A Genetic Algorithm Optimized Undersampling Method for Seismic Sparse Acquisition and Reconstruction

Abstract

The irregular observation region poses challenges to seismic acquisition systems design. The commonly-used parallel-type acquisition system requires that the geophones are located at equally spaced positions and therefore is hard to implement in an irregular observation region. An acquisition system which allows implementation of sparse and irregular observation (e.g., the node-type geometry) followed by a reconstruction procedure is a solution. It can not only fit in irregular observation regions but also make a significant reduction in seismic acquisition costs. The seismic sparse acquisition can be mathematically modeled as a undersampling operator in the seismic reconstruction problem. A suboptimal undersampling pattern will lead to an inferior reconstruction result. To optimize the seismic sparse acquisition, I propose a undersampling method based on bionic intelligence in this study. In the proposed method, a Shannon entropy maximum model is proposed to improve the observed ergodicity and reduce the undersampling artifacts. To solve the maximum problem, an improved version of genetic algorithm is presented. The proposed method is applicable to irregular observation regions and can optimize the subsequent reconstruction performance. I provide the detailed algorithm framework and discuss the undersampling artifacts of different undersampling methods. The application to synthetic and field seismic data validates the effectiveness of the proposed method.