Feature Extraction and Intelligent Identification of Induced Polarization Effects in 1D Time-Domain Electromagnetic Data Based on PMI-FSVM Algorithm

The transient electromagnetic method can obtain resistivity and chargeability simultaneously in polarizable medium detection. Typically, we assume that the earth may contain a chargeable medium if the electromagnetic (EM) data appear negative values or sign reversals. Unfortunately, with barely perceptible characteristics, some EM responses with the induced polarization (IP) effects are considered to be non-polarizable responses. Insufficient understanding of features and inaccurate identification of the IP responses limits the use of the IP effects for broader purposes. For these reasons, we perform 1D forward modeling to discuss the degree of EM response affected by the IP effects and to extract polarization characteristics. To identify the IP effects, we combine partial mutual information (PMI) and the fuzzy support vector machine (FSVM) methods to complete the intelligent identification algorithm. We verify the efficiency and practicality of the algorithm by building Debye loops in field experiments. From the analysis, we distinguish the strong and weak IP effects by introducing the impact ratio. The strong IP responses manifest fast decays and sign reversals, and the weak IP responses primarily show fast decays or outward concavity. The identification algorithm validation results show that the recognition accuracy reaches 90.7%. In the field experiment verification, the Debye loop successfully simulates the IP effects of different intensity, and the identification results indicate that the algorithm has potential in the measured data. With this intelligent identification algorithm, the measurements can provide access to the weak polarizable medium when the impact ratio exceeds 30%.