Dynamic exogenous noise suppression parallel computing method and device, electronic equipment and medium
By using the parallel computing method of the SPARK framework in offshore seismic exploration, dynamic exogenous noise is identified and suppressed, the problem of serious noise interference in offshore seismic exploration is solved, and the computing efficiency and data quality are improved.
Patent Information
- Application Number
- CN202311419819.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-10-30
- Publication Date
- 2025-05-02
AI Technical Summary
In offshore seismic exploration, dynamic exogenous noise interference is severe, resulting in a decrease in signal-to-noise ratio, affecting exploration efficiency, and the existing technology is difficult to effectively process massive data, affecting computing efficiency.
The parallel computing method based on the SPARK framework is adopted to identify and suppress dynamic external noise, reasonably allocate parallelism, and combine multi-threaded computing to make full use of computing resources and improve computing efficiency.
The calculation efficiency of seismic exploration data is significantly improved, the calculation cost is reduced, and high-quality seismic exploration data is obtained.
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Figure CN119917782A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of seismic exploration methods, and more specifically, to a dynamic external noise suppression parallel computing method, device, electronic equipment and medium. Background Art
[0002] Dynamic external noise interference is a coherent noise unique to offshore exploration. It has little attenuation during propagation and presents a strong, chaotic, and irregular inverted hyperbolic parabola in the seismic trace gathers, which seriously reduces the signal-to-noise ratio of the seismic signal and hinders the smooth progress of exploration. Noise must be removed. Since the marine exploration data is very large, even at the PB level, the dynamic external noise suppression calculation does not adopt the parallel method of the SPARK framework. During the calculation process, if the massive data situation is not considered, the efficiency of big data calculation will be affected.
[0003] There is still a need to develop a parallel computing method for dynamic exogenous noise suppression.
[0004] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention
[0005] The present invention proposes a parallel computing method, device, electronic device and medium for dynamic external noise suppression. According to the dynamic external noise identification and suppression computing characteristics, a parallel computing method based on the SPARK framework is adopted to reasonably allocate the degree of parallelism, and at the same time, multi-threaded computing is combined to make full use of computing resources, thereby greatly improving computing efficiency and saving computing costs.
[0006] In a first aspect, an embodiment of the present disclosure provides a parallel computing method for dynamic external noise suppression, comprising:
[0007] Taking the shot number as the smallest calculation unit, the input of the shot gather RDD is obtained;
[0008] Determine the scanning range through flatMap of the shot gather RDD;
[0009] Calculate energy grid judgment value;
[0010] Calculate the energy spectrum of the grid point shot, and then calculate the energy spectrum of the entire shot set AllEnergeData[];
[0011] Shot gather noise suppression is performed based on the energy spectrum AllEnergeData[] of the entire shot gather.
[0012] As a specific implementation of the embodiment of the present disclosure, taking the gun number as the smallest calculation unit, obtaining the RDD input includes:
[0013] Input seismic shot data, the shot number corresponds to the FFID value in the trace header, and the RDD of a single shot is established, with the trace header sorting keyword as the key value. The RDD is expressed as seisRDD = [TraceHeaderKey, ShotRecord].
[0014] As a specific implementation of the embodiment of the present disclosure, determining the scanning range through flatMap of the shot gather RDD includes:
[0015] The maximum area boundary where the dynamic external source appears is calculated from the shot set that needs to be processed. By comparing the maximum and minimum values of the shot point x coordinate Sx, the detection point x coordinate Gx, the shot point y coordinate Sy, and the detection point y coordinate Gy, the maximum and minimum values of the x coordinate and the maximum and minimum values of the y coordinate are obtained.
[0016] As a specific implementation of the embodiment of the present disclosure, calculating the energy grid judgment value includes:
[0017] The map of the shot gather RDD uses the operator filter, the minimum shot number is MinShotNo, the gather data FirstshotData of the minimum shot is obtained, and then the energy grid judgment value judgmentData is calculated.
