Abstract:

Controlled-source electromagnetic method (CSEM) signals are often contaminated by strong human noise, and the resolution of CSEM exploration is greatly affected. In order to improve the quality of CSEM data, in this paper, a new intelligent data processing method is proposed based on support vector machine (SVM) algorithm. Firstly, the baseline-drift noise is removed by complementary ensemble empirical mode decomposition (CEEMD) algorithm, and then the data is classified by SVM to select high-quality signals. In order to validate the proposed method, a targeted experiment was conducted using simulated noise and measured high-quality data, and then the method was applied to the measured data by the wide-field electromagnetic method (WFEM). The results show that the recognition accuracy of SVM is greater than 92.00%. After the treatment of the proposed method, the apparent resistivity changes from jumping shape to continuous and smooth.

Key words: wide-field electromagnetic method (WFEM), complementary ensemble empirical mode decomposition (CEEMD), signal-noise identification, support vector machine (SVM), controlled-source electromagnetic method (CSEM)