Rapid post-stack seismic data splicing method and device and storage medium

Abstract

The invention belongs to the technical field of geophysical exploration and development, and particularly relates to a post-stack seismic data rapid splicing method and device and a storage medium. The method comprises the following steps: S1, acquiring seismic data and marker bed position data; s2, analyzing and preprocessing the seismic data; s3, calculating a system difference correction value for the marker bed position data, and performing system difference correction on the marker bed position data; s4, calculating a non-system difference correction value for the position data of the marker bed after the system difference correction; s5, correcting and splicing the seismic data according to the non-system difference correction value; s6, performing splicing enhancement processing on the spliced seismic data; and S7, carrying out amplitude consistency compensation on the seismic data after splicing enhancement. According to the method, the amplitude relative relation is strictly kept unchanged, then seamless splicing is achieved, the cost is reduced, the seismic data processing period is shortened, and achievements can be explained by directly utilizing all blocks in the early stage.

Description

Technical Field

[0001] The invention belongs to the technical field of geophysical exploration and development, and in particular relates to a method, a device and a storage medium for rapid splicing of post-stack seismic data. Background Art

[0002] Due to the limitation of cost and equipment hardware and software, 3D seismic acquisition is carried out in blocks, and the acquisition and processing parameters are different in different periods. There are obvious differences in 3D seismic imaging, energy, phase and frequency. When the study area is expanded and multiple 3D work areas need to be spliced ​​to carry out comprehensive research, the usual practice is to manually splice the target layer structure map and sensitive attributes of different work areas. This method is greatly affected by human factors, has certain errors with the actual geology, and does not support 3D seismic body splicing. The time shift method can realize 3D seismic body splicing by using systematic error drift and finding the minimum closure error line by line. This method has huge workload, low efficiency, and is prone to profile splicing fracture. The cross-correlation quantitative identification method uses systematic error correction and non-systematic error correction to perform 3D seismic body splicing, which greatly improves work efficiency, but cannot obtain good splicing effect for data with poor seismic quality. The pre-stack time migration joint reprocessing method can solve the problem of inconsistency in imaging, energy, phase and frequency of different blocks, and can achieve seamless splicing, but the processing cost is high and the time cycle is long, which cannot meet the fast-paced requirements of development and production.

[0003] Therefore, there is an urgent need to provide a fast, efficient, amplitude-preserving, and fidelity-preserving post-stack splicing method that can meet the needs of lithologic oil and gas reservoir prediction. Summary of the invention

[0004] In order to solve the above technical problems existing in the prior art, the present invention provides a method, device and storage medium for rapid splicing of post-stack seismic data based on time-space dual variation, and mainly conducts research on five key factors (direction, wavelet, frequency, phase and amplitude) of post-stack splicing, considers the time correction amount from the vertical time and horizontal space respectively, eliminates the time difference of seismic data bodies in different blocks as much as possible, and uses series amplitude compensation technology to eliminate the amplitude energy difference caused by the earthquake source, acquisition age, surface conditions and other aspects in different blocks, while strictly keeping the relative relationship of the amplitude unchanged, thereby realizing seamless splicing, reducing costs and shortening the processing cycle of seismic data.

[0005] The present invention establishes a measurement network covering all work areas that need to be spliced ​​for splicing processing. In the splicing process, the main methods used are time-space dual-variable time difference correction, main measurement line & contact line amplitude statistics, etc., to improve the quality of splicing result data, eliminate amplitude energy differences, eliminate time difference problems, and achieve seamless splicing. The details are as follows:

[0006] A post-stack seismic data rapid splicing method, comprising:

[0007] S1. Obtain seismic data and marker horizon data;

[0008] S2. Analyze and preprocess the seismic data;

[0009] S3. Calculate the systematic error correction amount for the marker horizon data, and perform systematic error correction on the marker horizon data;

[0010] S4. Calculate the non-systematic error correction amount for the marker horizon data after systematic error correction;

[0011] S5. Correct and splice the seismic data according to the non-systematic error correction amount;

[0012] S6. Perform splicing enhancement processing on the spliced seismic data;

[0013] S7. Perform amplitude consistency compensation on the seismic data after splicing enhancement.

[0014] Further, the marker horizon data is obtained by performing synthetic seismogram calibration on well logging data and seismic data, and jointly determining the position of the marker horizon in-phase axis by well-seismic method.

[0015] Further, the analysis in step S2 refers to analyzing the differences between the seismic data to be spliced from aspects of survey network, frequency spectrum, time difference distribution, and energy.