[0018] As a specific implementation of the embodiment of the present disclosure, calculating the energy spectrum of the grid point shot includes:
[0019] The map of the shot collection RDD uses the operator filter. If the shot grid interval is gridInc, the shot collection gridData on the grid point is obtained through the filter operator;
[0020] With a single shot set as the minimum parallel granularity, the energy spectrum is calculated to form a new map, gridEnergeMap. The energy spectrum value of the entire grid data is obtained through the collect operator.
[0021] As a specific implementation of the embodiment of the present disclosure, calculating the energy spectrum of the entire shot gather includes:
[0022] The energy spectrum of the grid data is smoothed to obtain the energy spectrum array AllEnergeData[i] of the entire processed shot gather.
[0023] As a specific implementation of the embodiment of the present disclosure, performing shot gather noise suppression includes:
[0024] With a single shot set as the minimum parallel granularity, the energy spectrum corresponding to a single shot is taken out from the entire energy spectrum AllEnergeData[], the position of the large ship is identified, the noise is predicted and suppressed, and the single shot set data is output.
[0025] In a second aspect, the embodiment of the present disclosure further provides a dynamic external noise suppression parallel computing device, comprising:
[0026] The input module uses the shot number as the smallest calculation unit to obtain the input of the shot gather RDD;
[0027] A scanning range determination module determines the scanning range by flatMapping the shot gather RDD;
[0028] A judgment value calculation module calculates the energy grid judgment value;
[0029] Energy spectrum calculation module, calculates the energy spectrum of the grid point shot, and then calculates the energy spectrum of the entire shot set AllEnergeData[];
[0030] The noise suppression module suppresses the shot gather noise according to the energy spectrum AllEnergeData[] of the entire shot gather.
[0031] As a specific implementation of the embodiment of the present disclosure, taking the gun number as the smallest calculation unit, obtaining the RDD input includes:
[0032] Input seismic shot data, the shot number corresponds to the FFID value in the trace header, and the RDD of a single shot is established, with the trace header sorting keyword as the key value. The RDD is expressed as seisRDD = [TraceHeaderKey, ShotRecord].
[0033] As a specific implementation of the embodiment of the present disclosure, determining the scanning range through flatMap of the shot gather RDD includes:
[0034] The maximum area boundary where the dynamic external source appears is calculated from the shot set that needs to be processed. By comparing the maximum and minimum values of the shot point x coordinate Sx, the detection point x coordinate Gx, the shot point y coordinate Sy, and the detection point y coordinate Gy, the maximum and minimum values of the x coordinate and the maximum and minimum values of the y coordinate are obtained.
[0035] As a specific implementation of the embodiment of the present disclosure, calculating the energy grid judgment value includes:
[0036] The map of the shot gather RDD uses the operator filter, the minimum shot number is MinShotNo, the gather data FirstshotData of the minimum shot is obtained, and then the energy grid judgment value judgmentData is calculated.
[0037] As a specific implementation of the embodiment of the present disclosure, calculating the energy spectrum of the grid point shot includes:
[0038] The map of the shot collection RDD uses the operator filter. If the shot grid interval is gridInc, the shot collection gridData on the grid point is obtained through the filter operator;
[0039] With a single shot set as the minimum parallel granularity, the energy spectrum is calculated to form a new map, gridEnergeMap. The energy spectrum value of the entire grid data is obtained through the collect operator.
[0040] As a specific implementation of the embodiment of the present disclosure, calculating the energy spectrum of the entire shot gather includes:
[0041] The energy spectrum of the grid data is smoothed to obtain the energy spectrum array AllEnergeData[i] of the entire processed shot gather.
[0042] As a specific implementation of the embodiment of the present disclosure, performing shot gather noise suppression includes:
[0043] With a single shot set as the minimum parallel granularity, the energy spectrum corresponding to a single shot is taken out from the entire energy spectrum AllEnergeData[], the position of the large ship is identified, the noise is predicted and suppressed, and the single shot set data is output.
