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CN-122017960-A - Seismic trace resampling method and storage medium

CN122017960ACN 122017960 ACN122017960 ACN 122017960ACN-122017960-A

Abstract

The invention provides a seismic channel resampling method and a storage medium, and belongs to the technical field of seismic exploration data processing. The method comprises the steps of 1, cutting data slices from an input channel set, preparing calculation parameters, 2, resampling channels in each channel set aiming at the data slices, and 3, summarizing and outputting processing results of each channel set. The invention utilizes the parallel processing mechanism of APACHE SPARK frames, and can well expand according to the cluster scale, thereby fully utilizing the cluster resources and completing the high-efficiency processing of resampling operation.

Inventors

  • WANG MINGQIU
  • DUAN XINGBIAO
  • YANG XIANGSEN
  • KANG YONGGAN
  • WANG CHAOYANG

Assignees

  • 中国石油化工股份有限公司
  • 中石化石油物探技术研究院有限公司

Dates

Publication Date
20260512
Application Date
20241112

Claims (10)

  1. 1. A seismic trace resampling method is characterized by comprising the following steps: Step 1, slicing data from an input channel set, and preparing calculation parameters; step 2, resampling the tracks in each track set for the data slice; And step 3, summarizing the processing results of each gather, and outputting.
  2. 2. The method for resampling seismic traces according to claim 1, wherein the slicing of the data in the step 1 refers to dividing the data automatically according to the trace type by using a calculation paradigm provided by a Spark framework to obtain data slices, and elements in the data slices are in units of trace sets, and a single seismic trace belongs to one trace set.
  3. 3. The method for resampling seismic traces according to claim 2, wherein step 1 further comprises reading the input trace set in a custom format on the HDFS, and using the JNI interface to transfer the data fragments into the local memory.
  4. 4. The seismic trace resampling method of claim 3 wherein the reading of the input gather in a custom format on HDFS refers to custom implementation in a MapReduce framework, including custom InputFormat classes, recordReader classes, custom implementation key classes KeyClass, and value classes ValueClass.
  5. 5. The method for resampling of seismic traces according to claim 1, wherein the step 2 comprises: step 21, slicing the obtained data into gather1, gather2, gather3, gatherN, N total gathers; Step 22, for each gather, using the JNI interface to transfer the data from the JVM to the local memory; Step 23, resampling processing is carried out by adopting a local code, wherein the resampling processing refers to interpolation calculation; Step 24, judging whether the spectrum aliasing of the original seismic signal caused by the downsampling operation exists, if so, turning to step 25, and if not, turning to step 3. Step 25, filtering the data after interpolation calculation using an anti-aliasing filter.
  6. 6. The method for resampling a seismic trace according to claim 5, wherein step 21 further comprises dynamically adjusting the tile size by a dynamic tile method according to the number of memories, the number of disks, and the network bandwidth factor.
  7. 7. The seismic trace resampling method according to claim 5, wherein step 23 comprises calculating interpolation using Lagrangian interpolation when the ratio of the new sampling interval to the original sampling interval is not an integer, and performing anti-aliasing processing on the resampled data using a zero-phase filter when the ratio of the new sampling interval to the original sampling interval is an integer.
  8. 8. The method for resampling a seismic trace according to any one of claims 5, 6, and 7, wherein said step 25 comprises: determining a sampling frequency fs, and ensuring that the sampling frequency fs is at least twice the highest frequency fm of the signal; the elliptic filter is an anti-aliasing filter, and removes signal components higher than half of the sampling frequency; setting the cut-off frequency of the anti-aliasing filter to be lower than half of the sampling frequency fs ensures that signal components above the cut-off frequency do not cause aliasing; An anti-aliasing filter is applied to eliminate high frequency components above the cut-off frequency.
  9. 9. The method for resampling a seismic trace according to claim 1, wherein the step 3 comprises: summarizing the processing results of each gather by using MapReduce; and submitting MapReduce operation to the Hadoop cluster for execution, and obtaining an output seismic data file according to the HDFS output format.
  10. 10. A computer readable storage medium, characterized in that the computer readable storage medium stores at least one program executable by a computer, which when executed by the computer causes the computer to perform the steps of the seismic trace resampling method according to any of claims 1-9.