[0016] Further, the preprocessing in step S2 specifically includes:

[0017] S201. According to the survey network information of the seismic data to be spliced, establish a large survey network covering the ranges of all work areas to be spliced;

[0018] S202. Check whether the sampling rates of the seismic data to be spliced are consistent. If not, resample the seismic data so that the sampling rates of the two seismic data to be spliced are consistent;

[0019] S203. According to the range of the marker horizon data, depict the horizon boundaries of the work areas of the seismic data to be spliced, and the horizon boundaries should be as straight and smooth as possible;

[0020] S204. Perform seismic data survey area conversion on the small survey network of the work areas of the seismic data to be spliced, and uniformly load it into the large survey network.

[0021] Even further, the systematic error correction amount in step S3 refers to taking the horizon boundary of any marker horizon in step S203 as a constraint, subtracting the marker horizon data to be spliced, using a histogram to statistically analyze the difference distribution range of the overlapping part of the marker horizons to be spliced, and calculating the average value of the differences as the systematic error correction amount.

[0022] Further, in step S3, systematic error correction is performed on the horizon data of the marker horizons, specifically including: according to the systematic error correction amount, performing horizon drift on any horizon, and then drifting the seismic data corresponding to the drifted horizon, so that the horizon and the seismic data are consistent;

[0023] Further, the non-systematic error correction amount in step S4 refers to taking the horizon boundary in step S203 as a constraint, subtracting the drifted horizon and another marker horizon to be spliced from the intermediate splicing result horizon respectively, and outputting their respective corresponding non-systematic error correction amounts; wherein the intermediate splicing result horizon is obtained through the following method:

[0024] Taking the horizon boundary in step S203 as a constraint, splicing the drifted horizon and another marker horizon to be spliced, and performing smoothing processing on the spliced horizon, and outputting the intermediate splicing result horizon under the large survey network.

[0025] Further, step S5 specifically includes: using the non-systematic error correction amount to perform non-systematic error correction on the drifted seismic data and another seismic data to be spliced respectively, obtaining two corrected intermediate result seismic data, and taking the horizon boundary in step S203 as a constraint to splice the two corrected intermediate result seismic data.

[0026] Further, if the similarity between the two splicing bodies reaches the threshold within the set window length, direct splicing is performed; otherwise, splicing is performed through interpolation calculation.

[0027] Further, the post-stack seismic data rapid splicing method further includes: after step S5, checking the spliced seismic data, determining the splicing effect through the splicing trace. If the splicing effect is lower than the set threshold, enter step S6; if the splicing effect is higher than the threshold, directly enter step S7.

[0028] Further, the splicing enhancement processing in step S6 specifically includes:

[0029] S601. Calculating the time difference field of the overlapping part of the intermediate result seismic data after non-systematic error correction, and expanding the inside and outside of the time difference field to form a new data body;

[0030] S602. Performing Gaussian smoothing processing on the time difference field obtained in step S601, and then splicing the seismic data body in the splicing manner in step S5.

[0031] Further, the time difference field is calculated according to the following formula:

[0032]

[0033] wherein, R SS(T) represents the correlation coefficient, Δd represents the delay time, [-t, t] represents the moving time window, and S(t) and S'(t - Δd) represent two seismic data to be spliced.

[0034] Furthermore, the amplitude consistency compensation in step S7 includes spherical spreading compensation and surface-consistent amplitude compensation.

[0035] Even further, the spherical spreading compensation is achieved through the following formula:

[0036] A' = A / Dd

[0037] where A' is the compensated amplitude, A is the original amplitude, and Dd is the spherical spreading compensation factor.

[0038] Even further, the surface-consistent amplitude compensation is achieved through the following formula:

[0039]

[0040] where i and j are the seismic trace numbers, p(i) is the root mean square amplitude of the i-th number of the seismic trace, n is the number of sampling points within the time window, l is the seismic trace number corresponding to the first sampling point within the time window, and x(j) is the seismic amplitude of the j-th number of the seismic trace.

[0041] Furthermore, the post-stack seismic data rapid splicing method further includes: after step S7, checking the spliced seismic data, determining the splicing effect through the splicing trace. If the splicing effect is lower than the set threshold, then enter step S4; if the splicing effect is higher than the threshold, then output the splicing result.

[0042] The present invention also provides a post-stack seismic data rapid splicing device, including a processor and a memory. The memory stores a computer-readable program executable by the processor; when the processor executes the computer-readable program, it implements the steps in the above post-stack seismic data rapid splicing method.