[0044] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:
[0045] A memory storing executable instructions;
[0046] A processor runs the executable instructions in the memory to implement the parallel computing method for dynamic external noise suppression.
[0047] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the parallel computing method for dynamic external noise suppression is implemented.
[0048] Its beneficial effects are:
[0049] In order to suppress dynamic external noise and obtain high-quality seismic exploration data, this paper is based on the SPARK framework platform of distributed clusters, combines the characteristics of seismic data, uses RDD to optimize the performance of parallel operations, divides different parallel granularity calculations according to computing needs, and fully calls computing resources.
[0050] The methods and apparatus of the present invention have other features and advantages that will be apparent from, or will be described in detail in, the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0051] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.
[0052] Figure 1 A flow chart showing the steps of a parallel computing method for dynamic external noise suppression according to an embodiment of the present invention.
[0053] Figure 2 A block diagram of a parallel computing device for dynamic external noise suppression according to an embodiment of the present invention is shown.
[0054] Description of reference numerals:
[0055] 201, input module; 202, scanning range determination module; 203, judgment value calculation module; 204, energy spectrum calculation module; 205, noise suppression module. DETAILED DESCRIPTION
[0056] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.
[0057] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that the examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.
[0058] Example 1
[0059] Figure 1 A flow chart showing the steps of a parallel computing method for dynamic external noise suppression according to an embodiment of the present invention.
[0060] like Figure 1As shown, the parallel calculation method for dynamic external noise suppression includes: step 101, taking the shot number as the minimum calculation unit, obtaining the input of the shot gather RDD; step 102, determining the scanning range through the flatMap of the shot gather RDD; step 103, calculating the energy grid judgment value; step 104, calculating the energy spectrum of the grid point shot, and then calculating the energy spectrum AllEnergeData[] of the entire shot gather; step 105, performing shot gather noise suppression according to the energy spectrum AllEnergeData[] of the entire shot gather.
[0061] In one example, taking the gun number as the smallest calculation unit, obtaining the RDD input includes:
[0062] Input seismic shot data, the shot number corresponds to the FFID value in the trace header, and the RDD of a single shot is established, with the trace header sorting keyword as the key value. The RDD is expressed as seisRDD = [TraceHeaderKey, ShotRecord].
[0063] In one example, by flatMapping the shot gather RDD, the scan range is determined to include:
[0064] The maximum area boundary where the dynamic external source appears is calculated from the shot set that needs to be processed. By comparing the maximum and minimum values of the shot point x coordinate Sx, the detection point x coordinate Gx, the shot point y coordinate Sy, and the detection point y coordinate Gy, the maximum and minimum values of the x coordinate and the maximum and minimum values of the y coordinate are obtained.
[0065] In one example, calculating the energy grid determination value includes:
[0066] The map of the shot gather RDD uses the operator filter, the minimum shot number is MinShotNo, and the gather data FirstshotData of the minimum shot is obtained, and then the energy grid judgment value judgmentData is calculated.
[0067] In one example, calculating the energy spectrum of a grid point shot includes:
[0068] The map of the shot collection RDD uses the operator filter. If the shot grid interval is gridInc, the filter operator is used to obtain the shot collection gridData on the grid point.
[0069] With a single shot set as the minimum parallel granularity, the energy spectrum is calculated to form a new map, gridEnergeMap. The energy spectrum value of the entire grid data is obtained through the collect operator.
[0070] In one example, calculating the energy spectrum of the entire shot gather includes:
[0071] The energy spectrum of the grid data is smoothed to obtain the energy spectrum array AllEnergeData[i] of the entire processed shot gather.
