Sparse Bayesian Magnetic Inversion Based on Fast Relevance Vector Machine

Tianjiao Chen

doi:10.1109/jsen.2026.3728752

Sparse Bayesian Magnetic Inversion Based on Fast Relevance Vector Machine

2026-09-04

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DOI：10.1109/jsen.2026.3728752

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Magnetic inversion faces challenges such as strong non-uniqueness, large computational scale, and low inversion efficiency under fine-grid discretization conditions, which seriously restrict its application in practical scenarios. To address the above issues, this paper introduces and adapts the Fast-RVM-driven sparse Bayesian learning (SBL) framework to the three-dimensional magnetic susceptibility inversion problem, forming an implementation method for fine-grid inversion. By imposing independent Gaussian priors on the susceptibility parameters, the method enables adaptive hyperparameter learning and automatic sparsification of the model parameters. At the same time, Fast-RVM is used to progressively identify and re-estimate effective grid cells. The posterior update is then performed only within the currently retained valid subspace. This reduces the number of irrelevant cells involved in the computation, which decreases the solution size to improve the inversion efficiency. Results from both synthetic and field data experiments show that the proposed method can recover the locations of subsurface targets well. Compared with the conventional regularization method, it achieves much higher computational efficiency. Its advantage becomes more evident under fine-grid conditions.

Keywords:

Fast Relevance Vector Machine

fine-grid discretization

magnetic susceptibility inversion

sparse Bayesian learning

Sparse Bayesian Magnetic Inversion Based on Fast Relevance Vector Machine

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