基于LSTM循环神经网络的大地电磁方波噪声抑制

doi:10.11720/wtyht.2022.1572

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Abstract

Denoising is an important part of magnetotelluric data processing. To enrich and develop the denoising method of magnetotelluric time series, this study introduced the LSTM network-one of the recurrent neural networks-into the square wave noise processing of the magnetotelluric time series. Different from previous studies, the measured magnetotelluric time series without human interference superimposed on simulated square wave noise were used as the input of the LSTM network, and the noise-free original time series were used as the target output of the network. After training for 1,500 epochs, the normalized cross-correlation coefficient between the time series extracted from the simulated noise signals by the network and the original time series reached 0.9718, indicating that the network has effectively learned the characteristics of the noise-free magnetotelluric time series. Finally, the denoising test results of measured square wave noise signals show that the proposed method can effectively suppress the interference of square wave noise and improve the estimation quality of impedance. This study provides a new idea for the processing of magnetotelluric time series based on deep learning.

Key words： magnetotellurics time series LSTM deep learning denoising

Received: 25 October 2021 Published: 17 August 2022

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	Kai YANG
	Wei-Dong TANG
	Cheng LIU
	Jing-Long HE
	Chuan YAO

Cite this article:

Kai YANG,Wei-Dong TANG,Cheng LIU, et al. Suppression of magnetotelluric square wave noise based on a LSTM recurrent neural network[J]. Geophysical and Geochemical Exploration, 2022, 46(4): 925-933.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2022.1572 OR https://www.wutanyuhuatan.com/EN/Y2022/V46/I4/925

Basic structure of LSTM

LSTM network structure

Original time series of training set

Original time series of validation set

LSTM network learning curve

Comparison of Simulation signal fragment before and after denoising

Comparison of resistivity and impedance before and after denoising of simulated signals

Comparison of measured signals before and after denoising

Comparison of resistivity and impedance before and after denoising of measured signals

Comparison of Nyquist diagrams before and after denoising of measured signals

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