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

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

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

Background technique

[0002] Noise in seismic data will bring errors to subsequent attribute analysis, wave impedance inversion, fracture prediction, etc., thereby seriously affecting the interpretation of subsurface structures, geophysical inversion and other work. Since the seismic data collected in the field will inevitably be disturbed by noise, it is necessary to improve the ratio of signal to noise in the signal system, that is, the signal-to-noise ratio.

[0003] In order to improve the signal-to-noise ratio of the seismic data, the incoherent noise in the acquired seismic signal data can be attenuated. The singular spectrum analysis method can detect the low-rank structure of the data. Based on the different performances of the seismic signal and random noise on the singular spectrum, this method can effectively suppress the seismic random noise. However, since the quadratic fitting curve of the singular spectrum analysis method 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 the weighting criterion based on the absolute difference cannot identify and suppressing outliers, it is easy to cause a large amount of noise energy to remain or be considered interference, the denoising effect of seismic data is not obvious, and the signal-to-noise ratio is not high, and if n (n>2) iterations are used to obtain better results. The denoising effect will also lead to lower data processing efficiency due to the larger computational cost required by the iterative step, especially when the robust function is difficult to approximate.

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

SUMMARY OF THE INVENTION

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

For solving above-mentioned technical problem, the embodiment of the present invention provides following technical scheme:

Embodiment of the present invention provides a kind of data processing method of seismic signal on the one hand, comprising:

Utilize singular spectrum analysis method to filter original seismic signal data, obtain initial filtering data;

determining differences and similarities between the original seismic signal data and the initial filtered data;

Calculate weighting matrix based on described difference, described similarity, preset weighted intensity control parameter and local similarity threshold; Described weighted intensity control parameter is used for predicting the energy decay of unstable disturbance, and described local similarity The sex threshold is used to determine the degree of signal retention;

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

Optionally, described determining the difference and similarity between the original seismic signal data and the initial filtering data, including:

Call absolute deviation calculating relational formula to calculate the absolute deviation U between described original seismic signal data and described initial filtering data, U=M, j, and described absolute deviation calculating relational formula is: qi ,= ratio-phoenix , %, j is the element at the position (i, j) in the matrix corresponding to the absolute deviation, (1b is the element at the position (i, j) in the matrix corresponding to the original seismic signal data

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

Optionally, described determining the difference and similarity between the original seismic signal data and the initial filtering data, including:

Call local similarity calculation relational formula to calculate the local similarity V between described original seismic signal data and described initial filtering data, V= , described local similarity calculation relational formula is:

[0016] D / = 1 β only ",; v (^Σ you but two)(^Σ J

In formula, Vj, j is the element at (i, j) position in the local similarity corresponding matrix, 3 is local smooth window function, t is current position (i, j) moves T units along i, k move k units along j for the current position (i, j), d_T, K is the element at the position (t, k) in the corresponding matrix of the original seismic signal data, and 4M is the element at the position (t, k) in the corresponding matrix of the initial filtered data.

Optionally, described based on described difference, described similarity, preset weighted intensity control parameter and local similarity threshold calculation weighted matrix, including:

Call weighted element calculation relational formula to calculate described weighted matrix, and described weighted element calculation relational formula is:_r , (u;, / £ya, u, ; < sandVi ; > η

[0020] j = a ' J ' ':

1, otherwise

In formula, Wj, j is the element of described weighting matrix at position (i, j) place, see, j is the absolute deviation corresponding matrix for reflecting described difference at position (i, j) place The element is not the normalized absolute deviation, Vj, j is the local similarity corresponding matrix used to reflect the similarity The element h at the position (i, j) is the local similarity threshold, p is the local similarity threshold Weighted intensity control parameter.

