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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.
Aiming at the characteristics of microseismic signals, based on the theory of compressive sensing, the method of noise suppression of microseismic data based on the framework theory is studied.Firstly, the microscopic seismic signal is discussed in terms of thinning, wavelet basis function, trigonometrical basis Function, spline basis function and Gauss basis function. Considering the spatiotemporal variability of microseismic signals, Gaussian basis functions and their combined basis functions suitable for microseismic signal reconstruction are selected. The test matrix design problem, which is related to the merits of reconstructed results, is taken as the deterministic Gaussian matrix to improve the Gaussian stochastic matrix, which improves the stability of the calculation results. After discussing the rules of data regularization and K parameter selection, , A joint processing method of local compressed sensing and regional compressed sensing is proposed. A signal sample vector is established by using a smaller sliding window, and a sliding point signal is reconstructed by a compressive sensing method. The processed signal is then compressed to a larger fixed window The sensing method rebuilds the fixed window signal, suppresses local homogenization and regional homogenization noise by processing, Strong weak microseismic valid signal.