[0043] The present invention also provides a computer-readable storage medium. The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the above post-stack seismic data rapid splicing method.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The post-stack seismic data rapid splicing method provided by the present invention corrects the horizon data of the marker layer through the systematic difference correction amount, calculates the non-systematic difference correction amount, corrects and splices the seismic data according to the non-systematic difference correction amount, and performs splicing enhancement processing on the spliced seismic data; performs amplitude consistency compensation on the spliced and enhanced seismic data; this method considers the time correction amount from both longitudinal time and lateral space, eliminates the time differences of seismic data volumes in different blocks as much as possible, and uses a series of amplitude compensation techniques to eliminate the amplitude energy differences caused by the source, acquisition age, surface conditions, etc. in different blocks, so as to achieve seamless splicing, reduce costs, shorten the processing cycle of seismic data, and directly utilize the interpretation results of each block in the early stage during splicing. At the same time, the splicing results strictly maintain the amplitude mutual relationship without being damaged, achieving amplitude preservation and fidelity processing, and meeting the requirements of lithologic trap reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a flowchart of the method of the present invention.

[0047] Figure 2 It is a schematic diagram of the time difference of the seismic data to be spliced in the embodiment of the present invention.

[0048] Figure 3 It is a schematic diagram of the amplitude difference of the seismic data to be spliced in the embodiment of the present invention.

[0049] Figure 4 It is a schematic diagram of the characteristic effect of the seismic profile after splicing in the embodiment of the present invention.

[0050] Figure 5 It is a schematic diagram of the effect of the planar time slice after splicing in the embodiment of the present invention.

[0051] Figure 6 It is a schematic diagram of the effect of the planar coherence attribute after splicing in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The technical solution of the present invention will be clearly described below in conjunction with the drawings. Obviously, the described embodiments are not all embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

[0053] It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps described in these embodiments and numerical expressions should not be construed as limiting the scope of the present invention.

[0054] The following description of the exemplary embodiments is merely illustrative and is not a limitation of the present invention in any sense, nor of its application or use. Technologies, methods, and devices known to those of ordinary skill in the relevant field may not be discussed in detail here, but when applicable, these technologies, methods, and devices should be regarded as part of this specification.

[0055] Embodiment 1

[0056] The present invention provides a method for quickly splicing post-stack seismic data, as Figure 1 shown, including:

[0057] S1. Obtain seismic data and marker horizon data; in this embodiment, the seismic data can be obtained through existing instruments and software, and the marker horizon data can be obtained by calibrating the synthetic seismogram of well logging data and seismic data, unifying the well-seismic to determine the position of the marker horizon in-phase axis, and finely tracking.

[0058] S2. Analyze and preprocess the seismic data;

[0059] Due to the limitations of software systems and hardware, the seismic data of each 3D work area is collected separately and processed by different methods. The current situation has the following problems: 1) The coordinate systems and line numbers of each work area are not unified; 2) There are obvious differences in phase, energy, polarity, etc. between the seismic data of each block; 3) It is not conducive to geological research in the edge areas of the blocks; 4) It is not conducive to the overall understanding of the geological structure and distribution of the reservoir. In order to smoothly carry out the splicing work of 3D work area seismic data, it is first necessary to analyze the differences between the seismic data to be spliced in terms of survey network, frequency spectrum, time difference distribution, energy, etc. to prepare for preprocessing.

[0060] In the embodiments of the present application, the preprocessing can be specifically implemented by the following four steps:

[0061] S201: According to the survey network information of the seismic data to be spliced, establish a large survey network covering the ranges of all work areas to be spliced;

[0062] S202: Check whether the sampling rates of the seismic data to be spliced are consistent. If not, resample them so that the sampling rates of the two seismic data to be spliced are consistent;

[0063] S203: According to the range of the marker horizon data, depict the horizon boundaries of the work areas of the seismic data to be spliced, and the horizon boundaries should be as straight and smooth as possible;

[0064] S204: Perform seismic data survey area conversion on the small survey network of the work areas of the seismic data to be spliced and uniformly load them into the large survey network.

