analysis method to obtain initial filtering data;

[0074] The parameter calculation module 202 is used to determine the difference and the difference between the original seismic signal data and the initial filtered data.

Similarity

[0075] The weight calculation module 203 is used to calculate a weighting matrix based on differences, similarities, preset weighted intensity control parameters and local similarity thresholds; weighted intensity control parameters are used to predict the energy attenuation of unstable disturbances, and local similarity The threshold is used to determine the degree of signal retention;

[0076] The denoising module 204 is used for filtering the original seismic signal data again based on the weighting matrix.

[0077] Optionally, in some implementations of this embodiment, the aforementioned parameter calculation module 202 may include an absolute deviation calculation unit and a similarity calculation unit;

[0078] Wherein, the absolute deviation calculation unit can be used to: call the absolute deviation calculation relational expression to calculate the absolute deviation U of the original seismic signal data and the initial filtered data, U=MJ, and the absolute deviation calculation relational expression can be expressed as: U, · = g . one% ¹ > J * yy %, j is the element at position (i, j) in the absolute deviation corresponding matrix, smaller is the element at position (i, j) in the matrix corresponding to the original seismic signal data, and a is the element in the matrix corresponding to the initial filter data (i, j) Element at position.

[0079] The similarity calculation unit can be used to: call the local similarity calculation relational expression to calculate the local similarity V between the original seismic signal data and the initial filtered data, V=MJ, the local similarity calculation relational expression is: 1i-τ . iKGonggongκ

[0080] ^Vi,j = // ;; ya\ w-fj-star tx )\ rx /

[0081] In the formula, Vj j is the element at the position (i, j) in the local similarity corresponding matrix, 3 is the local smoothing window function, t is the current position (i, j) moved T units along i, k Is the current position (i, j) moves k units along j, d_T k is the element at position (T, k) in the original seismic signal data corresponding matrix, and J is the element C, norm element at the position (T, k) in the matrix corresponding to the initial filtered data.

[0082] Optionally, in other implementations of this embodiment, the above-mentioned weight calculation module 203 may be further configured to: call a weighted element calculation relational expression to calculate a weighted matrix, and the weighted element calculation relational expression is: (situation, ·/ £)?, condition;; <sandv.,.>

[0083] W.. ^lJ )'J /';

I j

[1, otherwise

[0084] In the formula, Wj, j are the elements of the weighting matrix at position (i, j), %, j are the absolute deviations, e is the normalized absolute deviation, Vj, j are the local similarity and E is the local similarity Sex threshold, P is the weighted intensity control parameter.

[0085] Optionally, in some other implementation manners of this embodiment, the aforementioned denoising module 204 may be further used for:

[0086] Calculate the Hadamard product of the weighting matrix to the original seismic signal data to obtain a seismic signal modification data;

[0087] Along the time direction, a forward one-dimensional Fourier transform is performed on the seismic signal modification data to obtain frequency slices;

[0088] Performing Hankel matrix embedding, rank reduction, and averaging operations on frequency slices to obtain secondary seismic signal modification data;

[0089] In the time direction, the modified data of the secondary seismic signal is then subjected to a reverse one-dimensional Fourier transform to obtain the final seismic signal data.

[0090] The function of each functional module of the seismic signal data processing device of the embodiment of the present invention can be implemented according to the above-mentioned method.

The method in the embodiment is specifically implemented, and the specific implementation process can refer to the related description of the above method embodiment, which will not be repeated here.

[0091] It can be seen from the above that the embodiments of the present invention effectively improve the signal-to-noise ratio of seismic signals, and can suppress seismic noise more efficiently and stably.

[0092] The seismic signal data processing device mentioned above is described from the perspective of functional modules. Further, this application also provides an electronic device, which is described from the perspective of hardware. FIG. 3 is a schematic structural diagram of an electronic device according to an embodiment of the application in an implementation manner. As shown in Fig. 3, the electronic device includes a memory 30 for storing a computer program; a processor 31 for implementing the steps of the seismic signal data processing method mentioned in any of the above embodiments when the computer program is executed.

[0093] The processor 31 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the processor 31 may also be a controller, a microcontroller, a microprocessor, or other data processing chips, etc. . The processor 31 may adopt at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). accomplish. The processor 31 may also include a main processor and a coprocessor. The main processor is a processor used to process data in the awake state, also called a CPU (Central Processing Unit, central processing unit); the coprocessor is A low-power processor used to process data in the standby state. In some embodiments, the processor 31 may be integrated with a GPU (Graphics Processing Unit, image processor), and the GPU is used for rendering and drawing content that needs to be displayed on the display screen. In some embodiments, the processor 31 may further include an AI (Artificial Intelligence, artificial intelligence) processor, and the AI processor is used to process calculation operations related to machine learning.

