基于在线惯序极限学习机的瞬变电磁非线性反演

doi:10.11720/wtyht.2021.1514

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Abstract

The traditional transient electromagnetic inversion method using artificial neural network based on gradient descent method is inefficient and can not guarantee global convergence. In order to solve these problems, this paper proposes a transient electromagnetic inversion method based on on online sequential extreme learning machine (OSELM). This approaches is used for inversion of high-dimensional exploration data obtained by transient electromagnetic method. Firstly, the hidden layer parameters (input weight and deviation) are randomly set to simplify the learning process of the model. Then, the prediction samples obtained from the test are added to the training samples as the next update information, and the online sequential extreme learning machine prediction model is established to maximize the inverse accuracy. Finally, the inversion results of two classical TEM layered geoelectric models and a quasi two-dimensional geoelectric model show that the proposed method can solve the problem of nonlinear modeling and high-dimensional data for TEM inversion, and a comparison with extreme learning machine (ELM) shows that this method has more accurate inversion, better generalization ability and higher calculation efficiency, which provides a new idea for the application of neural network in geophysical inversion.

Key words： transient electromagnetic method artificial neural network online sequential extreme learning machine inversion

Received: 23 November 2020 Published: 20 August 2021

ZTFLH:

P631

Corresponding Authors: ZHANG Huai-Qing E-mail: 1378546842@qq.com;zhanghuaiqing@cqu.edu.cn

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	Rui-You LI
	Huai-Qing ZHANG
	Zhao WU

Cite this article:

Rui-You LI,Huai-Qing ZHANG,Zhao WU. Online sequential extreme learning machine for transient electromagnetic nonlinear inversion[J]. Geophysical and Geochemical Exploration, 2021, 45(4): 1048-1054.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2021.1514 OR https://www.wutanyuhuatan.com/EN/Y2021/V45/I4/1048

Schematic diagram of the layered geoelectric model and TEM method

Maximum and minimum values of parameters in each layer for five-layer and nine-layer geoelectric model

ELM neural network structure

Block diagram of the calculation flow of the OSELM algorithm

Comparison of inversion performance for two ELM methods

Theoretical values of five-layered and nine-layered geoelectric model inversion

Inversion results of different algorithms for of 5-layer(a) and 9-layer(b) geoelectric models

Forward response curves of 5-layer(a) and 9-layer(b) geoelectric models

Schematic diagram of quasi two dimensional model and measurement position

Inversion results of OSELM method (a) and ELM method (b) of quasi-two-dimensional geoelectric model

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