Optionally, the described original seismic signal data is filtered again based on the weighting matrix, including:

Calculate the Hadamard product of described weighting matrix to described original seismic signal data, obtain a seismic signal modification data;

Along time direction, carry out forward one-dimensional Fourier transform to described primary seismic signal modification data, obtain frequency slice;

Carry out Hankel matrix embedding operation, rank reduction operation, averaging operation successively to described frequency slice, obtain secondary seismic signal modification data;

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

Embodiment of the present invention provides a kind of data processing device of seismic signal on the other hand, comprising:

Initial filtering module, for utilizing singular spectrum analysis method to filter original seismic signal data, obtain initial filtering data;

Parameter calculation module, for determining the difference and similarity between the original seismic signal data and the initial filtering data;

Weight calculation module, for calculating weighted matrix based on described difference, described similarity, preset weighted intensity control parameter and local similarity threshold; Described weighted intensity control parameter is used for predicting the energy of unstable disturbance decline

minus, the local similarity threshold is used to determine the degree of signal retention;

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

Optionally, described parameter calculation module is further used for: calling absolute deviation calculation relational formula to calculate the absolute deviation U between the original seismic signal data and the initial filtering data, U=[%,j], The absolute deviation calculation relationship can be expressed as: %-edge j is the element at the (i, j) position in the absolute deviation corresponding matrix, and k is the (i, j) position in the original seismic signal data corresponding matrix % is the element at the position (i, j) in the matrix corresponding to the initial filter data.

Embodiments of the present invention also provide a kind of electronic equipment, comprising a processor, when the processor is used for executing the computer program stored in the memory, realizes the steps of the data processing method of the seismic signal as described in any preceding item.

The embodiment of the present invention also provides a kind of readable storage medium at last, and a computer program is stored on the described readable storage medium, and when the computer program is executed by the processor, the data of the seismic signal as described in any preceding item is realized. The steps of the processing method.

The advantage of the technical scheme that the application provides is, utilize the weighted matrix that the difference between the first filtering data and the original seismic signal data and the similarity calculation obtains, can accurately predict the position and the energy level of unstable disturbance , based on the weighted matrix, the original seismic signal data is filtered twice, which can eliminate the unstable 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 does not require large computational costs. , which can suppress seismic noise more efficiently and stably.

Besides, the embodiment of the present invention also provides corresponding realization device, electronic device and readable storage medium for the data processing method of seismic signal, further makes the method more practical, and the device, electronic device and readable storage media have corresponding advantages.

[0037] It is to be understood that the foregoing general description and the following detailed description are exemplary only and do not limit the present disclosure.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention or related technologies more clearly, the accompanying drawings that need to be used in the descriptions of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

Fig. 1 is the schematic flow chart of the data processing method of a kind of seismic signal provided by the embodiment of the present invention;

Fig. 2 is a kind of specific implementation structure diagram of the data processing device of the seismic signal that the embodiment of the present invention provides;

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

Detailed ways

[0042] In order to make those skilled in the art better understand the solution of the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, but not all, embodiments of the present invention. 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.

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[0043] The terms "first", "second", "third", "fourth" and the like in this application and the claims and the above drawings are used to distinguish different objects, rather than to describe a particular Order. Furthermore, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or elements is not limited to the listed steps or elements, but may include unlisted steps or elements.

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

First referring to Fig. 1, Fig. 1 is the schematic flow sheet of the data processing method of a kind of seismic signal that the embodiment of the present invention provides, the embodiment of the present invention can comprise the following content:

S101: utilize singular spectrum analysis method to filter original seismic signal data, obtain initial filtering data.

In this step, the singular spectrum analysis method is any existing method for studying nonlinear time series data, and it can construct a trajectory matrix according to the observed time series, and decompose the trajectory matrix, repeat the process. In order 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 the Hankel matrix embedding operation, the rank reduction operation, and the 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, and obtains a Hankel matrix of size (nx-m+l) Xm as follows. The value of each element of the Hankel matrix and the direction of the matrix sub-diagonal The values of the above elements are equal. m is a predefined integer, and the value of m should make the Hankel matrix f approximate to a square matrix, such as mountain=&-&/2, where " represents the integer part of the parameter. The Hankel matrix f can be expressed as:

