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…(Scopus)Joint Inversion of Gravity and Magnetic Data based on the Modified Structural Similarity Index for the Structural Consistency Constraint
Sheng Liu
1. Department of Safety Engineering, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467041, China2. Institute of Geophysics, China Earthquake Administration, Beijing, 100080, China
Yiju Tang
Department of Safety Engineering, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467041, China
Fangchao Lu
Department of Safety Engineering, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467041, China
Dali Sun
The First Monitoring and Application Center, CEA, Tianjin 300180, China
Bin Jia
Henan University of Urban Construction, Pingdingshan 467041, China
Yuhao Ma
Henan University of Urban Construction, Pingdingshan 467041, China
DOI: https://doi.org/10.36956/eps.v3i1.849
Received: 24 April 2023; Published: 30 April 2024
Copyright © 2024 Sheng Liu, Yiju Tang, Fangchao Lu, Dali Sun, Bin Jia, Yuhao Ma. Published by Nan Yang Academy of Sciences Pte. Ltd..
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Abstract
Joint inversion is a crucial approach to mitigate the non-uniqueness problem in geophysical inversion. Nevertheless, existing joint inversion methods fall short of meeting the stringent requirements of high-precision exploration, necessitating the development of new techniques. In this paper, we introduce the Structural Similarity Index (SSIM) as a novel structural consistency constraint for the joint inversion of gravity and magnetic data. Compared with the results of cross-gradient inversion, our method demonstrates outstanding performance and stability. SSIM inversion not only introduces a new class of joint inversion with structural constraints but also enhances the consistency of inversion results in the distribution of physical attribute values. The structural constraints of SSIM inversion are more comprehensive and robust, significantly improving the reliability of the inversion. Both synthetic and real data applications demonstrate that the proposed method can effectively handle both synthetic and real data, yielding outstanding results.
Keywords: Joint inversion, Structural consistency constraints, Structural similarity index