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.

It is well known that a magnetotelluric (MT) signal with high signal-to-noise ratio is an important prerequisite for correct interpretation of subsurface structures. However, MT signals collected in the environment of strong cultural noise often are of low data quality due to noise pollution, which seriously affects the accuracy of interpretation. As can be seen from the MT time-domain waveform, the noise is highly energetic, diverse, and random. This means MT denoising methods should have strong applicability to guarantee accurate and effective separation of MT signal from noise data. Therefore, we propose a deep-learning-based data nonlinear mapping method for MT signal-to-noise separation. First, this method focuses on learning the nonlinear mapping relationship between a large amount of noise data and the corresponding noise contour by using the convolutional neural network (CNN) in advance. Then, the mapping transformation of noise data to noise contour in the measured data is realized by CNN model. Specifically, based on the features of MT noise data, we construct a large amount of training data very close to it by mathematical functions. At the same time, we also select some of the measured data to be added to the training set. This not only expands the amount and diversity of the training set but also improves the adaptability of CNN when dealing with complex data. Finally, we evaluate the denoising performance of the proposed method in terms of time-domain waveforms, apparent resistivity-phase curves, and polarization directions before and after denoising. The processing results of the simulated data and the measured data collected in Luzong area have verified the feasibility and effectiveness of the proposed method in MT data denoising.

Audio magnetotellurics (AMT), as a commonly used passive geophysical technique, provides outstanding metal ore exploration capabilities based on the resistivity structure of the earth. However, the accuracy of AMT in translating geoelectrical structures decreases when the data collected in mining areas are of poor quality and contain complex anthropogenic noise, leading to distorted apparent resistivity-phase curves and posing significant challenges for mineral exploration. To effectively denoise AMT data, we develop a new denoising method that combines atom-profile updating dictionary learning (APrU) with the nucleus sampling attention mechanism (NSAM) sparse coding. First, we use APrU to accurately learn the characteristics of the noise in the AMT data; then, we apply the updated dictionary to perform sparse coding on the AMT data by NSAM to obtain the noise; finally, we subtract the noise from the original AMT data to obtain the denoised data. Our experimental results suggest that the proposed method can learn an overcomplete dictionary via the to-be-processed AMT data, thereby enabling the sparse representation of the noise within the learned dictionary. We also demonstrate the efficacy of this method with a set of field data collected from the Lu-Zong mining area, and the attained denoised data faithfully restore the geoelectrical structures with heightened accuracy. The findings confirm that the proposed method realizes the unsupervised learning of the AMT data and allows us to achieve precise denoising performance.

Magnetotelluric (MT) data processing can increase the reliability of measured data. Traditional MT data denoising methods are usually applied to entire MT time-series, which results in the loss of useful MT signals and a decrease of imaging accuracy of electromagnetic inversion. However, targeted MT noise separation can retain part of the signal unaffected by strong noise and enhance the quality of MT responses. Thus, we propose a novel method for MT noise separation that uses the refined composite multiscale dispersion entropy (RCMDE) and the orthogonal matching pursuit (OMP) algorithm. First, the RCMDE is extracted from each segment of the MT data. Then, the RCMDEs for each segment are input to the fuzzy c-mean (FCM) clustering algorithm for automatic identification of the MT signal and noise. Next, the OMP method is utilized to remove the identified noise segments independently. Finally, the reconstructed signal consists of the denoised signal segments and the identified useful signal segments. We conducted simulation experiments and algorithm evaluations on electromagnetic transfer function (EMTF) data, simulated data and measured sites. The results indicate that the RCMDE can improve the stability of multiscale dispersion entropy (MDE) and multiscale entropy (ME) by analyzing the characteristics of the signal samples library, effectively distinguishing MT signals and noise. Compared with the existing technique of denoising entire time series, the proposed method uses the RCMDE as characteristic parameter and uses the OMP algorithm for noise separation, simplifies the multi-feature fusion, and improves the accuracy of signal-noise identification. Moreover, the denoising efficiency is accelerated, and the MT response in the low-frequency band is greatly improved.