Seismic data processing method and device, equipment and medium

Abstract

The invention provides a seismic data processing method and device, equipment and a medium, and belongs to the field of oil exploration. The method comprises the following steps: based on the distance between shot points and detection point arrays, determining nearest detection point arrays respectively corresponding to a plurality of shot points from seismic data of a target area; for each shot point in the plurality of shot points, determining the number of strong energy channels corresponding to the shot point based on the seismic data collected by the recently received arrangement corresponding to the shot point; on the basis of the strong energy channel number corresponding to each shot point in each sub-region, the subareas of different earth surfaces in the target region are readjusted, and a plurality of adjusted sub-regions are obtained; and based on the plurality of adjusted sub-regions, carrying out differential denoising on the seismic data of the target region to obtain denoised seismic data. According to the scheme, partitioning can be carried out more accurately, and the denoising effect of the seismic data is improved.

Description

Technical Field

[0001] The present application relates to the field of petroleum exploration, and in particular to a method, device, equipment and medium for processing seismic data. Background Art

[0002] In recent years, with the continuous improvement of oil and gas exploration technology, efficient lithologic exploration in medium and deep layers has received more and more attention. The identification and prediction of lithologic exploration targets have very high requirements on the fidelity of seismic data. However, during the entire propagation process of seismic waves from the excitation point to the receiving point, the main energy of the source is concentrated near the near offset of the shot point, resulting in the imaging process. The effective signal of the deep layer is completely submerged in the strong energy noise of the shallow layer. Therefore, the denoising of seismic data is very important. Since the work area of ​​oil and gas exploration is usually large and contains various types of surfaces, when denoising seismic data, it is usually differentiated according to the surface partition in the electronic map to improve the fidelity of the seismic data. However, it is found in actual applications that the partition of different surfaces in the electronic map is not accurate, resulting in poor denoising effect of seismic data. Summary of the invention

[0003] The present application provides a method, device, equipment and medium for processing seismic data, which can more accurately partition and improve the denoising effect of seismic data. The technical solution is as follows:

[0004] In one aspect, a method for processing seismic data is provided, the method comprising:

[0005] Based on the distance between the shot point and the detection point arrangement, the nearest detection point arrangement corresponding to the multiple shot points is determined from the seismic data of the target area, the nearest detection point arrangement corresponding to any shot point is the detection point arrangement closest to the shot point among the multiple detection point arrangements corresponding to the shot point, the target area includes multiple sub-areas with different surface types, and multiple shot points are distributed in each sub-area;

[0006] For each shot point among the plurality of shot points, based on seismic data collected by the nearest detection point arrangement corresponding to the shot point, determine the number of strong energy channels corresponding to the shot point, the number of strong energy channels being the number of seismic channels with energy intensity greater than an intensity threshold, one seismic channel corresponding to one detection point in the detection point arrangement and the shot point corresponding to the detection point arrangement;

[0007] Based on the number of strong energy channels corresponding to each shot point in each sub-area, re-adjusting the partitions of different surfaces in the target area to obtain a plurality of adjusted sub-areas, so that the difference of the number of strong energy channels corresponding to different shot points in the adjusted sub-areas does not exceed a threshold value of the number of strong energy channels;

[0008] Based on the multiple adjusted sub-areas, differential denoising is performed on the seismic data of the target area to obtain denoised seismic data.

[0009] In a possible implementation, based on the number of strong energy channels corresponding to each shot point in each sub-area, the partitions of different surfaces in the target area are readjusted to obtain a plurality of adjusted sub-areas, including:

[0010] For any sub-region, the strong energy trace number corresponding to the reference shot point in the sub-region is determined as the reference trace number;

[0011] Based on the strong energy trace number corresponding to each shot point in each sub-area and the reference trace number, determining a shot point whose difference between the strong energy trace number and the reference trace number does not exceed the strong energy trace number threshold;

[0012] Based on the reference shot points and the determined shot points, an adjusted sub-region is determined, the adjusted sub-region including the reference shot points and the determined shot points.

[0013] In a possible implementation, the method further includes any of the following:

[0014] Determine a shot point located at a central position in the sub-area as a reference shot point;

[0015] Determine the shot point closest to the center position in the sub-area as the reference shot point;

[0016] A shot point distribution interface of the target area is displayed, wherein the shot point distribution interface includes a plurality of shot points distributed in the target area; in response to a selection operation of any shot point in the shot point distribution interface, the shot point is determined as a reference shot point of the sub-area where the shot point is located.

[0017] In a possible implementation, for each shot point among the multiple shot points, determining the number of strong energy traces corresponding to the shot point based on seismic data collected by the nearest detection point arrangement corresponding to the shot point includes:

[0018] For each of the multiple shot points, autocorrelation processing is performed on seismic data collected by the nearest detection point arrangement corresponding to the shot point to obtain amplitudes of multiple seismic traces formed by the shot point and the nearest detection point arrangement;

[0019] The number of strong energy traces corresponding to the shot point is determined based on the amplitudes of the multiple seismic traces.

[0020] In a possible implementation, the method further includes:

[0021] For each of the multiple shot points, multiple seismic data segments with equal time intervals are selected from the seismic data collected by arranging the nearest detection point corresponding to the shot point in time sequence; based on each seismic data segment, the number of strong energy channels corresponding to the shot point in multiple time periods is determined; based on the change rule of the number of strong energy channels of the shot point in different time periods, the surface type corresponding to the shot point is determined, and the surface type corresponding to the shot point is used to indicate the surface type of the location of the shot point;

[0022] For any adjusted sub-region, determining the surface type of the adjusted sub-region based on the surface type corresponding to the shot points in the adjusted sub-region;

[0023] The step of performing differential denoising on the seismic data of the target area based on the multiple adjusted sub-areas to obtain denoised seismic data includes:

[0024] Based on the multiple adjusted sub-regions and the surface types of the multiple adjusted sub-regions, differential denoising is performed on the seismic data of the target region to obtain the denoised seismic data.

