重磁异常独立成分分析的多道信号构建方法  

Multi-channel Signal Construction Methods for Independent Component Analysis of Gravity and Magnetic Anomalies

作　　者：杨明豫 杜劲松[1,2,3] 王震 袁常青 胡正旺 Yang Mingyu;Du Jinsong;Wang Zhen;Yuan Changqing;Hu Zhengwang(Institute of Geophysics and Geomatics,China University of Geosciences,Wuhan Hubei 430074,China;Hubei Subsurface Multi-scale Imaging Key Laboratory,China University of Geosciences,Wuhan Hubei 430074,China;State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences,Wuhan Hubei 430074,China)

机构地区：[1]中国地质大学地球物理与空间信息学院,湖北武汉430074 [2]中国地质大学地球内部多尺度成像湖北省重点实验室,湖北武汉430074 [3]中国地质大学地质过程与矿产资源国家重点实验室,湖北武汉430074

出　　处：《工程地球物理学报》2022年第4期546-557,共12页Chinese Journal of Engineering Geophysics

基　　金：国家自然科学基金项目(编号:42174090);地质过程与矿产资源国家重点实验室自主研究课题(#MSFGPMR01-5);中国地质调查局项目(编号:1212011220245)。

摘　　要：独立成分分析方法(ICA)通过求解观测信号中源信号统计独立性最大的最优化问题对观测信号进行分解,得到相互独立的信号分量即独立成分。这种利用统计学原理的信号分解方法在静态和时变重磁数据处理中具有广泛的应用前景,前人利用相邻剖面法构建ICA算法所需的多道输入信号并对重磁异常数据进行分解,得到了较优的位场分解结果。本文在前人的研究基础上,首先提出了利用相空间重构法与空间延拓方法构建ICA多道输入数据;其次,分别使用相邻剖面法、相空间重构法与空间延拓法对仿真的重、磁异常数据进行分解,对比分析了三种方法在异常分离中的应用效果,结果表明空间延拓法能够更加有效地对局部场与区域场进行分离;最后,将基于空间延拓的ICA方法应用于克拉玛依后山地区的重磁异常数据,结果显示ICA分解具有一定的模态混叠效应,因此在其基础上再进行匹配滤波,相比于直接的匹配滤波,联合方法计算结果的边界效应更弱,并且分离得到的局部重磁异常与地质构造之间的对应关系更加清晰。Independent component analysis(ICA)decomposes the observed data by solving the optimization problem with the maximizing statistical independence of the source signal in the observed data to obtain mutually independent signal components,namely the independent components.This signal decomposition method based on statistical principle has a wide application prospect in processing static and time-varying gravity and magnetic data.Previous researchers used the adjacent profile(AP)method to construct multi-channel input signals required by ICA algorithm,and decomposed static gravity and magnetic data to obtain good potential field decomposition results.On the basis of the previous researches,this paper first proposes the integration of the phase space reconstruction(PSR)and the spatial continuation(SC)to construct the ICA multi-channel input data.Then,the AP,PSR and SC methods are applied to the independent component analysis of simulated gravity and magnetic anomaly data,and their application effects of anomaly separation are compared and analysed.The results show that the SC-based ICA can separate the local field and the regional field more effectively than other two methods.Finally,the SC-based ICA is applied to the practical Bouguer gravity and reduction-to-the-pole(RTP)aeromagnetic anomaly data in the Karamay back mountain area.The decomposition results indicate that the independent components have the mode mixing effect,and thus the matched filtering method is applied to the ICA decomposition results.Compared with the direct matching filtering,the boundary effect of the integrated method is weaker and the correlation between the seperated local gravity and magnetic anomalies and the geological structures in the study area is more clearly identified.

关 键 词：独立成分分析 重磁异常 多道信号构建方法 空间延拓法 相空间重构法 异常分离 

分 类 号：P631[天文地球—地质矿产勘探]