[0072] In one example, performing shot gather noise suppression includes:
[0073] With a single shot set as the minimum parallel granularity, the energy spectrum corresponding to a single shot is taken out from the entire energy spectrum AllEnergeData[], the position of the large ship is identified, the noise is predicted and suppressed, and the single shot set data is output.
[0074] Specifically, the shot number is used as the minimum calculation unit to form an RDD input. The maximum area boundary where dynamic external sources appear is calculated from the shot set to be processed. By comparing the maximum and minimum values of the shot point x coordinate Sx, the detection point x coordinate Gx, the shot point y coordinate Sy, and the detection point y coordinate Gy, the maximum and minimum values of the x coordinate and the maximum and minimum values of the y coordinate are obtained. During the calculation, the shot point coordinates and the detection point coordinates in the trace header are taken out through the flatMap of RDD, with a single channel as the minimum parallel granularity, and saved in a new map in the form of key-value pairs, with the trace number traceNo as the key value, CoordnateMap = {(traceNo, Sx, Sy, Gx, Gy), dynamic external source noise suppression parallel calculation}, and the map operators max and min are used to obtain the maximum and minimum values to determine the scanning range.
[0075] Energy grid judgment only requires taking the first shot in the shot set to be processed, taking out the data of the first shot through the filter operator of map, calculating the energy grid value, and storing it in the array judgmentData[].
[0076] According to the set shot point grid size, calculate the energy spectrum of the shot collection on the grid point. Through the filter operator of map, take out the shot collection on the grid point to calculate the energy spectrum array shotEnerge[], and form a new energy spectrum map, with the shot number FFID as the key value. gridEnergeMap = {(FFID, shotNerge[]), ...}.
[0077] Smooth the grid point energy spectrum and calculate the energy spectrum of the entire processed shot set allShotEnerge[].
[0078] With a single shot set as the minimum parallel granularity, the energy spectrum corresponding to a single shot is taken out from the entire energy spectrum allShotEnerge[], the position of the large ship is identified, the noise is predicted to suppress the shot set noise, and the single shot set data is output.
[0079] Example 2
[0080] The present invention also provides a dynamic external noise suppression parallel computing device, comprising:
[0081] The input module uses the shot number as the smallest calculation unit to obtain the input of the shot collection RDD;
[0082] The scanning range determination module determines the scanning range through flatMap of the shot gather RDD;
[0083] A judgment value calculation module calculates the energy grid judgment value;
[0084] Energy spectrum calculation module, calculates the energy spectrum of the grid point shot, and then calculates the energy spectrum of the entire shot set AllEnergeData[];
[0085] The noise suppression module suppresses the shot gather noise according to the energy spectrum AllEnergeData[] of the entire shot gather.
[0086] In one example, taking the gun number as the smallest calculation unit, obtaining the RDD input includes:
[0087] Input seismic shot data, the shot number corresponds to the FFID value in the trace header, and the RDD of a single shot is established, with the trace header sorting keyword as the key value. The RDD is expressed as seisRDD = [TraceHeaderKey, ShotRecord].
[0088] In one example, by flatMapping the shot gather RDD, the scan range is determined to include:
[0089] The maximum area boundary where the dynamic external source appears is calculated from the shot set that needs to be processed. By comparing the maximum and minimum values of the shot point x coordinate Sx, the detection point x coordinate Gx, the shot point y coordinate Sy, and the detection point y coordinate Gy, the maximum and minimum values of the x coordinate and the maximum and minimum values of the y coordinate are obtained.
[0090] In one example, calculating the energy grid determination value includes:
[0091] The map of the shot gather RDD uses the operator filter, the minimum shot number is MinShotNo, and the gather data FirstshotData of the minimum shot is obtained, and then the energy grid judgment value judgmentData is calculated.
[0092] In one example, calculating the energy spectrum of a grid point shot includes:
[0093] The map of the shot collection RDD uses the operator filter. If the shot grid interval is gridInc, the filter operator is used to obtain the shot collection gridData on the grid point.