[0065] S3. Calculate the systematic error correction amount for the formation horizon data of the markers, and perform systematic error correction on the formation horizon data of the markers;

[0066] Taking the horizon boundary of any marker horizon in step S203 as a constraint, subtract the formation horizon data of the markers to be spliced. Use a histogram to statistically analyze the difference distribution range of the overlapping part of the formation horizon data of the markers to be spliced, and calculate the average value of the differences as the systematic error correction amount. According to the systematic error correction amount, perform horizon drift on any horizon, and then drift the seismic data corresponding to the drifted horizon to make the horizon consistent with the seismic data; taking the horizon boundary of the seismic data work area to be spliced described in S203 as a constraint, splice the drifted horizon with another formation horizon of the marker to be spliced, and smooth the spliced horizon to output the intermediate spliced result horizon under the large survey network described in S201. Figure 2 This is a schematic diagram of the time difference of the seismic data to be spliced in this embodiment. From Figure 2 it can be seen that in different overlapping areas, there are different time differences in the seismic data work areas to be spliced. From this, it can be concluded that only using systematic error correction will affect the splicing effect.

[0067] S4. Calculate the non-systematic error correction amount for the formation horizon data of the markers after systematic error correction; taking the horizon boundary in step S203 as a constraint, subtract the drifted horizon and another formation horizon of the marker to be spliced from the intermediate spliced result horizon respectively, and output their respective corresponding non-systematic error correction amounts.

[0068] S5. Correct and splice the seismic data according to the non-systematic error correction amount; use the non-systematic error correction amount to perform non-systematic error correction on the drifted seismic data and another seismic data to be spliced respectively to obtain two corrected intermediate result seismic data, and take the horizon boundary in step S203 as a constraint to splice the two corrected intermediate result seismic data.

[0069] Among them, the main splicing parameter settings can be adjusted according to the actual splicing effect. For example: the relative movement amount limits the time window size that the splicing data can move, and the cross-correlation threshold controls the similarity threshold of the data bodies participating in the splicing within the correlation window length. That is, when the similarity of the two splicing bodies within the correlation window length reaches the threshold, direct splicing can be performed, otherwise, splicing is performed by interpolation calculation. By comparing the splicing effects, select the appropriate optimal splicing parameters to obtain the best intermediate splicing result.

[0070] After step S5, check the spliced seismic data, determine the splicing effect through the splicing trace. If the splicing effect is lower than the set threshold, enter step S6; if the splicing effect is higher than the threshold, directly enter step S7. The threshold of the splicing effect can be set according to actual needs.

[0071] S6. Perform splicing enhancement processing on the spliced seismic data. For most spliced seismic data, the above steps can already achieve a good splicing effect. However, when the quality of seismic data is very poor, further splicing enhancement processing is required to achieve a good splicing effect.

[0072] In this embodiment, the splicing enhancement processing can be specifically implemented by the following two steps:

[0073] S601. Calculate the time difference field of the overlapping part of the intermediate result seismic data after non-systematic error correction, and expand it inside and outside the time difference field to form a new data body, and the new data body is twice as large as the original difference field range; the time difference field is calculated according to the following formula:

[0074]

[0075] where R SS (T) represents the correlation coefficient, Δd represents the delay time, [-t, t] represents the moving time window, and S(t) and S'(t - Δd) represent the two seismic data to be spliced.

[0076] S602. Perform Gaussian smoothing processing on the time difference field obtained in step S601, and then splice the seismic data body in the splicing manner in step S5 to obtain a spliced seismic body without time difference.

[0077] S7. Compensate for the amplitude consistency of the spliced and enhanced seismic data. Due to the differences in conditions such as the seismic source, acquisition age, and surface conditions between the spliced seismic data blocks, there are large lateral differences in the single-shot energy of seismic data, and amplitude consistency processing is required. Figure 3 As shown in the amplitude difference schematic diagram of the seismic data to be spliced according to this embodiment, from Figure 3 it can be seen that there are obvious amplitude differences in the seismic data to be spliced, which is not conducive to analyzing the distribution of reservoirs through attributes. At present, implementing lithologic oil and gas reservoirs is one of the goals in development evaluation work. According to the requirements of reservoir prediction in lithologic traps, it is necessary to strictly maintain the amplitude mutual relationship without being damaged during the processing of seismic data, and truly achieve amplitude and fidelity preservation of the data. In the processing, a series of amplitude compensation techniques are used to compensate for the lateral and longitudinal energy differences caused by the changes in excitation and reception conditions and formation absorption during the propagation of seismic waves. At the same time, strengthen the quantitative analysis and monitoring during the processing to provide reliable data for geological interpretation.

