基于深度神经网络的重力异常反演

doi:10.11720/wtyht.2022.1341

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Abstract

Traditional linear inversion of gravity anomalies is liable to encounter local minima and suffer low computational efficiency. Given this, this paper proposed a deep learning-based inversion of gravity anomalies. Specifically, two-dimensional density models of various shapes were firstly established, and gravity anomalies were obtained through forward simulation using these models to form a dataset. Then, a deep neural network was trained using the dataset. Finally, gravity anomaly data were input into the deep neural network to directly yield inversion results. Experimental results show that the inversion method proposed in this study can determine the locations and shapes of underground anomalies quickly and accurately, with high generalization ability and anti-noise ability. Therefore, this method can be used for the inversion of gravity anomalies.

Key words： deep neural network gravity anomaly inversion

Received: 17 June 2021 Published: 28 June 2022

ZTFLH:

P631

Corresponding Authors: XIONG Jie E-mail: 201972322@yangtzeu.edu.cn;xiongjie@yangtzeu.edu.cn

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	Rong WANG
	Jie XIONG
	Qian LIU
	Rui-Jie XUE

Cite this article:

Rong WANG,Jie XIONG,Qian LIU, et al. Inversion of gravity anomalies based on a deep neural network[J]. Geophysical and Geochemical Exploration, 2022, 46(2): 451-458.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2022.1341 OR https://www.wutanyuhuatan.com/EN/Y2022/V46/I2/451

Three-layer deep fully connected neural network structure

Schematic diagram of gravity anomaly inversion based on deep fully connected network

Deep fully connected network

Schematic diagram of 7 single models

Deep fully connected network inversion parameter settings

Loss change curve

Inversion results of part of the test set samples

Average Inversion results of six complex model samples

Inversion results of abnormal data obtained with the same density parameters

Inversion result after adding 5 dB Gaussian white noise

Inversion result after adding 10 dB Gaussian white noise

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