中文版 | English
Title

Joint 3D inversion of gravity and magnetic data using deep learning neural networks

Author
Corresponding AuthorWei, Nanyu
DOI
Publication Years
2022-08-15
Conference Name
2nd International Meeting for Applied Geoscience and Energy, IMAGE 2022
ISSN
1052-3812
EISSN
1949-4645
Source Title
Volume
2022-August
Pages
1457-1461
Conference Date
August 28, 2022 - September 1, 2022
Conference Place
Houston, TX, United states
Publisher
Abstract
Three-dimensional (3D) joint inversion of geophysical data is often non-unique, non-linear on a large scale, and is complicated for most conventional model-driven approaches that use additional regularization terms in the objective function. In recent years, with the development of computing devices and artificial intelligence, processing large-scale data using data-driven methods is no longer difficult, and great progress has been made in the inversion of single geophysical dataset using the deep learning. In this work, we explore the feasibility of using deep learning methods for 3D joint inversion. In particular, we propose two methods based on modified U-Net architectures: (1) early fusion that constructs a single network and requires different types of data to be preprocessed to share the same size; (2) late fusion that employs multiple branches of network designed for different types of data, but feature-fused together before the final loss is calculated. Our synthetic examples focus on the joint 3D inversion of gravity and magnetic inversion for mineral exploration; the model is parameterized by an ore body represented by an ellipsoid with an arbitrary size, position and orientation in the 3D space. We have found that the performance of the early fusion mostly relies on the data preprocessing, but the early fusion has obvious advantages in its simplicity and efficiency; the late fusion is a more stable choice and highly flexible in cases where data are in different sizes. Our results have proven the feasibility and the basic workflow of 3D joint inversion using the deep learning methods.
© 2022 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.
SUSTech Authorship
First ; Corresponding
Language
English
Indexed By
Funding Project
This work was supported by National Key R&D Program of China under grant no. 2018YFC0603305 and the BGP, CNPC Scientific Research Program.
EI Accession Number
20230413445790
EI Keywords
Data handling ; Deep neural networks ; Geophysical prospecting ; Geophysics ; Large dataset ; Mineral exploration ; Ore deposits ; Ores
ESI Classification Code
Ergonomics and Human Factors Engineering:461.4 ; Geophysics:481.3 ; Geophysical Prospecting:481.4 ; Exploration and Prospecting Methods:501.1 ; Data Processing and Image Processing:723.2 ; Gravitation, Relativity and String Theory:931.5
Data Source
EV Compendex
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/519712
DepartmentDepartment of Earth and Space Sciences
Affiliation
1.Department of Earth and Space Sciences, Southern University of Science and Technology, China
2.BGP Inc., CNPC
First Author AffilicationDepartment of Earth and Space Sciences
Corresponding Author AffilicationDepartment of Earth and Space Sciences
First Author's First AffilicationDepartment of Earth and Space Sciences
Recommended Citation
GB/T 7714
Wei, Nanyu,Yang, Dikun,Wang, Zhigang,et al. Joint 3D inversion of gravity and magnetic data using deep learning neural networks[C]:Society of Exploration Geophysicists,2022:1457-1461.
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