[0094] With a single shot set as the minimum parallel granularity, the energy spectrum is calculated to form a new map, gridEnergeMap. The energy spectrum value of the entire grid data is obtained through the collect operator.
[0095] In one example, calculating the energy spectrum of the entire shot gather includes:
[0096] The energy spectrum of the grid data is smoothed to obtain the energy spectrum array AllEnergeData[i] of the entire processed shot gather.
[0097] In one example, performing shot gather noise suppression includes:
[0098] With a single shot set as the minimum parallel granularity, the energy spectrum corresponding to a single shot is taken out from the entire energy spectrum AllEnergeData[], the position of the large ship is identified, the noise is predicted and suppressed, and the single shot set data is output.
[0099] Specifically, the shot number is used as the minimum calculation unit to form the RDD input. The maximum area boundary of the dynamic external source is calculated from the shot set to be processed, and the maximum and minimum values of the x coordinate Sx of the shot point, the x coordinate Gx of the detection point, the y coordinate Sy of the shot point, and the y coordinate Gy of the detection point are compared to obtain the maximum and minimum values of the x coordinate and the maximum and minimum values of the y coordinate. During the calculation, the shot point coordinates and the detection point coordinates in the trace header are taken out through the flatMap of RDD, with a single trace as the minimum parallel granularity, and saved in a new map in the form of key-value pairs, with the trace number traceNo as the key value, CoordnateMap = {(traceNo, Sx, Sy, Gx, Gy), ...}, and the map operators max and min are used to obtain the maximum and minimum values to determine the scanning range.
[0100] Energy grid judgment only requires taking the first shot in the shot set to be processed, taking out the data of the first shot through the filter operator of map, calculating the energy grid value, and storing it in the array judgmentData[].
[0101] According to the set shot point grid size, calculate the energy spectrum of the shot collection on the grid point. Through the filter operator of map, take out the shot collection on the grid point to calculate the energy spectrum array shotEnerge[], and form a new energy spectrum map, with the shot number FFID as the key value. gridEnergeMap = {(FFID, shotNerge[]), ...}.
[0102] Smooth the grid point energy spectrum and calculate the energy spectrum of the entire processed shot set allShotEnerge[].
[0103] With a single shot set as the minimum parallel granularity, the energy spectrum corresponding to a single shot is taken out from the entire energy spectrum allShotEnerge[], the position of the large ship is identified, the noise is predicted to suppress the shot set noise, and the single shot set data is output.
[0104] Example 3
[0105] An actual shot-channel seismic data set with 841 shots and 334718 seismic channels is selected for dynamic external noise suppression calculation.
[0106] The first step is to input seismic shot data, with the shot number corresponding to the FFID value in the trace header, and establish the RDD of a single shot. The trace header sorting keyword is used as the key value, and the RDD is expressed as seisRDD = [TraceHeaderKey, ShotRecord];
[0107] The second step is to determine the scanning range. Use flatMap to map the shot collection RDD, take a single trace as the minimum parallel granularity, take out the shot point x coordinate, shot point y coordinate, detection point x coordinate, detection point y coordinate in the trace header, and save them in a new map in the form of key-value pairs, with trace number traceNo as the key value, CoordnateMap = {(traceNo, Sx, Sy, Gx, Gy), ...}, and use the map operators max and min to get the maximum and minimum values of the x coordinate and the maximum and minimum values of the y coordinate.
[0108] The third step is to calculate the energy grid judgment value.
[0109] The map of the shot gather RDD uses the operator filter, the minimum shot number is MinShotNo, and the minimum shot gather data FirstshotData is obtained. As follows:
[0110]
[0111] Calculate the energy grid judgment value judgmentData of the shot gather.
[0112] The fourth step is to calculate the energy spectrum of the grid point shot.
