Magnetotelluric data denoising method combining two deep-learning-based models

Author:

Li Jin1ORCID,Liu Yecheng2ORCID,Tang Jingtian3ORCID,Peng Yiqun2ORCID,Zhang Xian4ORCID,Li Yong5ORCID

Affiliation:

1. Hunan Normal University, College of Information Science and Engineering, Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Changsha, China. (corresponding author)

2. Hunan Normal University, College of Information Science and Engineering, Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Changsha, China.

3. Central South University, Monitoring Ministry of Education, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment, Changsha, China and Ministry of Natural Resources, Technical Innovation Center of Coverage Area Deep Resources Exploration, Hefei, China.

4. Central South University, Monitoring Ministry of Education, Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment, Changsha, China.

5. Chinese Academy of Geological Science, Institute of Geophysical and Geochemical Exploration, Key Laboratory of Geophysical Electromagnetic Probing Technologies of Ministry of Natural Resources, Langfang, China.

Abstract

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.

Funder

National Natural Science Foundation of China

Postgraduate Scientific Research Innovation Project of Hunan Province

National Key RD Program of China

Publisher

Society of Exploration Geophysicists

Subject

Geochemistry and Petrology,Geophysics

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