Recognition and removal of strong noise in long‐period magnetotelluric data using deep learning

Long‐period magnetotelluric (LMT) data have long recording durations and low frequencies, with exploration depths reaching up to 1000 km. However, the LMT method is highly susceptible to anthropogenic noise, and the long acquisition time of the instruments makes rework costly; therefore, denoising of the data is particularly critical. At present, relatively few methods are available for LMT data processing, and most of them suffer from a low degree of automation, large bias caused by manual processing, and excessive parameter dependence. To address these issues, this paper proposes a denoising method for LMT signals. The proposed method employs an improved task‐specific network, batch normalization residual temporal convolutional network, to automatically identify noise‐contaminated segments in the signal and retain high‐quality segments and then combines Hampel filtering, interval‐point difference detection, step compensation and other processing methods to remove different types of noise in a targeted manner. The experimental results show that the improved deep learning model achieves a recognition accuracy of 0.9994. Compared with the denoising results without signal‐noise identification, after introducing the model, the mean squared errors of the BX and EX channels are reduced by 97.94% and 96.38%, respectively, whereas the signal‐to‐noise ratios are improved by 16.84 and 7.62 dB, respectively. In addition, the proposed method significantly reduces the dispersion of the apparent resistivity and phase curves. The method can reliably improve the quality of LMT data and help further enhance the effectiveness of deep LMT exploration.