Seismic data static correction processing method and system based on deep learning

Abstract

The invention provides a seismic data static correction processing method and system based on deep learning. The method comprises the steps of: S1, constructing a neural network, and reconstructing seismic data according to the neural network; s2, training the neural network by using the reconstructed seismic data to obtain a neural network training model; and S3, obtaining a static correction processing result according to horizontal surface elevation and the neural network training model. The system comprises a neural network construction module used for constructing a neural network, a datareconstruction module used for reconstructing seismic data according to the neural network; a training module used for training the neural network by using the reconstructed seismic data to obtain aneural network training model, and a static correction processing module used for obtaining a static correction processing result according to horizontal surface elevation and the neural network training model. With the method and system of the invention adopted, first arrival data and near-surface velocity model data are not needed, a complex near-surface modeling process is avoided, and an efficient and accurate static correction processing function is realized.

Description

technical field [0001] The present invention relates to data processing technology in the field of seismic exploration, in particular to a static correction processing method and system for seismic data based on deep learning. Background technique [0002] Static correction is an important part of seismic data processing. Whether the static correction is accurate or not is directly related to the effect of subsequent series of processing. Accurate first-arrival data and near-surface velocity model data are required to obtain accurate static correction processing results. The process of picking up the first arrival is time-consuming and laborious, and the modeling process of the near-surface velocity is complicated, so it is a difficult process to obtain an accurate near-surface velocity model. In the face of seismic data in complex surface exploration areas such as mountains, it is very difficult to obtain accurate first-arrival data and near-surface velocity models, which ...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem of static correction processing of seismic data in complex areas in seismic data processing, the present invention provides a static correction processing method and system based on deep neural network, which avoids the complex near-surface velocity modeling process and realizes direct static correction processing of seismic data , to obtain an efficient processing effect

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