Description

Seismic trace resampling method and storage medium Technical Field The invention belongs to the technical field of seismic exploration data processing, and particularly relates to a seismic channel resampling method and a storage medium. Background Seismic data obtained by modern seismic acquisition means are typically stored in digital signals and subsequently processed by an electronic computer. The digital processing method can only process discrete time signals, i.e. the input signal is discrete in time and amplitude, whereas typically the seismic trace data collected in seismic acquisition is obtained by recording the seismic signal at a fixed sampling frequency. This process may be considered as sampling a continuous seismic signal. Raw seismic trace data typically occupies a large amount of memory space and processing directly on the raw seismic trace data also consumes a significant amount of computing resources. The seismic trace resampling operation can reduce the original seismic record data and simultaneously maintain each signal component in the original data. For example, for a seismic data path, the signal generally has a high signal-to-noise ratio near its end position, less effective signal energy, and limited practical significance for analysis, and such data can be discarded directly. Or the resampling work of reserving frequency components is carried out according to the highest frequency of the recorded seismic trace data to compress the data volume, so that the calculation time consumption of various processing programs is reduced. In recent years, along with the change of the exploitation situation of oil and gas resources in China, the seismic exploration target is gradually changed to complex, concealed, deep ultra-deep and various unconventional oil and gas reservoirs. With the rise of the area of the exploration work area and the increase of the depth of the exploration target, the spatial and temporal resolution of the seismic data is continuously improved, and the mode of processing the actual work area data by a single workstation or a small cluster is no longer of practical significance in production. Modern seismic data processing pursues large clusters and high performance computational processing methods, which can result in programs running on large systems that differ greatly in design and implementation from programs running on stand-alone or small clusters. The existing signal processing theory is less related to the efficient implementation of the algorithm. The seismic channel resampling relates to four cases, namely downsampling with the current sampling rate being an integer multiple of the original sampling rate, downsampling with the current sampling rate being a non-integer multiple of the original sampling rate, supersampling with the current sampling rate being an integer multiple of the original sampling rate, and supersampling with the current sampling rate being a non-integer multiple of the original sampling rate. Because of the interpolation algorithm, serial operation on multiple seismic traces inevitably results in high time consumption, and thus the execution efficiency of the program needs to be improved by a parallel means. Disclosure of Invention The invention aims to solve the problems in the prior art, and provides a method for realizing seismic trace resampling, which is used for providing an efficient implementation way for trace resampling operation related to various seismic data processing in Spark computing environment. The invention is realized by the following technical scheme: in a first aspect of the invention, there is provided a method of seismic trace resampling, the method comprising: Step 1, slicing data from an input channel set, and preparing calculation parameters; step 2, resampling the tracks in each track set for the data slice; And step 3, summarizing the processing results of each gather, and outputting. Further, in the step 1, the slicing of the data refers to using a calculation paradigm provided by a Spark frame to automatically split the data according to a gather type, so as to obtain data slices, wherein elements in the data slices are in the gather unit, and a single seismic trace belongs to one gather. Further, the step 1 further includes reading in the input gather in a custom format on the HDFS, and transferring the data fragments into the local memory using the JNI interface. Further, the reading data in the custom format on the HDFS includes that the operation of reading the input gather in the custom format on the HDFS refers to custom implementation in the MapReduce framework, including implementing custom InputFormat classes, recordReader classes, custom implementation key classes KeyClass, and value classes ValueClass. Further, the step 2 includes: step 21, slicing the obtained data into gather1, gather2, gather3, gatherN, N total gathers; Step 22, for each gather, using the JNI interface to transfer the data