As a novel tool, deep learning is used to solve complex problems in the real world and has been successfully applied to invert for seismic and electromagnetic data. In this study, we propose to use deep neural networks (DNNs) to recover the distribution of the physical properties of buried magnetic orebodies from the surface and airborne magnetic anomaly data. This approach is based on data training instead of prior-knowledge assumptions used in traditional inversion methods. Once our generalized network is established, the computing time to predict new magnetic data using this method can be significantly reduced. By implementing the forward modeling of different types of synthetic physical property models, we obtained enough datasets to train a DNN model so that the network can establish a nonlinear mapping directly from magnetic anomaly data to physical properties. The pre-trained network can be used to estimate the distribution of magnetization intensity from new input magnetic data. Two DNN structures were employed to test the feasibility and generalization of the proposed method by implementing Experiments in serval two-dimensional (2D) synthetic examples. Compared with the conventional method, the predicted distribution of magnetization intensity obtained by using our method is more concentrated and has better resolution to determine the boundary of the magnetic body. In a field example of Galinge iron ore deposits in China, the magnetization distribution of concealed iron orebodies inverted by the proposed approach was in good agreement with that detected by borehole data. DNNs have good nonlinear inversion capability and exhibit some excellent merits of strong learning ability, wide coverage and strong adaptability, which is a considerable application prospect in geophysical exploration.

Zhenlin Hu

Shuang Liu

Xiangyun Hu

Lihua Fu

Jie Qu

Huaijiang Wang

Qiuhua Chen

Physics of The Earth and Planetary Interiors

《地球物理学》和《行星内部》于 1968 年推出，旨在满足行星物理学、大地测量学和地球物理学领域对国际期刊的需求，现已发展成为所有地球物理学家的重要阅读材料。它是唯一完全致力于行星内部物理和化学过程的期刊。原创研究论文、评论文章、短通讯和书评均定期发表；该杂志不时发布特刊，专门刊登编辑认为读者会特别感兴趣的专题讨论会和大会的会议记录。

"Physics of the Earth and Planetary Interiors" was launched in 1968 to meet the demand for an international journal in the fields of planetary physics, geodesy, and geophysics. It has since developed into an essential reading material for all geophysicists and is the only journal fully dedicated to the physical and chemical processes of planetary interiors. The journal regularly publishes original research papers, review articles, short communications, and book reviews, and occasionally releases special issues featuring conference proceedings and discussions on topics of particular interest to readers.

《Physics of the Earth and Planetary Interiors》创刊于1968年，旨在满足行星物理学、大地测量学和地球物理学领域对国际期刊的需求。该期刊现已成为所有地球物理学家的重要阅读材料，是唯一专注于行星内部物理和化学过程的期刊。期刊定期发表原创研究论文、评论文章、短通讯和书评，并不时发布特刊，专门刊登编辑认为读者会特别感兴趣的专题讨论会和大会的会议记录。

Physics of the Earth and Planetary Interiors

Inversion of magnetic data using deep neural networks

DNNs have good nonlinear inversion capability and exhibit some excellent merits of strong learning ability, wide coverage and strong adaptability, which is a considerable application prospect in geophysical exploration.

Category	Quartile
地球科学	3
地球科学, 地球化学与地球物理	3

Inversion of magnetic data using deep neural networks

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
GEOCHEMISTRY & GEOPHYSICS	2