A Bayesian Framework with Geology-Informed Structural Priors for Tomographic Reconstruction and Uncertainty Quantification

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ID: 328378
2026
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Abstract
Abstract Tomographic reconstruction involves fundamental trade-offs between resolution, data coverage, and parameterisation. Conventional approaches commonly rely on regularised pixel-wise or cell-based parameterisations. These regularisations are valuable and can themselves be geology-informed when their assumptions match the target setting. However, they often encode structural information only indirectly through generic penalty terms and regularisation weights. As a result, they can be less suited to questions in which the objective is to test a specific structural hypothesis. We develop a Bayesian framework for tomographic reconstruction and uncertainty quantification that formulates such hypotheses explicitly and assesses them through posterior uncertainty. As an illustrative application of the framework, we consider ambient noise tomography. The phase velocity reconstruction is reformulated to address a targeted geological hypothesis by introducing a parameterised structural prior. The prior is constructed from a Whittle–Matérn latent Gaussian field, combined with spectral dimensional reduction via a truncated Karhunen–Loève expansion and a differentiable nonlinear pushforward mapping that promotes approximately piecewise-constant velocity structures. The prior parameters, including the correlation length, smoothness, pushforward slope, and velocity bounds, provide interpretable hyperparameters related to expected lateral scale, structural regularity, transition sharpness, and plausible phase-velocity contrasts. In this formulation, the prior is used as a parameterised structural assumption informed by geological context, rather than as hard geological knowledge. The resulting parameterisation substantially reduces dimensionality, making both maximum a posteriori estimation and Hamiltonian Monte Carlo sampling computationally feasible for the synthetic and observed datasets considered here. Synthetic experiments demonstrate accurate recovery of prescribed velocity contrasts and show that posterior uncertainty patterns reflect acquisition geometry and data coverage. Application to a ∼1000-sensor dense array dataset produces structures consistent with the imposed geology-informed prior assumptions, while providing spatially resolved uncertainty estimates that identify regions of robust inference and remaining ambiguity. The framework offers a proof-of-concept formulation for goal-oriented, uncertainty-aware seismic tomography when prior information supports an explicit structural hypothesis that can be encoded through the proposed Whittle–Matérn latent-field parameterisation.
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Authors H Yazdanian, G Hillers, Y Lu, B Maboudi Afkham
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Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag374
URL
https://doi.org/10.1093/gji/ggag374
Keywords Keywords not found

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