[0078] In this embodiment, for the lateral change and longitudinal attenuation of amplitude, they are respectively achieved through two steps of spherical diffusion compensation technology and surface consistency amplitude compensation technology:

[0079] The spherical diffusion compensation is achieved through the following formula:

[0080] A’ = A / Dd

[0081] Wherein, A’ is the compensated amplitude, A is the original amplitude, and Dd is the spherical spreading compensation factor. By using different pre-designed spherical spreading compensation factors, the parameters with the best effect are optimized through experiments, and the energy loss of seismic waves during propagation is compensated according to the spherical spreading compensation formula, so as to achieve a certain balance of the energy in the middle and deep layers.

[0082] The surface-consistent amplitude compensation is achieved through the following formula:

[0083]

[0084] Wherein, i and j are the seismic trace numbers, p(i) is the root mean square amplitude of the i-th seismic trace number, n is the number of sampling points within the time window, l is the seismic trace number corresponding to the first sampling point within the time window, and x(j) is the seismic amplitude of the j-th seismic trace number.

[0085] Select a reasonable time window range, perform amplitude energy statistics on the two spliced work areas according to the root mean square amplitude formula, decompose them according to the CDP domain, INLINE domain, and XLINE domain to obtain amplitude compensation factors in different domains, and apply them to each seismic trace respectively, so that the energy of each seismic trace is more balanced horizontally, thereby realizing seamless splicing of the seismic data volume.

[0086] After step S7, check the spliced seismic data. Examine the splicing effect from three aspects: seismic profile characteristics, planar time slice, and planar seismic attributes, and check the splicing trace to determine the splicing effect. If the splicing effect is lower than the set threshold, enter step S4 to adjust the parameters and perform splicing processing again; if the splicing effect is higher than the threshold, output the splicing result. Figure 4 It is a schematic diagram of the effect of the spliced seismic profile characteristics according to this embodiment, Figure 5 It is a schematic diagram of the effect of the spliced planar time slice according to this embodiment, Figure 6 It is a schematic diagram of the effect of the spliced planar coherence attribute according to this embodiment. As can be seen from Figure 4 、 Figure 5 、 Figure 6 the in-phase axis of the obtained spliced data volume has no time difference, the connection at the splicing position is natural, and the amplitude consistency is good, which can be used for subsequent contiguous structural interpretation, attribute analysis, and reservoir prediction.

[0087] Embodiment 2

[0088] The present invention also provides a post-stack seismic data fast splicing device, including a processor and a memory. A computer-readable program executable by the processor is stored on the memory; when the processor executes the computer-readable program, the steps in the post-stack seismic data fast splicing method provided in Embodiment 1 are implemented.

[0089] Embodiment III

[0090] The present invention also provides a computer-readable storage medium storing one or more programs, which can be executed by one or more processors to implement the steps in the method for quickly splicing post-stack seismic data provided in Embodiment I.

[0091] The above specific embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the examples, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the scope of the technical solutions of the present invention, and all of them should be covered by the scope of the claims of the present invention.

Claims

1. A method for quickly splicing post-stack seismic data, characterized in that, it includes: S1. Obtain seismic data and marker bed horizon data; S2. Analyze and preprocess the seismic data; S3. Calculate the systematic error correction amount for the marker bed horizon data, and perform systematic error correction on the marker bed horizon data; S4. Calculate the non-systematic error correction amount for the marker bed horizon data after systematic error correction; S5. Correct and splice the seismic data according to the non-systematic error correction amount; S6. Perform splicing enhancement processing on the spliced seismic data; S7. Compensate the amplitude consistency of the seismic data after splicing enhancement.

2. The method for quickly splicing post-stack seismic data according to claim 1, characterized in that, the marker bed horizon data is obtained by performing synthetic seismogram calibration on well logging data and seismic data, and jointly determining the position of the marker bed in-phase axis by well-seismic correlation.

3. The method for quickly splicing post-stack seismic data according to claim 1, characterized in that, the analysis in step S2 refers to analyzing the differences between the seismic data to be spliced from aspects of survey network, spectrum, time difference distribution, and energy.

4. The method for quickly splicing post-stack seismic data according to claim 3, characterized in that, the preprocessing in step S2 specifically includes: S201. According to the survey network information of the seismic data to be spliced, establish a large survey network covering the ranges of all work areas to be spliced; S202. Check whether the sampling rates of the seismic data to be spliced are consistent. If not, resample the seismic data so that the sampling rates of the two seismic data to be spliced are consistent; S203. According to the range of the marker bed horizon data, depict the horizon boundaries of the work areas of the seismic data to be spliced, and ensure that the horizon boundaries are straight and smooth; S204. Perform seismic data survey area conversion on the small survey network of the work areas of the seismic data to be spliced and uniformly load it into the large survey network.

