The magnetotelluric (MT) data collected in an ore-concentration area are extremely vulnerable to all kinds of noise pollution. However, separating real MT signals from strong noise is still a difficult problem, and the noise in MT data is quite distinct from clean data in morphological features. By performing the signal-noise identification and data prediction, we develop a deep learning method to denoise MT data containing strong noise. First, we use the convolutional neural network (CNN) to learn the feature differences between the samples of massive noise and clean data and use the learned features to realize signal-noise identification of the measured data. Second, we use the measured clean data obtained by CNN identification to train the long short-term memory (LSTM) neural network and perform the prediction denoising of the noisy data. The simulation results clearly demonstrate the following two facts: (1) the predicted data output from LSTM basically matches the time-frequency domain features of the real data and (2) our CNN method performs significantly better than the features parameter classification method in dealing with signal-noise identification. In addition, the validity of our method is verified by the processing results of the measured data.

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.

Magnetotelluric (MT) has significant value in earthquake prediction, space weather monitoring, mineral resources exploration, and deep earth structure detection. However, due to the complexity of the environment, MT data collected often are of low data quality due to noise pollution. The noisy data seriously affects the accuracy of the detection of underground structures. Therefore, we propose a magnetotelluric noise suppression method based on a coordinate attention-temporal convolutional network (CA-TCN). Firstly, the CA-TCN is trained with a large data set of artificially created data to learn the nonlinear mapping relationship between the noisy data and noise contours. Then, the CA-TCN model achieves the mapping transformation from noisy data to noise contours in the MT data. Finally, we subtract the noise contours obtained from the CA-TCN mapping model from the original noisy data to achieve signal-to-noise separation and reconstruct high quality data.In simulated experiments, the similarity between denoised data and known high quality data from Qinghai reaches 98%. The results demonstrate that the proposed method exhibits significant advantages compared to convolutional neural network (CNN) methods etc. These findings validate the feasibility of the proposed approach. We applied the proposed method to the real measured data collected from the LuZong mining area, resulting in smoother and more continuous apparent resistivity curves. This indicates that noise in the MT data has been effectively removed, leading to a significant improvement in the quality of the MT data.