[0025] In a possible implementation, determining the surface type corresponding to the shot point based on the variation rule of the strong energy trace number of the shot point in different time periods includes:

[0026] Obtaining a ratio of the number of strong energy channels corresponding to the shot point in the first time period to the number of strong energy channels corresponding to the shot point in the second time period to obtain a first ratio;

[0027] Obtaining a ratio of the number of strong energy channels corresponding to the shot point in the second time period to the number of strong energy channels corresponding to the shot point in the third time period to obtain a second ratio, wherein the first time period, the second time period and the third time period are arranged in order from front to back;

[0028] Determine the difference between the first ratio and the second ratio as a first relationship coefficient, wherein the relationship coefficient is used to indicate the strength of the relationship between the number of strong energy channels and noise;

[0029] Based on the first relationship coefficient and the corresponding relationship between the relationship coefficient and the surface type, the surface type corresponding to the first relationship coefficient is determined as the surface type corresponding to the shot point.

[0030] In another aspect, a seismic data processing device is provided, the device comprising:

[0031] A first determination module is used to determine the nearest detection point arrangements corresponding to the multiple shot points respectively from the seismic data of the target area based on the distance between the shot point and the detection point arrangement, the nearest detection point arrangement corresponding to any shot point is the detection point arrangement with the shortest distance to the shot point among the multiple detection point arrangements corresponding to the shot point, the target area includes multiple sub-areas with different surface types, and multiple shot points are distributed in each sub-area;

[0032] A second determination module is used to determine, for each shot point among the multiple shot points, the number of strong energy channels corresponding to the shot point based on the seismic data collected by the nearest detection point arrangement corresponding to the shot point, wherein the number of strong energy channels is the number of seismic channels with energy intensity greater than an intensity threshold, and one seismic channel corresponds to one detection point in the nearest detection point arrangement and the shot point corresponding to the nearest detection point arrangement;

[0033] An adjustment module, for re-adjusting the partitions of different surfaces in the target area based on the number of strong energy channels corresponding to each shot point in each sub-area to obtain a plurality of adjusted sub-areas, so that the difference of the number of strong energy channels corresponding to different shot points in the adjusted sub-areas does not exceed a threshold value of the number of strong energy channels;

[0034] The denoising module is used to perform differential denoising on the seismic data of the target area based on the multiple adjusted sub-areas to obtain denoised seismic data.

[0035] In a possible implementation, the adjustment module is used to determine, for any sub-region, the number of strong energy traces corresponding to a reference shot point in the sub-region as the reference trace number; determine, based on the number of strong energy traces corresponding to each shot point in each sub-region and the reference trace number, a shot point whose difference between the number of strong energy traces and the reference trace number does not exceed the threshold value of the number of strong energy traces; and determine, based on the reference shot point and the determined shot point, an adjusted sub-region, wherein the adjusted sub-region includes the reference shot point and the determined shot point.

[0036] In a possible implementation manner, the apparatus further includes a third determining module, and the third determining module is configured to point to any one of the following items:

[0037] Determine a shot point located at a central position in the sub-area as a reference shot point;

[0038] Determine the shot point closest to the center position in the sub-area as the reference shot point;

[0039] A shot point distribution interface of the target area is displayed, wherein the shot point distribution interface includes a plurality of shot points distributed in the target area; in response to a selection operation of any shot point in the shot point distribution interface, the shot point is determined as a reference shot point of the sub-area where the shot point is located.

[0040] In one possible implementation, the second determination module is used to perform autocorrelation processing on the seismic data collected by the nearest detection point arrangement corresponding to each shot point among the multiple shot points, to obtain the amplitudes of multiple seismic traces formed by the shot point and the nearest detection point arrangement; and determine the number of strong energy traces corresponding to the shot point based on the amplitudes of the multiple seismic traces.

[0041] In a possible implementation manner, the device further includes:

[0042] A fourth determination module is used to select, for each of the multiple shot points, multiple seismic data segments with equal time intervals from the seismic data collected by the nearest detection point corresponding to the shot point in chronological order; determine the number of strong energy channels corresponding to the shot point in multiple time periods based on each seismic data segment; determine the surface type corresponding to the shot point based on the change rule of the strong energy channel number of the shot point in different time periods, and the surface type corresponding to the shot point is used to indicate the surface type of the location of the shot point;

[0043] A fifth determination module, configured to determine, for any adjusted sub-region, a surface type of the adjusted sub-region based on the surface types corresponding to the shot points in the adjusted sub-region;

[0044] The denoising module is used to perform differential denoising on the seismic data of the target area based on the multiple adjusted sub-areas and the surface types of the multiple adjusted sub-areas to obtain the denoised seismic data.

[0045] In a possible implementation, the fourth determination module is used to obtain a ratio of the number of strong energy channels corresponding to the shot point in a first time period to the number of strong energy channels corresponding to the shot point in a second time period to obtain a first ratio; obtain a ratio of the number of strong energy channels corresponding to the shot point in the second time period to the number of strong energy channels corresponding to the shot point in a third time period to obtain a second ratio, the first time period, the second time period and the third time period are arranged in chronological order from front to back; determine the difference between the first ratio and the second ratio as a first relationship coefficient, the relationship coefficient is used to indicate the strength of the relationship between the number of strong energy channels and noise; based on the first relationship coefficient and the correspondence between the relationship coefficient and the surface type, determine the surface type corresponding to the first relationship coefficient as the surface type corresponding to the shot point.

[0046] On the other hand, a computer device is provided, which includes a processor and a memory, wherein the memory stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the method for processing seismic data as described in any of the above implementations.

[0047] On the other hand, a computer-readable storage medium is provided, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to implement the method for processing seismic data as described in any of the above implementations.

[0048] On the other hand, a computer program product is provided, the computer program product comprising at least one program code, the at least one program code being loaded and executed by a processor to implement the method for processing seismic data as described in any of the above implementations.

