CSEM pseudo-random signal processing method based on feature extraction and clustering identification
ZHANG Xian1,2, LI Diquan1,2, LI Jin3, HU Yanfang1,2
1. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha, Hunan 410083, China; 2. School of Geosciences and Info-Physics, Central South University, Changsha, Hunan 410083, China; 3. College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan 410081, China
Abstract：The controlled-source electromagnetic (CSEM) data is susceptible to noise,which leads to unsatisfactory exploration effects. Human factors will exert a huge influence on traditional CSEM data processing which usually employs frequency point screening,abnormal elimination and other methods and the filtering method cannot retain pseudo-random effective signals. According to the recorded CSEM data in the time domain,we analyze the time-domain statistical characteristics of useful signals and noises in the CSEM data,and quantitatively identify and qualitatively analyze useful CSEM signals to address the above problems. As a resulta CSEM pseudo-random signal processing method based on feature extraction and clustering identification is proposed in this paper. Firstly,the sample library including two kinds of typical noises and pseudo-random signals is established,and features of the time and frequency domains of the sample library signals are analyzed. Then the time-domain statistical features are extracted,and the fuzzy C-means clustering algorithm is adopted to identify and eliminate the noise for retaining useful signals and reconstructing original CSEM data. Finally,the frequency spectrum of effective frequency points is extracted by digital coherence technology. Through the processing of simulated data and measured data,results show that the proposed method can identify and eliminate typical noises accurately and effectively,thereby significantly improving the quality of CSEM data. After being processed by the proposed method,the component Ex normalization electric field curve and wide field electromagnetic (WFEM) resistivity curve are smoother and more continuous, thus effectively increasing the signal-to-noise ratio of CSEM signals.
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ZHANG Xian, LI Diquan, LI Jin, HU Yanfang. CSEM pseudo-random signal processing method based on feature extraction and clustering identification. Oil Geophysical Prospecting, 2022, 57(4): 973-981,1008.
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