基于深度学习的中心回线瞬变电磁全区视电阻率计算

doi:10.11720/wtyht.2021.1511

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Abstract

Deep learning is an extension of the artificial neural network algorithm, which has a good approximation ability for complex functions. This paper introduces this means for the calculation of transient electromagnetic apparent resistivity. First, a 5-layer deep neural network is established with a single mapping relationship between the normalized induced electromotive force and the transient field parameters. By analyzing the error conditions trained by different numbers of neurons in a single hidden layer, the hidden layers of the 5-layer deep neural network are determined. The number of layered neurons is 13,8,5,8,13. The training algorithm chooses the improved Nadam algorithm with adaptive learning rate, which can speed up the training process. The trained deep neural network model is simulated and verified by a typical electrical model, and it is found that it has a good response to different geoelectric models, which proves the feasibility of calculating apparent resistivity based on deep learning put forward in this paper. The actual application results show that the trained deep neural network model can quickly and accurately calculate the apparent resistivity, and its effectiveness is verified by drilling.

Key words： TEM deep learning artificial neural network whole-region apparent resistivity

Received: 08 November 2020 Published: 27 July 2021

ZTFLH:

P631

Corresponding Authors: ZHANG Ying-Ying E-mail: wu_guopei@163.com;zhangyy19890423@163.com

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	Articles by authors
	Guo-Pei WU
	Ying-Ying ZHANG
	Bo-Wen ZHANG
	Hua-Liang ZHAO

Cite this article:

Guo-Pei WU,Ying-Ying ZHANG,Bo-Wen ZHANG, et al. The calculation of full-region apparent resistivity of central loop TEM based on deep learning[J]. Geophysical and Geochemical Exploration, 2021, 45(3): 750-757.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2021.1511 OR https://www.wutanyuhuatan.com/EN/Y2021/V45/I3/750

F(u) and the change characteristics of the transient field parameter u

DNN calculation of apparent resistivity selection discriminant diagram

Universal five layer DNN structure

The convergence of different numbers of neurons in the hidden layer of ANN%

The convergence reduction graph of the DNN Nadam training algorithm

Comparison of DNN apparent resistivity with different training convergence

Schematic diagram of apparent resistivity and apparent depth of 4 geoelectric models

Schematic diagram of apparent aesistivity and depth of Hejing’s water resources survey

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