D(3,(d)

[0050] The singular spectrum analysis method assumes that the seismic data can be viewed 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 removes noise by reducing the rank of f, that is, performing singular value decomposition (SVD):

<img file="CN113687421B_D0001.tif" />

7^(H^ω) - ；

In formula, ()n represents the Hermitian transpose of matrix. Pen, U: and VL denote the top K largest singular values of matrix f and the associated K singular vectors. Near a represents a low-rank approximation of y. Finally, the singular spectrum analysis method averages the matrix or the antidiagonal based on the relational expression to recover the filtered data. In the formula, r represents the averaging operator. The filtering method of the singular spectrum analysis method can be realized based on law=2(left(ma(D))), where bar, 2, and island represent the averaging operator, the rank reduction operator and the Hankelization operator, respectively.

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[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 to represent, and this application does not make any limitation.

[0056] S103: Calculate the weighting matrix based on the difference, similarity, the preset weighted strength control parameter and the local similarity threshold.

In order to accurately predict the position and energy level of the unstable disturbance, the application will preset multiple parameters, including but not limited to the weighted intensity control parameter P that can be used to predict the energy decay of the unstable disturbance, for determining the signal retention A local similarity threshold ή of the degree, a constant k that depends on the number of plane waves contained in the processing window, a constant value used to determine the strength of the unstable perturbation, and the denoising strength of this embodiment decreases with decreasing Ke and k. enhanced, with the increase of P. The reference values for the parameter Ph and human may be, for example, 5, 0.6 and 4, respectively. In actual processing, its value may fluctuate up and down according to specific problems, which does not affect the implementation of the present application. According to the similarities and differences between the initial filtered data and the original seismic signal, combined with parameters that determine the energy attenuation and signal retention of unstable disturbances, a weighting matrix that can predict the location and energy level of unstable disturbances can be obtained.

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

This step is based on the weighted matrix that last step calculates gained, and original seismic signal data is processed again, can first calculate the Hadamard product of weighted matrix to original seismic signal data, also about the matrix corresponding to weighted matrix and original seismic signal data The corresponding position elements are multiplied to obtain the modified data of a seismic signal. In the time direction, forward one-dimensional Fourier transform is performed on the modified data of a seismic signal to obtain frequency slices. According to the method of S101, the Hankel matrix embedding operation, the rank reduction operation and the averaging operation can be sequentially performed on the frequency slice to obtain the modified data of the secondary seismic signal. In the direction of time, inverse 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.

In the technical scheme that the embodiment of the present invention provides, utilize the weighted matrix that the difference and similarity between the first filtering data and the original seismic signal data are calculated to obtain, can accurately predict the position and energy of unstable disturbance. Based on the weighted matrix, the original seismic signal data is filtered twice, 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, so that seismic noise can be suppressed more efficiently and stably.

It should be noted that there is no strict sequential execution order between the steps in this application, as long as the order is in line with logic, then these steps can be executed simultaneously, also can be executed according to a certain preset order, Fig. 1 is only a This is a schematic way, and it does not mean that it can only be executed in this order.

In the above-described embodiment, how to perform steps S102 and S103 is not limited, a kind of calculation method of weighting matrix is provided in the present embodiment, and can include the following steps:

First, can call absolute deviation calculation relational formula to calculate the absolute deviation U between original seismic signal data and initial filtering data, U=, absolute deviation calculation relational formula can be expressed as: w lamp=%-,%, j is The absolute deviation corresponds to the element at the position (i, j) in the matrix, di i is the element at the position (i, j) in the matrix corresponding to the original seismic signal data, and B is the position (i, j) in the matrix corresponding to the initial filtered data. Elements.