[0094] The memory 30 may include one or more computer-readable storage media, which may be non-transitory. The memory 30 may also include high-speed random access memory and non-volatile memory, such as one or more magnetic disk storage devices and flash memory storage devices. In some embodiments, the memory 30 may be an internal storage unit of an electronic device, such as a hard disk of a server. In other embodiments, the memory 30 may also be an external storage device of the electronic device, such as a plug-in hard disk equipped on a server, a smart memory card (Smart Media Card, SMC), a Secure Digital (SD) card, and a flash memory. Card (Flash Card) etc. Further, the memory 30 may also include both an internal storage unit of an electronic device and an external storage device. The memory 30 can be used not only to store application software and various data installed in the electronic device, such as code of a program that executes the vulnerability processing method, etc., but also to temporarily store data that has been output or will be output. In this embodiment, the memory 30 is used to store at least the following computer program 301, where the computer program is loaded and executed by the processor 31 to implement the relevant steps of the seismic signal data processing method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 30 may also include an operating system 302 and data 303, etc. The method can be short-term storage or permanent storage. Among them, the operating system 302 may include Windows, Unix, Linux, and so on. The data 303 may include, but is not limited to, data corresponding to the data processing result of the seismic signal, and the like.

[0095] In some embodiments, the above-mentioned electronic device may further include a display screen 32, an input/output interface 33, a communication interface 34, or a network interface, a power supply 35, and a communication bus 36. Among them, the display screen 32 and the input/output interface 33 such as a keyboard (Keyboard) belong to the user interface, and the optional user interface may also include a standard wired interface, a wireless interface, and so on. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, and the like. The display can also be appropriately called a display screen or a display unit, which is used to display information processed in the electronic device and to display a visualized user interface. The communication interface 34 may optionally include a wired interface and/or a wireless interface, such as a WI-FI interface, a Bluetooth interface, etc., which are usually used for

Establish a communication connection between the electronic device and other electronic devices. The communication bus 36 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus or the like. The bus can be divided into address bus, data bus, control bus and so on. For ease of presentation, only a thick line is used in FIG. 3 to represent it, but it does not mean that there is only one bus or one type of bus.

[0096] Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, for example, may also include sensors that implement various functions. 37.

[0097] The function of each functional module of the electronic device described in the embodiment of the present invention can be specifically implemented according to the method in the above method embodiment, and the specific implementation process can refer to the relevant description of the above method embodiment, and will not be repeated here.

[0098] It can be seen from the above that the embodiments of the present invention effectively improve the signal-to-noise ratio of seismic signals, and can suppress seismic noise more efficiently and stably.

[0099] It can be understood that if the seismic signal data processing method in the above embodiment is implemented in the form of 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, the technical solution of this application essentially or the part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium. Execute all or part of the steps of the method in each embodiment of this application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), electrically erasable programmable ROM, registers, hard disks, multimedia Cards, card-type memories (such as SD or DX memories, etc.), magnetic memories, removable disks, CD-ROMs, magnetic disks or optical disks and other media that can store program codes.

[0100] Based on this, an embodiment of the present invention also provides a readable storage medium that stores a computer program, and when the computer program is executed by a processor, the steps of the seismic signal data processing method described in any of the above embodiments are the same.

[0101] The function of each functional module of the readable storage medium described in the embodiment of the present invention can be specifically implemented according to the method in the above method embodiment. For the specific implementation process, please refer to the relevant description of the above method embodiment, which will not be repeated here. .

[0102] The various embodiments in this disclosure are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. For the hardware disclosed in the embodiment, including the device and electronic equipment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0103] Professionals may further realize that the units and algorithm steps of the examples described in the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two, in order to clearly illustrate the hardware and For the interchangeability of software, the composition and steps of each example have been generally described in accordance with the function in the above description. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered as going beyond the scope of the present invention.

[0104] The above provides a detailed introduction to the seismic signal data processing method, device, electronic equipment, and readable storage medium provided by this application. Specific examples are used in this article to describe the principle and implementation of the present invention. The description of the above embodiments is only used to help understand the method and core idea of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made to this application, and these improvements and modifications also fall within the protection scope of the claims of this application.