5. The method for quickly splicing post-stack seismic data according to claim 4, characterized in that, the systematic error correction amount in step S3 refers to taking the horizon boundary of any marker bed in step S203 as a constraint, subtracting the marker bed horizon data to be spliced, using a histogram to statistically analyze the difference distribution range of the overlapping part of the marker bed horizons to be spliced, and calculating the average value of the differences as the systematic error correction amount.

6. The method for quickly splicing post-stack seismic data according to claim 5, characterized in that, performing systematic error correction on the marker bed horizon data in step S3 specifically includes: according to the systematic error correction amount, performing horizon drift on any horizon, and then drifting the seismic data corresponding to the drifted horizon so that the horizon and the seismic data are consistent.

7. The method for quickly splicing post-stack seismic data according to claim 6, characterized in that, the non-systematic error correction amount in step S4 refers to taking the horizon boundary in step S203 as a constraint, subtracting the drifted horizon and another marker bed horizon to be spliced from the splicing intermediate result horizon respectively, and outputting their corresponding non-systematic error correction amounts; wherein the splicing intermediate result horizon is obtained by the following method: Taking the horizon boundary in step S203 as a constraint, splice the drifted horizon with the horizon of another marker horizon to be spliced, and smooth the spliced horizon, and output the spliced intermediate result horizon under the large survey network.

8. The post-stack seismic data rapid splicing method according to claim 7, characterized in that, Step S5 specifically includes: using the non-systematic error correction amount to perform non-systematic error correction on the drifted seismic data and another seismic data to be spliced respectively, obtaining two corrected intermediate result seismic data, and taking the horizon boundary in step S203 as a constraint to splice the two corrected intermediate result seismic data.

9. The post-stack seismic data rapid splicing method according to claim 8, characterized in that, If the similarity between the two spliced bodies reaches the threshold within the set window length, direct splicing is performed, otherwise, splicing is performed through interpolation calculation.

10. The post-stack seismic data rapid splicing method according to claim 8, characterized in that, The splicing enhancement process in step S6 specifically includes: S601. Calculate the time difference field of the overlapping part of the intermediate result seismic data after non-systematic error correction, and expand the inside and outside of the time difference field to form a new data volume; S602. Perform Gaussian smoothing on the time difference field obtained in step S601, and then splice the seismic data volume in the splicing manner in step S5.

11. The post-stack seismic data rapid splicing method according to claim 10, characterized in that, The time difference field is calculated according to the following formula: where R SS (T) represents the correlation coefficient, Δd represents the delay time, [-t, t] represents the moving time window, and S(t) and S'(t - Δd) represent two seismic data to be spliced.

12. The post-stack seismic data rapid splicing method according to claim 1, characterized in that, Step S7 for amplitude consistency compensation includes spherical spreading compensation and surface-consistent amplitude compensation.

13. The post-stack seismic data rapid splicing method according to claim 12, characterized in that, The spherical spreading compensation is realized by the following formula: A’ = A / Dd where A’ is the compensated amplitude, A is the original amplitude, and Dd is the spherical spreading compensation factor.

14. The post-stack seismic data rapid splicing method according to claim 12, characterized in that, The surface-consistent amplitude compensation is realized by the following formula: where i and j are seismic trace numbers, p(i) is the root mean square amplitude of the i-th seismic trace number, n is the number of sampling points within the time window, l is the seismic trace number corresponding to the first sampling point within the time window, and x(j) is the seismic amplitude of the j-th seismic trace number.

15. The post-stack seismic data rapid splicing method according to claim 1, characterized in that, The post-stack seismic data rapid splicing method further includes: after step S7, checking the spliced seismic data, determining the splicing effect through the splicing trace, if the splicing effect is lower than the set threshold, then entering step S4; if the splicing effect is higher than the threshold, then outputting the splicing result.

16. A post-stack seismic data rapid splicing device, including a processor and a memory, characterized in that, A computer-readable program executable by the processor is stored on the memory; when the processor executes the computer-readable program, the steps in the method for quickly splicing post-stack seismic data according to any one of claims 1-15 are implemented.

17. A computer-readable storage medium storing one or more programs, characterized in that the one or more programs are executable by one or more processors to implement the steps in the method for quickly splicing post-stack seismic data according to any one of claims 1-15.

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