[0049] The beneficial effects of the technical solution provided by the embodiments of the present application include at least:

[0050] The embodiment of the present application provides a method for processing seismic data. Taking into account the different energy losses of seismic waves propagating on different types of surfaces, the target area is partitioned according to the number of strong energy channels of shot points at different positions in the target area. The partitioning is in line with the energy loss of seismic waves propagating on different types of surfaces. Therefore, the partitioning is more accurate, and thus the seismic data can be differentiated and denoised more accurately, thereby improving the denoising effect of the seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 is a flow chart of a method for processing seismic data provided by an embodiment of the present application;

[0053] Figure 2 is a flow chart of a method for processing seismic data provided by an embodiment of the present application;

[0054] Figure 3 is a comparison diagram of denoised seismic data provided by an embodiment of the present application;

[0055] Figure 4 is a structural schematic diagram of a seismic data processing device provided in an embodiment of the present application;

[0056] Figure 5 is a structural schematic diagram of a seismic data processing device provided in an embodiment of the present application;

[0057] Figure 6 is a schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0058] Figure 7 It is a structural diagram of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0060] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof 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 optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0061] The processing method of seismic data provided in the embodiment of the present application is performed by a computer device. In some embodiments, the computer device can be a terminal, and the terminal can be any type of terminal such as a mobile phone, a tablet computer, a laptop computer, a desktop computer, etc., and the embodiment of the present application does not limit the type of terminal. In other embodiments, the computer device can be a server, and the server can be a server, or a server cluster composed of several servers, or a cloud computing service center. Of course, the server can also include other functional servers to provide more comprehensive and diversified services. In other embodiments, the computer device includes a terminal and a server. It should be noted that the embodiment of the present application is only an exemplary description of the execution subject of the processing method of seismic data, and does not limit the execution subject. It should be noted that the processing method of seismic data provided in the embodiment of the present application can be performed by a terminal and a server in cooperation, and the terminal and the server do not limit which steps are specifically performed.

[0062] Figure 1 is a flowchart of a method for processing seismic data provided in an embodiment of the present application. The present application embodiment is illustrated by taking a computer device as an example. Figure 1 , the method comprising:

[0063] 101. The computer device determines the nearest detection point arrangements corresponding to multiple shot points from the seismic data of the target area based on the distance between the shot point and the detection point arrangement. The nearest detection point arrangement corresponding to any shot point is the detection point arrangement that is closest to the shot point among the multiple detection point arrangements corresponding to the shot point. The target area includes multiple sub-areas with different surface types, and multiple shot points are distributed in each sub-area.

[0064] During the oil and gas exploration process, multiple shot points and multiple detection point arrangements can be set up in the work area. Any detection point arrangement includes multiple detection points located in the same arrangement. After the seismic wave is excited by any shot point, the seismic wave is received based on the multiple detection point arrangements. In the embodiment of the present application, when the nearest detection point arrangement corresponding to the multiple shot points is determined from the seismic data of the target area, the multiple shot points can be all the shot points in the target area or part of the shot points, and the embodiment of the present application does not limit this. Since the embodiment of the present application is to partition the target area, it is only necessary to ensure that the multiple shot points are distributed in multiple different positions in the target area, and each sub-area in the target area is distributed with multiple shot points.

[0065] It should be noted that the multiple sub-areas with different surface types in the above step 101 can be determined based on an electronic map, or can be determined by equally dividing the target area. Since the target area contains different types of surfaces, after the target area is divided into multiple sub-areas, the surface types of the multiple sub-areas are not exactly the same.

[0066] Among them, the surface type can be a Gobi type, a small desert type, a large desert type, a grassland type, etc. The embodiment of the present application does not limit the surface type.

[0067] 102. For each shot point among the multiple shot points, the computer device determines the number of strong energy channels corresponding to the shot point based on the seismic data collected by the nearest detection point arrangement corresponding to the shot point. The number of strong energy channels is the number of seismic channels with energy intensity greater than the intensity threshold. One seismic channel corresponds to one detection point in the detection point arrangement and the shot point corresponding to the detection point arrangement.

[0068] A seismic channel corresponds to a detection point in a detection point arrangement and a shot point corresponding to the detection point arrangement means: a detection point and a shot point constitute a seismic channel, and the seismic data of the seismic channel is the data excited from the shot point and received at the detection point.

[0069] The intensity threshold can be any value, and the embodiment of the present application does not limit the intensity threshold. Optionally, the intensity threshold is a preset value; optionally, the intensity threshold is an empirical value. Since the number of strong energy channels is the number of seismic channels with energy intensity greater than the intensity threshold, the number of strong energy channels can also be regarded as the number of strong energy channels corresponding to the shot point.

[0070] 103. The computer device readjusts the partitions of different surfaces in the target area based on the strong energy channel number corresponding to each shot point in each sub-area to obtain a plurality of adjusted sub-areas, so that the difference in the strong energy channel number corresponding to different shot points in the adjusted sub-areas does not exceed the strong energy channel number threshold.

[0071] Since different types of surfaces have different effects on seismic waves, the strong energy channel numbers of shot points located on different types of surfaces will also be different. Therefore, the embodiment of the present application readjusts the partitioning of different surfaces in the target area based on the strong energy channel numbers corresponding to each shot point in each sub-area, so that the strong energy channel numbers corresponding to different shot points in each sub-area are similar, and the noise impact of the location of the shot point in each sub-area is also similar, ensuring the consistency of surface types at multiple locations in the sub-area.

[0072] 104. The computer device performs differential denoising on the seismic data of the target area based on the multiple adjusted sub-areas to obtain denoised seismic data.

[0073] Since different types of surfaces have different effects on the propagation of seismic waves, it is necessary to perform differential denoising on the seismic data of different sub-regions in order to obtain accurate seismic data. Among them, differential denoising on the seismic data of different sub-regions can be: for sub-regions with different surface types, different denoising methods are used to denoise the seismic data of the sub-regions; it can also be: for sub-regions with different surface types, different noise suppression parameters are used to denoise the seismic data of the sub-regions. Of course, the differential denoising can also be other types of differential denoising, and the embodiment of the present application does not limit the differential denoising.

[0074] After performing differential denoising on the seismic data of the target area, the computer device may also perform stacking processing, deconvolution processing, interpretation processing, etc. on the denoised seismic data. The embodiment of the present application does not limit the application of the denoised seismic data.

[0075] The method for processing seismic data provided in the embodiment of the present application takes into account the different energy losses of seismic waves propagating on different types of surfaces. Therefore, the target area is partitioned according to the number of strong energy channels of shot points at different positions in the target area. The partitioning is in line with the energy loss of seismic waves propagating on different types of surfaces. Therefore, the partitioning is more accurate, and can more accurately perform differentiated denoising on the seismic data, thereby improving the denoising effect of the seismic data.

