1. Engineering Research Center for Seismic Disaster Prevention and Engineering Geological Disaster Detection of Jiangxi Province (East China University of Technology), Nanchang 330013, China
Abstract：
The issue of strong noise has increasingly become a bottleneck restricting the precision and application space of electromagnetic exploration methods. Noise suppression and extraction of effective electromagnetic response information under a strong noise background is a crucial scientific task to be addressed. To solve the noise suppression problem of the controlled-source electromagnetic method in strong interference areas, we propose an approach based on complex-plane 2D k-means clustering for data processing. Based on the stability of the controlled-source signal response, clustering analysis is applied to classify the spectra of different sources and noises in multiple time segments. By identifying the power spectra with controlled-source characteristics, it helps to improve the quality of the controlled-source response extraction. This paper presents the principle and workflow of the proposed algorithm, and demonstrates feasibility and effectiveness of the new algorithm through synthetic and real data examples. The results show that, compared with the conventional Robust denoising method, the clustering algorithm has a stronger suppression effect on common noise, can identify high-quality signals, and improve the preprocessing data quality of the controlledsource electromagnetic method.
作者简介: Zhou Cong, Associate professor. He received a bachelor's degree from Central South University in 2009 and a Ph. D.degree from Central South University in 2016. From 2016 to 2018, he was a postdoctoral researcher in both Central South University and Oregon State University. He currently works at the East China University of Technology. His main research interests are the theory of electromagnetic methods and their applications.
Email: czhou@ecut.edu.cn.
. Extraction of effective response for controlled-source electromagnetic data based on clustering analysis[J]. APPLIED GEOPHYSICS, 2025, 22(4): 1297-1312.
2025, Vol. 22 
