摘要
为有效应用SVD方法压制微地震低信噪比资料中的随机噪声,从微地震压裂后背景监测数据中获取背景噪声特征值和特征值下降比,判断各奇异值分量对数据的贡献,提出一种优选特征值并确定合适降噪阶次的方法。模型数据和实际微地震数据应用结果表明:该方法能够压制与背景噪声一致的随机噪声,压制效果明显,同时对有效信号子波损害较少,有效信号频带未发生迁移且能量得到增强,噪声频率成分的能量得到压制,与实际微地震资料吻合较好,可以为微地震数据后期处理提供依据。
It is very important to effectively suppress the strong noise in the low SNR data.Singular value decomposition(SVD)is a common and effective method,but it is very important to select the appropriate eigenvalues more effectively and conveniently to reconstruct the real micro seismic data.In order to effectively use SVD method to remove the random noise in the data,we proposed a method to obtain the maximum eigenvalue from the background monitoring data after micro seismic fracturing,and judged the contribution of each singular value component to the data,determined the appropriate noise reduction order and optimized the eigenvalue of the micro seismic data.The processing of the model data and the microseismic data show that the random noise paralleling to the background noise can be removed.This makes it possible to make full use of the background noise,and the noise suppression effect is obvious,which can provide a solid foundation for the correct identification of microseismic events and other post-processing.
作者
王程
王维红
WANG Cheng;WANG Weihong(School of Earth Science,Northeast Petroleum University,Daqing,Heilongjiang 163318,China)
出处
《东北石油大学学报》
CAS
北大核心
2020年第5期1-12,I0001,共13页
Journal of Northeast Petroleum University
基金
国家自然科学基金项目(41974116)
国家自然科学基金项目(41930431)。
关键词
微地震
奇异值分解
特征值下降比
背景噪声
降噪阶次
micro-seismic
singular value decomposition
eigenvalue ratio of reduction
background noise
noise reduction order