[0076] In a possible implementation, based on the number of strong energy channels corresponding to each shot point in each sub-area, the partitions of different surfaces in the target area are readjusted to obtain multiple adjusted sub-areas, including:

[0077] For any sub-region, the strong energy trace number corresponding to the reference shot point in the sub-region is determined as the reference trace number;

[0078] Based on the strong energy trace number and the reference trace number corresponding to each shot point in each sub-area, a shot point whose difference between the strong energy trace number and the reference trace number does not exceed a strong energy trace number threshold is determined;

[0079] Based on the reference shot point and the determined shot point, an adjusted sub-region is determined, and the adjusted sub-region includes the reference shot point and the determined shot point.

[0080] In a possible implementation, the method further includes any of the following:

[0081] The shot point located at the center of the sub-area is determined as the reference shot point;

[0082] The shot point closest to the center position in the sub-area is determined as the reference shot point;

[0083] A shot point distribution interface of the target area is displayed, the shot point distribution interface includes a plurality of shot points distributed in the target area; in response to a selection operation of any shot point in the shot point distribution interface, the shot point is determined as a reference shot point of the sub-area where the shot point is located.

[0084] In a possible implementation, for each shot point among the multiple shot points, based on the seismic data collected by arranging the nearest detection point corresponding to the shot point, the number of strong energy traces corresponding to the shot point is determined, including:

[0085] For each of the multiple shot points, autocorrelation processing is performed on the seismic data collected by the nearest detection point arrangement corresponding to the shot point to obtain the amplitudes of multiple seismic traces formed by the shot point and the nearest detection point arrangement;

[0086] Based on the amplitudes of multiple seismic traces, the number of strong energy traces corresponding to the shot point is determined.

[0087] In a possible implementation, the method further includes:

[0088] For each of the multiple shot points, multiple seismic data segments with equal time intervals are selected from the seismic data collected from the nearest detection point corresponding to the shot point in chronological order; based on each seismic data segment, the number of strong energy channels corresponding to the shot point in multiple time periods is determined; based on the change rule of the number of strong energy channels of the shot point in different time periods, the surface type corresponding to the shot point is determined, and the surface type corresponding to the shot point is used to indicate the surface type of the location of the shot point;

[0089] For any adjusted sub-region, based on the surface types corresponding to the shot points in the adjusted sub-region, determining the surface type of the adjusted sub-region;

[0090] Based on multiple adjusted sub-areas, the seismic data of the target area is differentially denoised to obtain denoised seismic data, including:

[0091] Based on the multiple adjusted sub-regions and the multiple adjusted surface types of the sub-regions, the seismic data of the target region are differentially denoised to obtain denoised seismic data.

[0092] In a possible implementation, the surface type corresponding to the shot point is determined based on the change rule of the strong energy trace number of the shot point in different time periods, including:

[0093] Obtaining a ratio of the number of strong energy traces corresponding to the shot point in the first time period to the number of strong energy traces corresponding to the shot point in the second time period, to obtain a first ratio;

[0094] Obtaining a ratio of the number of strong energy channels corresponding to the shot point in the second time period to the number of strong energy channels corresponding to the shot point in the third time period to obtain a second ratio, wherein the first time period, the second time period and the third time period are arranged in order from front to back in time;

[0095] The difference between the first ratio and the second ratio is determined as a first relationship coefficient, where the relationship coefficient is used to indicate the strength of the relationship between the number of strong energy channels and the noise;

[0096] Based on the first relationship coefficient and the corresponding relationship between the relationship coefficient and the surface type, the surface type corresponding to the first relationship coefficient is determined as the surface type corresponding to the shot point.

[0097] All the above optional technical solutions can be arbitrarily combined to form optional embodiments of the present application, which will not be described one by one here.

[0098] Figure 2 is a flowchart of a method for processing seismic data provided in an embodiment of the present application. The present application embodiment is illustrated by taking a computer device as an example. Figure 2 , the method comprising:

[0099] 201. The computer device determines the nearest detection point arrangements corresponding to multiple shot points from the seismic data of the target area based on the distance between the shot point and the detection point arrangement. The nearest detection point arrangement corresponding to any shot point is the detection point arrangement that is closest to the shot point among the multiple detection point arrangements corresponding to the shot point. The target area includes multiple sub-areas with different surface types, and multiple shot points are distributed in each sub-area.

[0100] In some embodiments, the computer device determines the nearest detection point arrangement corresponding to each of the plurality of shot points from the seismic data of the target area based on the distance between the shot point and the detection point arrangement, including: for any shot point in the target area, obtaining the distance between the shot point and each detection point arrangement of the shot point, and determining the detection point arrangement corresponding to the minimum distance as the nearest detection point arrangement corresponding to the shot point. The distance between the shot point and the detection point arrangement may be the vertical distance between the shot point and the detection point arrangement.

[0101] The step 201 is the same as the above step 101 and will not be described in detail here.

[0102] 202. For each shot point among the multiple shot points, the computer device selects, in chronological order, a plurality of seismic data segments with equal time intervals from the seismic data collected by arranging the nearest detection points corresponding to the shot point; based on each seismic data segment, the number of strong energy channels corresponding to the shot point in the multiple time periods is determined.

[0103] The duration of the seismic data segment can be any duration, such as 200 milliseconds, 300 milliseconds, etc., which is not limited in the present embodiment. The time interval between multiple seismic data segments can be any time interval, such as 1000 milliseconds, etc., which is not limited in the present embodiment.

[0104] For example, for any shot point, the computer device selects the seismic data segment from 1500 milliseconds to 1700 milliseconds, the seismic data segment from 2500 milliseconds to 2700 milliseconds, and the seismic data segment from 3500 milliseconds to 3700 milliseconds from the seismic data collected by the nearest detection point arrangement corresponding to the shot point.

[0105] In the embodiment of the present application, the number of strong energy channels is the number of strong energy seismic channels, and the energy strength is determined by the amplitude. Therefore, whether the seismic channel is a strong energy seismic channel can be determined based on the amplitude of the seismic channel.

