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• W3128641376 abstract "The ambient noise in controlled-source electromagnetic (CSEM) data seriously affects the accuracy and reliability of the exploration result. Traditional correlation-based data selection method requires manually setting the threshold. To overcome the deficiency, we analyze the typical noises in CSEM data and find that normalized cross-correlation (NCC), absolute maximum value of the amplitude (Max), and detrend fluctuation analysis (DFA) can be used to accurately identify high-quality time series. Based on this discovery, we replace traditional manually intervention with unsupervised machine learning and propose a novel CSEM data processing method. We applied the newly proposed method to synthetic and measured CSEM data to verify the feasibility and effectiveness. Experimental results demonstrate that the newly proposed method is superior to the conventional data selection method because it accurately selects the best data fragments from noisy data automatically. The newly proposed method requires no human intervention which makes the results obtained free of subjective distortion caused by the operator." @default.
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• W3128641376 date "2021-03-01" @default.
• W3128641376 modified "2023-10-16" @default.
• W3128641376 title "Robust CSEM data processing by unsupervised machine learning" @default.
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• W3128641376 doi "https://doi.org/10.1016/j.jappgeo.2021.104262" @default.
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