Signal-Noise Identification for Wide Field Electromagnetic Method Data Using Multi-Domain Features and IGWO-SVM

Abstract

Noise tends to limit the quality of wide field electromagnetic method (WFEM) data and exploration results. The existing WFEM denoising methods lack the signal identification process and are only able to filter or eliminate abnormalities in the time or frequency domain, which easily leads to the loss of more abundant real data and to low data quality. Thus, we built the WFEM data sample library to extract the multi-domain features. Then, neighborhood search and location sharing were used to improve the grey wolf optimizer (IGWO) algorithm. The support vector machine (SVM) parameters were optimized by IGWO to train multi-domain features, and an IGWO-SVM data model was generated. We used the data model to quantitatively test the WFEM signal and noise in the simulation and measured data. This method can effectively identify the WFEM signal and noise, eliminate the identified noise, and use the identified signal to reconstruct the effective data. Finally, the digital coherence technique was used to extract the spectrum amplitude of the effective frequency points. The experiments demonstrated the advantage of the convergence of IGWO algorithms and the comparison of the SVM parameters optimization techniques. The proposed method can quickly and effectively search the optimal SVM parameters, significantly improve the identification effect of WFEM signal noise, and completely remove the abnormal noise waveform in the reconstructed data. The more stable electric field curves in the results verify the effectiveness of the algorithm design and optimized identification method.