Application of independent component analysis in gravity and magnetic data processing
Ma Long1, Meng Junhai1, Fu Qiang2, Shan Zhongxue3, Li Baolan1
1. Qinghai Third Geological Mineral Exploration Institute, Xining, Qinghai 810029, China; 2. Key Laboratory of Continental Collision and Plateau Uplift, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; 3. Qinghai First Geological Mineral Exploration Institute, Ping'an, Qinghai 810600, China
Abstract：Independent component analysis (ICA) is the core of blind source separation (BSS),which makes each output component independent through exploring and eliminating the high order correlation between data components to separate and extract independent source signals.This paper first introduces the basic theory of ICA as well as explains the principle and experiments of the fast ICA algorithm based on the maximum negative entropy.Then according to magnetic signal characteristics,ICA magnetic signals are processed,and a gravity anomaly simulation experiment is conducted based on ICA algorithm iteration.The experiment proves the effectiveness of the proposed method in the gravity anomaly separation and weak anomaly extraction.Finally,real magnetic survey data is processed,and some anomalies are successfully extracted.
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Ma Long, Meng Junhai, Fu Qiang, Shan Zhongxue, Li Baolan. Application of independent component analysis in gravity and magnetic data processing. Oil Geophysical Prospecting, 2017, 52(6): 1344-1353.
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