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A constrained Bayesian algorithm and software for 3-D density gravity inversion
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DOI：10.1093/gji/ggaf264.png)
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Abstract
En 中文
In this study, we present a new algorithm and accompanying software for 3-D gravity inversion of density structures. The algorithm combines the strengths of the Bayesian approach-which incorporates prior model information through a variogram model-with the advantages of the Tikhonov regularization framework to address the challenge of depth resolution. We also provide a detailed derivation of the procedure for calculating and fitting the 3-D experimental variogram, which serves as a fundamental input to the algorithm. The software implementing the proposed algorithm was developed using the widely adopted computational programming language Matlab. To evaluate its effectiveness, we conducted four representative experiments, ranging from simple to complex scenarios. The synthetic results demonstrate that incorporating model covariance constraints yields a more localized and better-focused density distribution compared to results obtained without such constraints. Additionally, we tested the algorithm's robustness by introducing noise into the observation data. The results show that the proposed method is resistant to noise and maintains strong performance. Finally, we applied the algorithm and software to real field data and compared the results with those from previous studies. The comparison confirms that our method is capable of producing reliable, high-resolution 3-D density models, with the added advantage of integrating prior information.
Keywords:
Geopotential theory
Gravity anomalies and Earth structure
Inverse theory
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