Bayesian Geophysical Inversion using Stein Mixture Variational Inference for Multimodal or Complex Solutions

Abstract

Bayesian geophysical inversion provides a probabilistic framework to estimate subsurface properties, in which uncertainties are represented by a so-called posterior probability density function (pdf). Estimating this pdf is computationally challenging when it is high dimensional or complex, particularly if it may be multimodal. We introduce Stein mixture variational inference (SMVI) for efficient Bayesian geophysical inversion with multimodal or complex posterior solutions. The method combines parametric variational inference with Stein variational gradient descent (SVGD). In SMVI, the posterior pdf is approximated by a mixture (sum) of distributions, in which component distributions are optimised jointly using the SVGD update rule. This construction combines computational efficiency with the flexibility to represent a range of plausible Earth models and allows components to be updated in parallel. Here, SMVI is first tested on two synthetic seismic inverse problems, demonstrating that SMVI can recover non-Gaussian, complex and multimodal posterior solutions accurately and efficiently. Then, we apply SMVI to real seismic data by performing Love wave tomography of the British Isles, and obtain inversion results that are consistent with previous Monte Carlo solutions. Around the East Irish Sea sedimentary basins, SMVI captures bimodal uncertainty associated with ambiguity in basin boundary locations, revealing alternative subsurface structures compatible with the observations. These results indicate that SMVI generalises various previous variational methods, and provides a practical and effective framework for quantifying complex uncertainties and supporting geological interpretation of seismic images.

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BibTeX RIS

BibTeX

@misc{zhao2026bayesian,
  title = {{Bayesian Geophysical Inversion using Stein Mixture Variational Inference for Multimodal or Complex Solutions}},
  author = {Xuebin Zhao and Andrew Curtis},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.23603},
  eprint = {2610.23603},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.23603},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Bayesian Geophysical Inversion using Stein Mixture Variational Inference for Multimodal or Complex Solutions
AU  - Xuebin Zhao
AU  - Andrew Curtis
PY  - 2026
DA  - 2026/10/16
PB  - arXiv
DO  - 10.48550/arxiv.2610.23603
UR  - https://arxiv.org/abs/2610.23603
AB  - Bayesian geophysical inversion provides a probabilistic framework to estimate subsurface properties, in which uncertainties are represented by a so-called posterior probability density function (pdf). Estimating this pdf is computationally challenging when it is high dimensional or complex, particularly if it may be multimodal. We introduce Stein mixture variational inference (SMVI) for efficient Bayesian geophysical inversion with multimodal or complex posterior solutions. The method combines parametric variational inference with Stein variational gradient descent (SVGD). In SMVI, the posterior pdf is approximated by a mixture (sum) of distributions, in which component distributions are optimised jointly using the SVGD update rule. This construction combines computational efficiency with the flexibility to represent a range of plausible Earth models and allows components to be updated in parallel. Here, SMVI is first tested on two synthetic seismic inverse problems, demonstrating that SMVI can recover non-Gaussian, complex and multimodal posterior solutions accurately and efficiently. Then, we apply SMVI to real seismic data by performing Love wave tomography of the British Isles, and obtain inversion results that are consistent with previous Monte Carlo solutions. Around the East Irish Sea sedimentary basins, SMVI captures bimodal uncertainty associated with ambiguity in basin boundary locations, revealing alternative subsurface structures compatible with the observations. These results indicate that SMVI generalises various previous variational methods, and provides a practical and effective framework for quantifying complex uncertainties and supporting geological interpretation of seismic images.
ER  -

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