Micro-seismic data compression and reconstruction based on distributed compressed sensing
ZHAO Xiaohu
LIU Shanshan
SHEN Xueru
DENG Yuanfang
Abstract：Aiming at the problem of the large amount of micro-seismic data in the distributed micro-seismic monitoring environment,the distributed compressed sensing theory was introduced to compress the micro-seismic data so as to reduce the data transmission.Firstly,the sparsity of micro-seismic signals was analyzed,and then the spatial correlation of the distributed micro-seismic sensor nodes and its coding and compression process were investigated by simulating the distributed scene.Based on the generalized orthogonal matching pursuit algorithm and the sparse adaptive matching pursuit algorithm,an improved distributed sparse adaptive orthogonal matching pursuit reconstruction algorithm was proposed and the performance of the improved algorithm was analyzed.Based on the MATLAB simulation platform,the improved algorithm was used to reconstruct the distributed micro-seismic signal after sparse measurement.The simulation results show that the improved algorithm can still reconstruct the original microseismic signal quickly and accurately when the data amount is reduced by nearlv 7/10.
Keywords：distributed compressed sensingdata compressionmicro-seismic signalreconstruction algorithmsparsity
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 172-182 )