[0106] In some embodiments, the computer device determines the number of strong energy channels corresponding to the shot point in multiple time periods based on each seismic data segment, including: for each seismic data segment, based on the seismic data segment, obtaining the amplitude of multiple seismic channels composed of the arrangement of the shot point and the nearest detection point, and determining the number of strong energy channels corresponding to the shot point based on the amplitude of the multiple seismic channels. For example, when the amplitude of the seismic channel is greater than the first amplitude threshold, the seismic channel is determined to be a strong energy seismic channel, and the number of strong energy channels is the number of seismic channels with amplitudes greater than the first amplitude threshold. Among them, the first amplitude threshold can be any value, and the embodiment of the present application does not limit the first amplitude threshold. Optionally, the first amplitude threshold is a preset value; optionally, the first amplitude threshold is an empirical value.

[0107] Assuming that the seismic data segment is recorded as x, the sub-correlation function of the seismic data segment can be expressed as:

[0108] r xx (τ) = E[x n x n+τ ];

[0109] Among them, r xx (τ) is the sub-correlation of the seismic data segment, E is the energy function, x n represents the earthquake data at the nth moment in the earthquake data segment, x n+τ Represents the seismic data at the n+τth moment in the seismic data segment.

[0110] Although the autocorrelation function r xx The waveform of (τ) seems to have no direct connection with the waveform of the seismic data segment x, but the autocorrelation function r xx (τ) is related to the frequency and amplitude contained in the seismic data segment, so the characteristics of the autocorrelation function reflect the energy and frequency characteristics of the seismic data segment to a certain extent. According to the stationary random signal analysis theory, the autocorrelation function is completely determined by its spectrum, and the spectrum is determined by the amplitude spectrum of the seismic data segment, that is, the energy spectrum of the seismic data segment is the spectrum of the autocorrelation function. Therefore, the computer device can obtain the amplitude of each seismic trace by autocorrelation processing. In some embodiments, the computer device determines the number of strong energy traces corresponding to the shot point in multiple time periods based on each seismic data segment, including: for each seismic data segment, the seismic data segment is subjected to autocorrelation processing to obtain the amplitudes of multiple seismic traces composed of the shot point and the nearest detection point, and based on the amplitudes of the multiple seismic traces, the number of strong energy traces corresponding to the shot point is determined.

[0111] Optionally, the computer device determines the number of strong energy channels corresponding to the shot point based on the amplitudes of multiple seismic channels, including: the computer device normalizes the amplitudes of multiple seismic channels to obtain reference amplitudes corresponding to the multiple seismic channels; if the reference amplitude of the seismic channel is greater than a second amplitude threshold, the seismic channel is determined to be a strong energy seismic channel; if the reference amplitude of the seismic channel is not greater than the second amplitude threshold, the seismic channel is determined to be a non-strong energy seismic channel; and the number of strong energy seismic channels is counted to obtain the number of strong energy channels.

[0112] The second amplitude threshold may be any value between 0 and 1, such as 0.5, etc. The embodiment of the present application does not limit the second amplitude threshold.

[0113] 203. The computer device determines the surface type corresponding to the shot point based on the variation rule of the strong energy channel number of the shot point in different time periods. The surface type corresponding to the shot point is used to indicate the surface type of the location of the shot point.

[0114] In the embodiment of the present application, if the number of strong energy channels at any shot point varies greatly in different time periods, the relationship between the number of strong energy channels and the noise is strong, and if the number of strong energy channels at any shot point varies little in different time periods, the relationship between the number of strong energy channels and the noise is weak. Noise is strongly correlated with the surface type, so the surface type can be determined based on the strength of the relationship between the number of strong energy channels and the noise.

[0115] In some embodiments, the variation rule of the strong energy channel number in different time periods is taken as the variation rule of the strong energy channel number in the first time period, the second time period and the third time period for exemplary description. The computer device determines the surface type corresponding to the shot point based on the variation rule of the strong energy channel number in different time periods, including: obtaining the ratio of the strong energy channel number corresponding to the shot point in the first time period to the strong energy channel number corresponding to the shot point in the second time period to obtain a first ratio; obtaining the ratio of the strong energy channel number corresponding to the shot point in the second time period to the strong energy channel number corresponding to the shot point in the third time period to obtain a second ratio, and the first time period, the second time period and the third time period are arranged in order from front to back in time; determining the difference between the first ratio and the second ratio as the first relationship coefficient, and the relationship coefficient is used to indicate the strength of the relationship between the strong energy channel number and the noise; based on the first relationship coefficient and the corresponding relationship between the relationship coefficient and the surface type, determining the surface type corresponding to the first relationship coefficient as the surface type corresponding to the shot point.

[0116] Among them, the first time period, the second time period and the third time period are arranged in chronological order from front to back. When the detection points are arranged to collect seismic data, the seismic wave signals reflected from the shallow layer will be collected first, and the seismic wave signals reflected from the deep layer will be collected later. Therefore, the number of strong energy channels corresponding to the shot points in the first time period, the second time period and the third time period reflects the change in the number of strong energy channels corresponding to the strata from shallow to deep, and can further reflect the strata type, that is, the surface type.

[0117] Among them, in the correspondence between the relationship coefficient and the surface type, one surface type can correspond to one or more relationship coefficients, or can correspond to a relationship coefficient interval, and the embodiment of the present application does not limit the correspondence.

[0118] 204. The computer device determines the number of strong energy channels corresponding to each shot point. The number of strong energy channels corresponding to each shot point is the sum of the number of strong energy channels corresponding to each shot point in different time periods.

[0119] The above steps 202 to 203 are to determine the law of the change of the strong energy channel number over time and further determine the surface type, so the seismic data is divided into multiple seismic data segments and the strong energy channel number corresponding to each seismic data segment is determined. The above steps 204 to 205 are to divide the target area into different surface zones. Therefore, there is no need to divide the seismic data into multiple seismic data segments, and the strong energy channel number of the seismic data corresponding to each shot point can be directly determined. The strong energy channel number is the sum of the strong energy channel numbers corresponding to the shot point in different time periods.

