15 citations · 15 across the 8 of their papers we have counts for
5 papers · 1 filter
Bayesian Inversion Beyond the Generator's Range: Reducing Non-physical Constraints from Generative Neural Networks
Pratyay Roy, Xuebin Zhao, Andrew Curtis
To address non-uniqueness in geophysical inversions, Generative Neural Networks (GNNs) are often used to parameterise geological or other prior information in a lower-dimensional s…
Response to: "A note on conditional densities, Bayes' rule, and recent criticisms of Bayesian inference" by Yan et al., 2026
Klaus Mosegaard, Andrew Curtis
In a recent preprint (Mosegaard and Curtis, 2024, arXiv:2411.13570v2) we analyzed the consequences of ignoring the well-known inconsistency of classical conditional probability den…
Designing Solutions to Geophysical Inverse Problems by Changing Variables
Xuebin Zhao, Andrew Curtis, Klaus Mosegaard
Geoscientists often solve inverse problems to estimate values of parameters of interest given relevant data sets. Bayesian inference solves these problems by combining probability…
Linearised versus Nonlinear Estimates of Uncertainty in Full Waveform Inversion
Xuebin Zhao, Andrew Curtis
Seismic full waveform inversion (FWI) is a powerful technique to generate high resolution images of the Earth's interior. However, significant uncertainty exists in all FWI solutio…
Variational and Monte Carlo Methods for Bayesian Inversion of Dynamic Subsurface Flow Simulations Using Seismic and Fluid Pressure Data
Zhen Zhang, Xuebin Zhao, Andrew Curtis
In order to predict future performance of subsurface fluid reservoirs under possible operating scenarios, a dynamic, porous-medium flow simulation model must be tuned to include re…