2D magnetotelluric inversion using hybrid stabilizing functionals: Exponential minimum support and smoothness

Yu, Peng; Wang, Cheng; Zhang, Luolei; Zhao, Chongjin; Shi, Bo; Hu, Ming

doi:10.1190/geo2020-0485.1

Cited by 3 publications

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

2-D probabilistic inversion of MT data and uncertainty quantification using the Hamiltonian Monte Carlo method

Peng,

Han,

et al. 2024

Geophysical Journal International

View full text Add to dashboard Cite

Summary Bayesian methods provide a valuable framework for rigorously quantifying the model uncertainty arising from the inherent non-uniqueness in the magnetotelluric (MT) inversion. However, widely-used Markov chain Monte Carlo (MCMC) sampling approaches usually require a significant number of model samples for accurate uncertainty estimates, making their applications computationally challenging for 2D or 3D MT problems. In this study, we explore the applicability of the Hamiltonian Monte Carlo (HMC) method for 2D probabilistic MT inversion. The HMC provides a mechanism for efficient exploration in high-dimensional model space by making use of gradient information of the posterior probability distribution, resulting in a substantial reduction in the number of samples needed for reliable uncertainty quantification compared to the conventional MCMC methods. Numerical examples with synthetic data demonstrate that the HMC method achieves rapid convergence to the posterior probability distribution of model parameters with a limited number of model samples, indicating the computational advantages of the HMC in high-dimensional model space. Finally, we applied the developed approach to the COPROD2 field dataset. The statistical models derived from the HMC approach agree well with previous results obtained by 2D deterministic inversions. Most importantly, the probabilistic inversion provides valuable quantitative model uncertainty information associated with the resistivity structures derived from the observed data, which facilitates model interpretation.

show abstract