Inversion of Gravity Data by Total Variation Constraint Using Alternative Direction Method of Multipliers

Author

Assistant Professor, College of Engineering, Malayer University, Malayer, Iran

Abstract

This paper addresses the challenge of accurately detecting subsurface structures through gravity inversion, a crucial task in geophysical exploration. Traditional methods, particularly those based on Tikhonov regularization, often yield blurred models due to the inherent smoothness introduced by quadratic penalties. This research aims to develop a sparse inversion method that utilizes L1 norm regularization combined with total variation (TV) penalties to achieve a focused representation of subsurface boundaries. The methodology employs an alternative direction method of multipliers (ADMM) algorithm for optimization, enhanced by an adaptive method for selection of a regularization parameter and conjugate gradient methods to improve computational efficiency. The Alternating Direction Method of Multipliers (ADMM) is a versatile and powerful optimization technique that excels in solving convex optimization problems with separable objectives and linear constraints.The proposed method is validated through synthetic tests and real data analyses, demonstrating its effectiveness in accurately reproducing subsurface structures and sharp boundaries. The implications of this study extend to improved interpretation of geophysical data, facilitating more precise subsurface modeling.

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References

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Volume 8, Issue 4
October 2030
Pages 341-350

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