Secondly, the local similarity calculation relational expression can be called to calculate the local similarity V between the original seismic signal data and the initial filtering data, V=M,j, and the local similarity calculation relational formula can be expressed as:

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[0065]

J Wu^ωϊ-τ,ϊ-κ^τ,κ )

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

Finally, the weighted element calculation relation can be called to calculate the weighted matrix for representing the position and amplitude of the unstable disturbance, the grid points (i, j) of the original seismic signal data are polluted by the unstable disturbance when Wj , The smaller the value of j, the stronger the disturbance. The weighted element calculation relation can be expressed as: ' w, ; &lt; eandv.; &gt; ding

%=one '/':

1, otherwise

In formula, Wj j is the element of weighting matrix at position (i, j) place, L j is the element at (i; j) position in the absolute deviation U corresponding matrix for reflecting difference / is normalized The calculation method of the absolute deviation / can be g = ian|U-median(U)|) Ba· is the local similarity correspondence matrix used to reflect the similarity (i , j) @6745' J^1J The element E at the position is the local similarity threshold, and P is the weighted intensity control parameter.

In the present embodiment, the position and the amplitude of the unstable disturbance can be predicted by the mj finally calculated, and the original seismic signal data is filtered again based on the position and the amplitude of the stable disturbance that can be predicted, and noise can be removed to the greatest extent. , to improve 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 data processing method of the seismic signal, which further makes the method more practical. Wherein, the device can be described from the perspective of functional modules and the perspective of hardware. The following describes the seismic signal data processing apparatus provided by the embodiments of the present invention. The seismic signal data processing apparatus described below and the seismic signal data processing method described above may refer to each other correspondingly.

Based on the angle of functional module, referring to Fig. 2, Fig. 2 is the structure diagram of the data processing device of the seismic signal provided by the embodiment of the present invention under a kind of specific implementation, this device can comprise:

Initial filtering module 201, for utilizing singular spectrum analysis method to filter original seismic signal data, obtain initial filtering data;

Parameter calculation module 202, for determining the difference and similarity between original seismic signal data and initial filtering data;

Weight calculation module 203, for calculating weighted matrix based on difference, similarity, preset weighted intensity control parameter and local similarity threshold; weighted intensity control parameter is used to predict the energy decay of unstable disturbance, and local similarity The threshold is used to determine the degree of signal retention;

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

Optionally, in some implementations of this embodiment, the above-mentioned parameter calculation module 202 may include an absolute deviation calculation unit and a similarity calculation unit;

Wherein, absolute deviation calculation unit can be used for: call absolute deviation calculation relational formula to calculate original seismic signal number

According to the absolute deviation u,u= u,j] from the initial filtering data, the absolute deviation calculation relationship can be expressed as: =|%-41, dew,j is the element at the position (i,j) in the absolute deviation corresponding matrix ,k is the element at the position (i, j) in the matrix corresponding to the original seismic signal data, and b is the element at the position (i, j) in the matrix corresponding to the initial filtered data.

Similarity computing unit can be used for: calling local similarity computing relational expression to calculate local similarity V between original seismic signal data and initial filtering data, V=MJ, and local similarity computing relational expression is:

[0080]

In the formula, Vjj is the element at (i, j) position in the local similarity corresponding matrix, 3 is the local smoothing window function, t is the current position (i, j) moves T units along i, k move k units along j for the current position (i, j), d_T h is the element at the position (t, k) in the matrix corresponding to the original seismic signal data, and 4M is the element at the position (t, k) in the matrix corresponding to the initial filtered data.

Optionally, in other implementations of this embodiment, the above-mentioned weight calculation module 203 can be further used for: Call the weighted element calculation relational formula to calculate the weighted matrix, and the weighted element calculation relational formula is:

[0083] / , % / &lt; gandv-&gt; "

1, otherwise

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

'Optionally, in some other implementations of this embodiment, the above-mentioned 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, carry out forward one-dimensional Fourier transform to the modified data of a seismic signal to obtain a frequency slice;

Carry out Hankel matrix embedding, rank reduction, averaging operation respectively to frequency slice, obtain secondary seismic signal modification data;

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

The function of each functional module of the data processing apparatus of the seismic signal of the embodiment of the present invention can be specifically realized according to the method in the above-mentioned method embodiment, and its concrete realization process can refer to the relevant description of the above-mentioned method embodiment, and repeat no more here. .