[0120] In the above step 204, when determining the number of strong energy channels corresponding to each shot point, the number of strong energy channels corresponding to the shot point determined in the above step 202 in multiple time periods can be added together, or the seismic data of the shot point can be directly processed as shown in the seismic data segment in step 202 to obtain the number of strong energy channels corresponding to the shot point, which is not limited in this embodiment of the present application.

[0121] 205. The computer device readjusts the partitions of different surfaces in the target area for the strong energy channel number corresponding to each shot point in each sub-area to obtain a plurality of adjusted sub-areas, so that the difference of the strong energy channel number corresponding to different shot points in the adjusted sub-areas does not exceed the strong energy channel number threshold.

[0122] In some embodiments, a shot point that is clearly located on a certain surface type can be first determined, and the strong energy channel number corresponding to the shot point is used as a reference value, and the positions of other shot points with strong energy channels similar to the reference value are all classified as the surface type. The computer device readjusts the partitions of different surfaces in the target area based on the strong energy channels corresponding to each shot point in each sub-area to obtain multiple adjusted sub-areas, including: for any sub-area, the strong energy channel number corresponding to the reference shot point in the sub-area is determined as the reference channel number; based on the strong energy channel number and the reference channel number corresponding to each shot point in each sub-area, the shot point whose difference between the strong energy channel number and the reference channel number does not exceed the strong energy channel number threshold is determined; based on the reference shot point and the determined shot point, the adjusted sub-area is determined, and the adjusted sub-area includes the reference shot point and the determined shot point.

[0123] Optionally, the method for determining the reference shot point includes any of the following: determining the shot point located at the center position of the sub-area as the reference shot point; determining the shot point closest to the center position in the sub-area as the reference shot point; displaying a shot point distribution interface of the target area, the shot point distribution interface including multiple shot points distributed in the target area; in response to a selection operation of any shot point in the shot point distribution interface, determining the shot point as the reference shot point of the sub-area where the shot point is located.

[0124] 206. The computer device determines, for any adjusted sub-region, the surface type of the adjusted sub-region based on the surface type corresponding to the shot points in the adjusted sub-region; and performs differential denoising on the seismic data of the target region based on multiple adjusted sub-regions and the surface types of the multiple adjusted sub-regions to obtain denoised seismic data.

[0125] The computer device determines the surface type of the adjusted sub-region based on the surface type corresponding to the shot points in the adjusted sub-region, that is, the surface type corresponding to the shot points in the adjusted sub-region is used as the surface type of the adjusted sub-region.

[0126] In some embodiments, sub-regions of different surface types need to be processed using different noise suppression parameters. The computer device performs differential denoising on the seismic data of the target region based on the multiple adjusted sub-regions and the surface types of the multiple adjusted sub-regions to obtain denoised seismic data, including: the computer device determines the corresponding noise suppression parameter based on the correspondence between the surface type and the noise suppression parameter and the surface type of the adjusted sub-region, and uses the noise suppression parameter to perform denoising on the adjusted sub-region.

[0127] The method for processing seismic data provided in the embodiment of the present application takes into account the different energy losses of seismic waves propagating on different types of surfaces. Therefore, the target area is partitioned according to the number of strong energy channels of shot points at different positions in the target area. The partitioning is in line with the energy loss of seismic waves propagating on different types of surfaces. Therefore, the partitioning is more accurate, and can more accurately perform differentiated denoising on the seismic data, thereby improving the denoising effect of the seismic data.

[0128] Some exploration work is mainly carried out on desert and Gobi surfaces. Deserts are mainly composed of aeolian dunes, with complex surface structures, large lateral changes, and strong energy noise. This has a great impact on the signal-to-noise ratio of seismic data. Due to the influence of the surface structure, it is difficult to finely partition noise, which has a great impact on noise suppression and deconvolution processing. The partitioning method provided in the embodiment of the present application distinguishes the noise development range and better suppresses the noise of seismic data (such as Figure 3 As shown, the left side is the seismic data before denoising, and the right side is the seismic data after denoising), which improves the quality of the seismic data after denoising and lays the foundation for subsequent processing.

[0129] Figure 4 is a schematic diagram of the structure of a seismic data processing device provided in an embodiment of the present application, such as Figure 4 As shown, the device comprises:

[0130] The first determination module 401 is used to determine the nearest detection point arrangements corresponding to the multiple shot points respectively from the seismic data of the target area based on the distance between the shot point and the detection point arrangement, the nearest detection point arrangement corresponding to any shot point is the detection point arrangement with the shortest distance to the shot point among the multiple detection point arrangements corresponding to the shot point, the target area includes multiple sub-areas with different surface types, and multiple shot points are distributed in each sub-area;

[0131] The second determination module 402 is used to determine, for each shot point among the multiple shot points, the number of strong energy channels corresponding to the shot point based on the seismic data collected by the nearest detection point arrangement corresponding to the shot point, where the number of strong energy channels is the number of seismic channels with energy intensity greater than an intensity threshold, and one seismic channel corresponds to one detection point in the detection point arrangement and the shot point corresponding to the detection point arrangement;

[0132] An adjustment module 403 is used to readjust the partitions of different surfaces in the target area based on the number of strong energy traces corresponding to each shot point in each sub-area to obtain a plurality of adjusted sub-areas, so that the difference of the number of strong energy traces corresponding to different shot points in the adjusted sub-areas does not exceed the threshold value of the number of strong energy traces;

[0133] The denoising module 404 is used to perform differential denoising on the seismic data of the target area based on the multiple adjusted sub-areas to obtain denoised seismic data.

[0134] In a possible implementation, the adjustment module 403 is used to determine, for any sub-region, the number of strong energy traces corresponding to the reference shot point in the sub-region as the reference trace number; determine, based on the number of strong energy traces and the reference trace number corresponding to each shot point in each sub-region, a shot point whose difference between the number of strong energy traces and the number of reference traces does not exceed a threshold value of the number of strong energy traces; and determine, based on the reference shot point and the determined shot point, an adjusted sub-region, the adjusted sub-region including the reference shot point and the determined shot point.

