Earth and Planetary Science

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, China
2. 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, China Earthquake Administration, Tianjin, 300180, China

Bin Jia

Department of Safety Engineering, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467041, China

Yuhao Ma

Department of Safety Engineering, School of Municipal and Environmental Engineering, Henan University of Urban Construction, Pingdingshan, 467041, China

DOI: https://doi.org/10.36956/eps.v3i1.849

Received: 24 April 2023; Received in revised form: 11 December 2023; Accepted: 16 April 2024; Published: 30 April 2024

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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

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