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

[0092] The data processing apparatus for seismic signals mentioned above is described from the perspective of functional modules, and further, the present 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 provided in an embodiment of the present 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 executing the computer program.

Wherein, the processor 31 may include one or more processing cores, such as a 4-core processor, an 8-core processor,

The processor 31 can also be a controller, a microcontroller, a microprocessor, or other data processing chips. The processor 31 may adopt at least one hardware form among DSP (Digital Signal Processing, digital signal processing), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array, 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 wake-up state, also called a CPU (Central Processing Unit, central processing unit); the coprocessor is a A low-power processor for processing data in a 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 the 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, where the AI processor is used to process computing operations related to machine learning.

[0094] Memory 30 may include one or more computer-readable storage media, which may be non-transitory. Memory 30 may also include high-speed random access memory as well as non-volatile memory, such as one or more magnetic disk storage devices, flash storage devices. The memory 30 may in some embodiments 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 Media Card (SMC), a Secure Digital (SD) card, a flash memory Card (Flash Card), etc. Further, the memory 30 may also include both an internal storage unit of the electronic device and an external storage device. The memory 30 can not only be used to store application software installed in the electronic device and various types of data, such as code of a program executing the vulnerability processing method, etc., but also can be used to temporarily store data that has been output or will be output. In this embodiment, the memory 30 is at least used to store the following computer program 301, wherein, after the computer program is loaded and executed by the processor 31, the relevant steps of the seismic signal data processing method disclosed in any of the foregoing embodiments can be implemented. In addition, the resources stored in the memory 30 may also include the operating system 302 and data 303, etc. The method can be short-term storage or permanent storage. The operating system 302 may include Windows, Unix, Linux, and the like. 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 and output interface 33 such as the keyboard (Keyboard) belong to the user interface, and the optional user interface may also include a standard wired interface, a wireless interface, and the like. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, and the like. The display, also appropriately referred to as a display screen or display unit, is used to display information processed in the electronic device and to display a visual 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., and is generally used to establish a communication connection between an electronic device and other electronic devices. The communication bus 36 may be a peripheral component interconnect (PCI for short) bus or an extended industry standard architecture (EISA for short) bus or the like. The bus can be divided into address bus, data bus line, control bus, etc. For ease of presentation, only one thick line is used in FIG. 3, 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 to the electronic device, and may include more or less components than those shown in the figure, for example, may also include sensors that implement various functions 37.

[0097] The functions of each functional module of the electronic device according to 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, which is not repeated here.

As can be seen from the above, the embodiment of the present invention effectively improves the signal-to-noise ratio of the seismic signal, and can be more efficient and more stable.

Seismic noise is suppressed locally.

[0099] It can be understood that, if the seismic signal data processing method in the above-mentioned 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 such understanding, the technical solution of the present application can be embodied in the form of a software product , and the computer software product is stored in a storage medium , perform all or part of the steps of the methods of the various embodiments of the present application. And the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), electrically erasable programmable ROM, register, hard disk, multimedia Cards, card-type memories (such as SD or DX memories, etc.), magnetic memories, removable disks, CD-ROMs, magnetic disks, or optical disks, etc., are various media that can store program codes.

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

[0101] The function of each functional module of the readable storage medium according to 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, which is not repeated here. .

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

Professionals may further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two, in order to clearly illustrate the hardware and Software interchangeability, the above description has generally described the components and steps of each example in terms of functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of the present invention.

[0104] The data processing method, device, electronic device, and readable storage medium for a seismic signal provided by the present application have been described in detail above. The principles and implementations of the present invention are described herein by using specific examples, and the descriptions of the above embodiments are only used to help understand the method and the core idea of the present invention. It should be pointed out that for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can also be made to the present application, and these improvements and modifications also fall within the protection scope of the claims of the present application.