[0135] like Figure 5 As shown, in a possible implementation, the device further includes a third determination module 405, and the third determination module is used to point to any of the following items:

[0136] The shot point located at the center of the sub-area is determined as the reference shot point;

[0137] The shot point closest to the center position in the sub-area is determined as the reference shot point;

[0138] A shot point distribution interface of the target area is displayed, the shot point distribution interface includes a plurality of shot points distributed in the target area; in response to a selection operation of any shot point in the shot point distribution interface, the shot point is determined as a reference shot point of the sub-area where the shot point is located.

[0139] In one possible implementation, the second determination module 402 is used to perform autocorrelation processing on the seismic data collected by the nearest detection point arrangement corresponding to the shot point for each shot point among the multiple shot points, to obtain the amplitudes of multiple seismic traces formed by the shot point and the nearest detection point arrangement; based on the amplitudes of the multiple seismic traces, determine the number of strong energy traces corresponding to the shot point.

[0140] In a possible implementation, the device further includes:

[0141] The fourth determination module 406 is used to select, for each of the multiple shot points, multiple seismic data segments with equal time intervals from the seismic data collected by the nearest detection point corresponding to the shot point in chronological order; determine the number of strong energy channels corresponding to the shot point in multiple time periods based on each seismic data segment; determine the surface type corresponding to the shot point based on the change rule of the strong energy channel number of the shot point in different time periods, and the surface type corresponding to the shot point is used to indicate the surface type of the location of the shot point;

[0142] A fifth determination module 407 is used to determine the surface type of any adjusted sub-region based on the surface types corresponding to the shot points in the adjusted sub-region.

[0143] The denoising module 404 is used to perform differential denoising on the seismic data of the target area based on the multiple adjusted sub-areas and the surface types of the multiple adjusted sub-areas to obtain denoised seismic data.

[0144] In a possible implementation, the fourth determination module 406 is used to obtain the ratio of the number of strong energy channels corresponding to the shot point in the first time period to the number of strong energy channels corresponding to the shot point in the second time period to obtain a first ratio; obtain the ratio of the number of strong energy channels corresponding to the shot point in the second time period to the number of strong energy channels corresponding to the shot point in the third time period to obtain a second ratio, and the first time period, the second time period and the third time period are arranged in a time order from front to back; determine the difference between the first ratio and the second ratio as a first relationship coefficient, and the relationship coefficient is used to indicate the strength of the relationship between the number of strong energy channels and noise; based on the first relationship coefficient and the correspondence between the relationship coefficient and the surface type, determine the surface type corresponding to the first relationship coefficient as the surface type corresponding to the shot point.

[0145] It should be noted that: the seismic data processing device provided in the above embodiment only uses the division of the above functional modules as an example to illustrate when processing seismic data. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the seismic data processing device provided in the above embodiment and the seismic data processing method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0146] Optionally, the computer device is provided as a terminal. Figure 6 The terminal 600 includes a processor 601 and a memory 602 .

[0147] The processor 601 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 601 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 601 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 601 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 601 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0148] The memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 602 is used to store at least one program code, which is used to be executed by the processor 601 to implement the method for processing seismic data provided in the method embodiment of the present application.

[0149] In some embodiments, the terminal 600 may further optionally include: a peripheral device interface 603 and at least one peripheral device. The processor 601, the memory 602 and the peripheral device interface 603 may be connected via a bus or a signal line. Each peripheral device may be connected to the peripheral device interface 603 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 604, a display screen 605, a camera 606, an audio circuit 607, a positioning component 608 and a power supply 609.

[0150] The peripheral device interface 603 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 601 and the memory 602. In some embodiments, the processor 601, the memory 602, and the peripheral device interface 603 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 601, the memory 602, and the peripheral device interface 603 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0151] The display screen 605 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos and any combination thereof. When the display screen 605 is a touch display screen, the display screen 605 also has the ability to collect touch signals on the surface or above the surface of the display screen 605. The touch signal can be input to the processor 601 as a control signal for processing. At this time, the display screen 605 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 605 can be one, and the front panel of the terminal 600 is set; in other embodiments, the display screen 605 can be at least two, which are respectively set on different surfaces of the terminal 600 or are folded; in some other embodiments, the display screen 605 can be a flexible display screen, which is set on the curved surface or folded surface of the terminal 600. Even, the display screen 605 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 605 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode, organic light-emitting diode).

[0152] The power supply 609 is used to power various components in the terminal 600. The power supply 609 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 609 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0153] Those skilled in the art will understand that Figure 6 The structure shown in the figure does not constitute a limitation on the terminal 600, and the terminal 600 may include more or less components than those shown in the figure, or combine some components, or adopt a different component arrangement.

[0154] Figure 77 is a schematic diagram of the structure of a server provided in an embodiment of the present application. The server 700 may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) 701 and one or more memories 702, wherein the memory 702 stores at least one program code, and the at least one program code is loaded and executed by the processor 701 to implement the methods provided in the above-mentioned various method embodiments. Of course, the server may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the server may also include other components for implementing device functions, which will not be described in detail here.

[0155] The server 700 is used to execute the steps executed by the server in the above method embodiment.

[0156] An embodiment of the present application also provides a computer-readable storage medium, in which at least one program code is stored. The at least one program code is loaded and executed by a processor to implement the method for processing seismic data as described in any of the above implementations.

[0157] An embodiment of the present application also provides a computer program product, which includes at least one program code, and the at least one program code is loaded and executed by a processor to implement the method for processing seismic data as described in any of the above implementations.

[0158] In some embodiments, the computer program involved in the embodiments of the present application may be deployed and executed on a computer device, or on multiple computer devices located at one location, or on multiple computer devices distributed at multiple locations and interconnected by a communication network. Multiple computer devices distributed at multiple locations and interconnected by a communication network may constitute a blockchain system.

[0159] The above are only optional embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for processing seismic data, characterized in that: The method comprises: Based on the distance between the shot point and the detection point arrangement, the nearest detection point arrangement corresponding to the multiple shot points is determined from the seismic data of the target area, the nearest detection point arrangement corresponding to any shot point is the detection point arrangement closest to the shot point among the multiple detection point arrangements corresponding to the shot point, the target area includes multiple sub-areas with different surface types, and multiple shot points are distributed in each sub-area; For each shot point among the plurality of shot points, based on seismic data collected by the nearest detection point arrangement corresponding to the shot point, determine the number of strong energy channels corresponding to the shot point, the number of strong energy channels being the number of seismic channels with energy intensity greater than an intensity threshold, one seismic channel corresponding to one detection point in the detection point arrangement and the shot point corresponding to the detection point arrangement; Based on the number of strong energy channels corresponding to each shot point in each sub-area, re-adjusting the partitions of different surfaces in the target area to obtain a plurality of adjusted sub-areas, so that the difference of the number of strong energy channels corresponding to different shot points in the adjusted sub-areas does not exceed a threshold value of the number of strong energy channels; Based on the multiple adjusted sub-areas, differential denoising is performed on the seismic data of the target area to obtain denoised seismic data.