[0113] The map of the shot collection RDD uses the operator filter. If the shot grid interval is gridInc, the shot collection gridData on the grid point is obtained through the filter operator. With a single shot collection as the minimum parallel granularity, the energy spectrum is calculated to form a new map, gridEnergeMap. The energy spectrum value of the entire grid data is obtained through the collect operator.
[0114] Step 5: Calculate the energy spectrum of the entire shot collection
[0115] In the fourth step, the energy spectrum of the grid data is smoothed to obtain the energy spectrum array AllEnergeData[i], (i∈[1,334718]) of the entire processed shot set.
[0116] Step 6: Suppress the noise of the artillery fire.
[0117] With a single shot set as the minimum parallel granularity, the energy spectrum corresponding to a single shot is taken out from the entire energy spectrum AllEnergeData[], the position of the large ship is identified, the noise is predicted and suppressed, and the single shot set data is output.
[0118] Example 4
[0119] Figure 2 A block diagram of a parallel computing device for dynamic external noise suppression according to an embodiment of the present invention is shown.
[0120] like Figure 2 As shown, the dynamic external noise suppression parallel computing device comprises:
[0121] Input module 201, taking the shot number as the minimum calculation unit, obtains the input of the shot gather RDD;
[0122] The scanning range determination module 202 determines the scanning range by flatMapping the shot gather RDD;
[0123] A judgment value calculation module 203 calculates the energy grid judgment value;
[0124] Energy spectrum calculation module 204, calculates the energy spectrum of the grid point shot, and then calculates the energy spectrum AllEnergeData[] of the entire shot set;
[0125] The noise suppression module 205 performs shot gather noise suppression according to the energy spectrum AllEnergeData[] of the entire shot gather.
[0126] As a specific implementation of the embodiment of the present disclosure, taking the gun number as the smallest calculation unit, obtaining the RDD input includes:
[0127] Input seismic shot data, the shot number corresponds to the FFID value in the trace header, and the RDD of a single shot is established, with the trace header sorting keyword as the key value. The RDD is expressed as seisRDD = [TraceHeaderKey, ShotRecord].
[0128] As a specific implementation of the embodiment of the present disclosure, determining the scanning range through flatMap of the shot gather RDD includes:
[0129] The maximum area boundary where the dynamic external source appears is calculated from the shot set that needs to be processed. By comparing the maximum and minimum values of the shot point x coordinate Sx, the detection point x coordinate Gx, the shot point y coordinate Sy, and the detection point y coordinate Gy, the maximum and minimum values of the x coordinate and the maximum and minimum values of the y coordinate are obtained.
[0130] As a specific implementation of the embodiment of the present disclosure, calculating the energy grid judgment value includes:
[0131] The map of the shot gather RDD uses the operator filter, the minimum shot number is MinShotNo, and the gather data FirstshotData of the minimum shot is obtained, and then the energy grid judgment value judgmentData is calculated.
[0132] As a specific implementation of the embodiment of the present disclosure, calculating the energy spectrum of the grid point shot includes:
[0133] The map of the shot collection RDD uses the operator filter. If the shot grid interval is gridInc, the filter operator is used to obtain the shot collection gridData on the grid point.
[0134] With a single shot set as the minimum parallel granularity, the energy spectrum is calculated to form a new map, gridEnergeMap. The energy spectrum value of the entire grid data is obtained through the collect operator.
[0135] As a specific implementation of the embodiment of the present disclosure, calculating the energy spectrum of the entire shot gather includes:
[0136] The energy spectrum of the grid data is smoothed to obtain the energy spectrum array AllEnergeData[i] of the entire processed shot gather.
[0137] As a specific implementation of the embodiment of the present disclosure, performing shot gather noise suppression includes:
[0138] With a single shot set as the minimum parallel granularity, the energy spectrum corresponding to a single shot is taken out from the entire energy spectrum AllEnergeData[], the position of the large ship is identified, the noise is predicted and suppressed, and the single shot set data is output.
