Magnetotelluric Noise Attenuation Using a Deep Residual Shrinkage Network

Abstract

Magnetotelluric (MT) surveying is an essential geophysical method for mapping subsurface electrical conductivity structures. The MT signal is susceptible to cultural noise, and the intensity of noise is growing with urbanization. Cultural noise is increasingly difficult to be removed by conventional data processing methods. We propose a novel time-series editing method based on the deep residual shrinkage network (DRSN) to address this issue. Firstly, the MT data are divided into small segments to form a dataset system. Secondly, we use the dataset system to train the denoising model. Finally, the trained model is used for MT data denoising. The experiments using synthetic data and actual field data collected in Qinghai and Luzong, China, show that the DRSN can effectively remove the cultural noise and has better adaptability and efficiency than traditional MT signal processing methods.