Denoising CSAMT signals in the time domain base on long short-term memory

Abstract

Controlled-Source Audio Frequency Magnetotellurics (CSAMT) is an artificial-source electromagnetic technique that partially mitigates the limitations of weak natural field signals. However, practical field surveys inevitably encounter strong interference, severely affecting signal quality. Traditional methods like Fourier transformation, which directly computes apparent resistivity from frequency-domain information, are inadequate for this context, so we need alternative denoising approaches. However, research on CSAMT denoising is currently limited. Given the excellent performance of Long Short-Term Memory (LSTM) neural networks in the processing of Magnetotelluric (MT) data, as demonstrated by previous studies, this paper proposes the use of LSTM to denoise CSAMT signals in the time domain. Unlike traditional denoising methods, we aim to directly extract the target frequency signal from the time series data for denoising. For MT data, target frequency signals and noise are all mixed together, so noise suppression can only be achieved by identifying noise characteristics in the time series. However, unlike MT data, CSAMT data has an artificial transmitting source, and the frequency of the valid signal is fixed within a time interval. This allows for the direct extraction of target frequency signals without considering the complex characteristics of noise. In this study, we developed a neural network based on bidirectional LSTM to accomplish the task of noise suppression. After conducting both simulated and measured data tests, this method was able to, on average, improve the signal-to-noise ratio (SNR) of CSAMT data by approximately 20dB and partially address the challenge of denoising when the data's SNR falls below 0dB.