摘要
针对分布式微震监测环境下微震数据量大的问题,引入分布式压缩感知理论对微震数据进行压缩以减小数据传输量.首先分析微震信号的稀疏性,其次模拟分布式场景,分析了分布式微震传感器节点的空间相关性及其编码与压缩处理过程.基于广义正交匹配追踪算法及稀疏度自适应匹配追踪算法,提出了一种改进的分布式稀疏度自适应正交匹配追踪重构算法,并分析了改进算法的性能.基于MATLAB仿真平台,用改进的算法重构稀疏测量后的分布式微震信号,仿真结果表明:改进后的算法在数据量减少了将近7/10时,依然可以快速精确的重构出原始微震信号.
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 nearly 7/10.
作者
赵小虎
刘闪闪
沈雪茹
邓园芳
ZHAO Xiaohu;LIU Shanshan;SHEN Xueru;DENG Yuanfang(IoT Perception Mine Research Center ,China University of Mining and Technology,Xuzhou, Jiangsu 221008,China;School of Information and Control Engineering, China University of Mining and Technology, Xuzhou,Jiangsu 221008, China)
出处
《中国矿业大学学报》
EI
CAS
CSCD
北大核心
2018年第1期172-182,共11页
Journal of China University of Mining & Technology
基金
国家重点研发计划课题(2016YFC0801405)
关键词
分布式压缩感知
数据压缩
微震信号
重构算法
稀疏性
distributed compressed sensing
data compression
micro-seismic signal
reconstruction algorithm
sparsity