2. The method according to claim 1, characterized in that Based on the number of strong energy channels corresponding to each shot point in each sub-area, the partitions of different surfaces in the target area are readjusted to obtain a plurality of adjusted sub-areas, including: For any sub-region, the strong energy trace number corresponding to the reference shot point in the sub-region is determined as the reference trace number; Based on the strong energy trace number corresponding to each shot point in each sub-area and the reference trace number, determining a shot point whose difference between the strong energy trace number and the reference trace number does not exceed the strong energy trace number threshold; Based on the reference shot points and the determined shot points, an adjusted sub-region is determined, the adjusted sub-region including the reference shot points and the determined shot points.

3. The method according to claim 2, characterized in that The method further comprises any of the following: Determine a shot point located at a central position in the sub-area as a reference shot point; Determine the shot point closest to the center position in the sub-area as the reference shot point; A shot point distribution interface of the target area is displayed, wherein the shot point distribution interface includes a plurality of shot points distributed in the target area; in response to a selection operation of any shot point in the shot point distribution interface, the shot point is determined as a reference shot point of the sub-area where the shot point is located.

4. The method according to claim 1, characterized in that: For each of the plurality of shot points, based on seismic data collected by arranging the nearest detection points corresponding to the shot point, determining the number of strong energy traces corresponding to the shot point comprises: For each of the multiple shot points, autocorrelation processing is performed on seismic data collected by the nearest detection point arrangement corresponding to the shot point to obtain amplitudes of multiple seismic traces formed by the shot point and the nearest detection point arrangement; The number of strong energy traces corresponding to the shot point is determined based on the amplitudes of the multiple seismic traces.

5. The method according to claim 1, characterized in that The method further comprises: For each of the multiple shot points, multiple seismic data segments with equal time intervals are selected from the seismic data collected by arranging the nearest detection point corresponding to the shot point in time sequence; based on each seismic data segment, the number of strong energy channels corresponding to the shot point in multiple time periods is determined; based on the change rule of the number of strong energy channels of the shot point in different time periods, the surface type corresponding to the shot point is determined, and the surface type corresponding to the shot point is used to indicate the surface type of the location of the shot point; For any adjusted sub-region, determining the surface type of the adjusted sub-region based on the surface type corresponding to the shot points in the adjusted sub-region; The step of performing differential denoising on the seismic data of the target area based on the multiple adjusted sub-areas to obtain denoised seismic data includes: Based on the multiple adjusted sub-regions and the surface types of the multiple adjusted sub-regions, differential denoising is performed on the seismic data of the target region to obtain the denoised seismic data.

6. The method according to claim 5, characterized in that The determining of the surface type corresponding to the shot point based on the variation rule of the strong energy channel number of the shot point in different time periods includes: Obtaining a ratio of the number of strong energy channels corresponding to the shot point in the first time period to the number of strong energy channels corresponding to the shot point in the second time period to obtain a first ratio; Obtaining a ratio of the number of strong energy channels corresponding to the shot point in the second time period to the number of strong energy channels corresponding to the shot point in the third time period to obtain a second ratio, wherein the first time period, the second time period and the third time period are arranged in order from front to back; Determine the difference between the first ratio and the second ratio as a first relationship coefficient, wherein the first relationship coefficient is used to indicate the strength of the relationship between the number of strong energy channels and noise; Based on the first relationship coefficient and the corresponding relationship between the relationship coefficient and the surface type, the surface type corresponding to the first relationship coefficient is determined as the surface type corresponding to the shot point.

7. A seismic data processing device, characterized in that: The device comprises: A first determination module is used to determine the nearest detection point arrangements corresponding to the multiple shot points respectively from the seismic data of the target area based on the distance between the shot point and the detection point arrangement, the nearest detection point arrangement corresponding to any shot point is the detection point arrangement with the shortest distance to the shot point among the multiple detection point arrangements corresponding to the shot point, the target area includes multiple sub-areas with different surface types, and multiple shot points are distributed in each sub-area; A second determination module is used to determine, for each shot point among the multiple shot points, the number of strong energy channels corresponding to the shot point based on the seismic data collected by the nearest detection point arrangement corresponding to the shot point, wherein the number of strong energy channels is the number of seismic channels with energy intensity greater than an intensity threshold, and one seismic channel corresponds to one detection point in the detection point arrangement and the shot point corresponding to the detection point arrangement; An adjustment module, for re-adjusting the partitions of different surfaces in the target area based on the number of strong energy channels corresponding to each shot point in each sub-area to obtain a plurality of adjusted sub-areas, so that the difference of the number of strong energy channels corresponding to different shot points in the adjusted sub-areas does not exceed a threshold value of the number of strong energy channels; The denoising module is used to perform differential denoising on the seismic data of the target area based on the multiple adjusted sub-areas to obtain denoised seismic data.

8. The device according to claim 7, characterized in that The adjustment module is used for determining, for any sub-region, the number of strong energy traces corresponding to the reference shot point in the sub-region as the reference trace number; determining, based on the number of strong energy traces corresponding to each shot point in each sub-region and the reference trace number, a shot point whose difference between the number of strong energy traces and the reference trace number does not exceed the threshold value of the number of strong energy traces; and determining, based on the reference shot point and the determined shot point, an adjusted sub-region, wherein the adjusted sub-region includes the reference shot point and the determined shot point.

9. A computer device, characterized in that: The computer device includes a processor and a memory, wherein at least one program code is stored in the memory, and the at least one program code is loaded and executed by the processor to implement the method for processing seismic data as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the method for processing seismic data according to any one of claims 1 to 6.

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