
Bayesian inversion: an important method and challenge for seismic source parameter inversion research
Zhe WANG, YunHua LIU
Prog Geophy ›› 2024, Vol. 39 ›› Issue (5) : 1771-1787.
Bayesian inversion: an important method and challenge for seismic source parameter inversion research
The role of geophysical inversion in seismic research and prediction is of paramount importance. This paper seeks to comprehensively outline the constraints associated with conventional inversion techniques, focusing on the introduction of a Bayesian-based uncertainty inversion method. Bayesian inversion involves computing posterior distributions utilizing diverse prior distributions and likelihood functions, with special emphasis on established techniques like the Markov Chain Monte Carlo (MCMC) and variational inference methods, thereby augmenting the reliability of inversion outcomes.The manuscript furnishes an elaborate exposition on pivotal techniques within Bayesian inversion, notably delving into regularization methods (such as Laplace and von Karman regularization) that confine the parameter space in seismic inversion, validated through rigorous case studies. Moreover, it expounds on sampling methodologies (including the Metropolis-Hastings algorithm and Gibbs sampling) that facilitate parameter space sampling and approximate posterior distributions. The application of the Metropolis-Hastings algorithm in seismic inversion is meticulously elucidated.The discussion accentuates the criticality of model parameter selection, notably the influence of uncertainty associated with fault geometric shape selection on inversion results. Additionally, it probes into the challenges encountered in constructing finite fault source models and presents a Bayesian-based case study evaluating the credibility of different slip model clusters.In conclusion, the paper summarizes the limitations inherent in the Bayesian approach and delineates potential avenues for future research directions. In the realm of geophysical inversion, the application of Bayesian methods presents novel prospects for overcoming the constraints of traditional methodologies.
Bayesian methods / Geophysical inversion / Regularization methods / Sampling methods / Model parameters / Model evaluation
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感谢审稿专家提出的修改意见和编辑部的大力支持!
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