Abstract：The seismic data compression is a key techno-logy in the data transmission of wireless seismic recording systems.The current technical scheme is to eliminate its redundancy with data transform coding to achieve the compression,and to restore the original data with reverse transform decoding.This scheme is inefficient due to acquired seismic data operation,which needs additional hardware resources.And high-precision data recovery cannot be ensured.To solve the above problems,we propose a new seismic data compression and reconstruction scheme based on the compressed sensing (CS) theory.By constructing the chaotic Bernoulli measurement matrix (CBMM),the sparse coefficients of seismic data wavelet transform are collected and compressed,and the real-time coding is achieved at the lower computer.In order to improve the reconstruction accuracy,we use the Bayesian tree-structured wavelet compressed sensing (BTSWCS) reconstruction algorithm,which builds a hierarchical Bayesian CS prior model according to the statistical characteristics of wavelet tree structure.The Markov chain Monte Carlo (MCMC) method estimates the model parameters and then the original data is restored at the superior machine.Based on our seismic data application,the compression time with the proposed scheme can be shortened to 1.0×10-5s when the total sampling points are 28.In the case of low signal-to-noise ratio (SNR),our reconstruction algorithm improves the peak signal-to-noise ratio (PSNR) value more than 5 dB.
陈祖斌, 王丽芝, 宋杨, 龙云. 基于压缩感知的小波域地震数据实时压缩与高精度重构[J]. 石油地球物理勘探, 2018, 53(4): 674-681,693.
Chen Zubin, Wang Lizhi, Song Yang, Long Yun. Seismic data real-time compression and high-precision reconstruction in the wavelet domain based on the compressed sensing. Oil Geophysical Prospecting, 2018, 53(4): 674-681,693.
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