摘要
针对微地震信号的特点,在讨论压缩感知理论基础上,研究了基于该框架理论下的微地震资料噪声压制方法.首先从微地震信号的稀疏化方面讨论入手,通过小波基函数、三角函数基函数、样条基函数及高斯基函数等众多基函数的实践试算,考虑微地震信号的时空多变性,优选了适于微地震信号重建处理的高斯基函数及其组合基函数;继而,讨论了关系到重建结果优劣的测试矩阵设计问题,把以往取高斯随机矩阵改进的取为确定性高斯矩阵,使计算结果稳定程度明显提高;在讨论数据的规则化、K参数的选取等问题后,提出了局部压缩感知和区域压缩感知联合处理方法,以较小的滑动窗口建立信号样本向量,通过压缩感知方法重建滑动点信号;然后取以上处理后的信号,以较大的固定窗口进行压缩感知方法重建固定窗口信号,通过处理,压制了局部均匀化和区域均匀化噪声,增强了弱微地震有效信号.
According to the characteristics of the microseismic signals,with the discussion of the compressive sampling theory,this paper studies the microseismic noise suppression method based on the theory.First,we start from the sparsification of microseismic data.Through the trial of wavelet basis function,trigonometric basis functions,spline basis function and Gaussian basis function and more other basis function,with the consideration of the spatial and temporal variability of microseismic signal,Gaussian basis function and its combination are found the most suitable for microseismic signal reconstruction.Then,we analyzed the test matrix design problems which are related to the quality of the reconstruction results,the stability of the calculation results is significantly improved by improving the conventional Gaussian random matrix to the certainty Gauss matrix.Further,after the discussion of data normalization,K parameter selection and other issues,this paper puts forward the new processing method combining local compressed sensing with regional compressed sensing,which establish a sample signals vector with a smaller sliding window and then reestablish sliding point signal by compression sensing method.Finally,these processed signal are taken and signal is rebuilt by a larger immobilization window with compressive sampling method.By processing with this method,we can suppress local homogenization and regional homogenization noise and strengthen the weak microseismic effective signal.
出处
《地球物理学进展》
CSCD
北大核心
2017年第4期1636-1642,共7页
Progress in Geophysics
基金
国家高技术研究发展计划(863计划)(2011AA060303)"陆上非一致性时延地震
微地震油藏监测方法研究"课题资助
关键词
微地震
压缩感知
基函数库
噪声压制
microearthquake
compressive sampling
basis function library
noise suppression
