An Entropy-Constrained Data-Driven Regularization Method for Regular Frame Seismic Data

Abstract

The invention discloses an entropy-constrained data driven formal frame seismic data regularization method which comprises the following steps of performing graying processing on original irregular seismic data; dividing the grayed seismic data to data subsets, calculating the entropy of each data subset; calculating the key value of each data subset according to the entropy of each data subset; selecting the data subset with relatively high key value for forming a training set, performing dictionary training, and calculating an adaptive dictionary primary function; using a relative error anda highest number-of-iterations of an iteration reconstruction result as a convergence standard, performing interpolation encryption on irregular seismic data by means of a final trained adaptive dictionary primary function; and outputting the interpolation seismic data on regular grids, thereby finishing seismic data normalization. The method of the invention utilizes a training set selecting strategy based on prior information-entropy constraint. Under a precondition that high encryption interpolation seismic channel precision is ensured, high-efficiency utilization of an adaptive dictionarylearning method at a seismic data reconstruction aspect is realized.

Description

technical field [0001] The invention relates to the technical field of oil and gas resource exploration, in particular to an entropy-constrained data-driven normal frame seismic data regularization method. Background technique [0002] In the process of field seismic data acquisition, due to the influence of obstacles such as reservoirs, embankments, villages, mines or forbidden mining areas, the spatial distribution of seismic data is often irregular. Since most seismic data processing algorithms are based on seismic data distributed in regular grids, these seismic processing algorithms cannot be performed without regularization of seismic data. At the same time, the irregularity of seismic data will also affect the accuracy of velocity analysis and the suppression effect of coherent noise, so the regularization of seismic data is of great significance in seismic exploration. [0003] The commonly used seismic data regularization method is to encrypt the seismic traces thr...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art and provide an entropy-constrained data-driven normal frame seismic data regularization method, which aims at the compressed sensing interpolation method, the adaptive basis function training takes a lot of time and the application efficiency is low Based on the shortcomings of prior information-entropy constraints, the training set selection strategy based on prior information-entropy constraints is used to speed up the training process of adaptive basis functions on the premise of ensuring the accuracy of encrypted interpolation seismic traces, and realize the adaptive dictionary learning method in seismic data reconstruction. efficient application of

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