[0139] Example 5
[0140] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; a processor running the executable instructions in the memory to implement the above-mentioned dynamic external noise suppression parallel computing method.
[0141] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
[0142] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.
[0143] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.
[0144] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.
[0145] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.
[0146] Example 6
[0147] An embodiment of the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the parallel computing method for dynamic external noise suppression is implemented.
[0148] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of each embodiment of the present disclosure are executed.
[0149] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).
[0150] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.
[0151] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.
Claims
1. A parallel computing method for dynamic external noise suppression, characterized in that: include: Taking the shot number as the smallest calculation unit, the input of the shot gather RDD is obtained; Determine the scanning range through flatMap of the shot gather RDD; Calculate energy grid judgment value; Calculate the energy spectrum of the grid point shot, and then calculate the energy spectrum of the entire shot set AllEnergeData[]; Shot gather noise suppression is performed based on the energy spectrum AllEnergeData[] of the entire shot gather.
2. The dynamic external noise suppression parallel computing method according to claim 1, wherein: Taking the gun number as the smallest calculation unit, the RDD input includes: Input seismic shot data, the shot number corresponds to the FFID value in the trace header, and the RDD of a single shot is established, with the trace header sorting keyword as the key value. The RDD is expressed as seisRDD = [TraceHeaderKey, ShotRecord].
3. The dynamic external noise suppression parallel computing method according to claim 1, wherein: By flatMapping the shot gather RDD, the scanning range is determined to include: The maximum area boundary where the dynamic external source appears is calculated from the shot set that needs to be processed. By comparing the maximum and minimum values of the shot point x coordinate Sx, the detection point x coordinate Gx, the shot point y coordinate Sy, and the detection point y coordinate Gy, the maximum and minimum values of the x coordinate and the maximum and minimum values of the y coordinate are obtained.
4. The dynamic external noise suppression parallel computing method according to claim 1, wherein: Calculation of energy grid judgment values includes: The map of the shot gather RDD uses the operator filter, the minimum shot number is MinShotNo, the gather data FirstshotData of the minimum shot is obtained, and then the energy grid judgment value judgmentData is calculated.
5. The dynamic external noise suppression parallel computing method according to claim 1, wherein: Calculating the energy spectrum of a grid point shot includes: The map of the shot collection RDD uses the operator filter. If the shot grid interval is gridInc, the shot collection gridData on the grid point is obtained through the filter operator; With a single shot set as the minimum parallel granularity, the energy spectrum is calculated to form a new map, gridEnergeMap. The energy spectrum value of the entire grid data is obtained through the collect operator.
6. The dynamic external noise suppression parallel computing method according to claim 1, wherein: Calculating the energy spectrum of the entire shot gather includes: The energy spectrum of the grid data is smoothed to obtain the energy spectrum array AllEnergeData[i] of the entire processed shot gather.
7. The dynamic external noise suppression parallel computing method according to claim 1, wherein: Shot gather noise suppression includes: With a single shot set as the minimum parallel granularity, the energy spectrum corresponding to a single shot is taken out from the entire energy spectrum AllEnergeData[], the position of the large ship is identified, the noise is predicted and suppressed, and the single shot set data is output.
8. A dynamic external noise suppression parallel computing device, characterized in that: include: The input module uses the shot number as the smallest calculation unit to obtain the input of the shot collection RDD; A scanning range determination module determines the scanning range by flatMapping the shot gather RDD; A judgment value calculation module calculates the energy grid judgment value; Energy spectrum calculation module, calculates the energy spectrum of the grid point shot, and then calculates the energy spectrum of the entire shot set AllEnergeData[]; The noise suppression module suppresses the shot gather noise according to the energy spectrum AllEnergeData[] of the entire shot gather.
9. An electronic device, characterized in that: The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the parallel computing method for dynamic external noise suppression according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the parallel computing method for suppressing dynamic external noise according to any one of claims 1 to 7 is implemented.