Parameter identification is one of the key elements in the construction of models in
geosciences. However, inherent difficulties such as the instability of ill-posed problems or …

Deeply buried fractured reservoirs have evolved into significant oil and gas potential in
many basins of the world. However, fracture prediction in deeply buried carbonate reservoirs …

We consider the coupled system of equations that describe flow in fractured porous media.
To describe such types of problems, multicontinuum and multiscale approaches are used …

Bayesian methods for full-waveform inversion allow quantification of uncertainty in the
solution, including determination of interval estimates and posterior distributions of the …

Naturally fractured reservoirs contribute to a large percentage of the world's oil and gas
reserves. Therefore, the nature, density, and geometry distribution of fractures in the …

In this paper, we consider the numerical solution of the poroelasticity problem with stochastic
properties. We present a Two-stage Markov Chain Monte Carlo method for geomechanical …

Conventional neural networks (NNs) have been extensively used to model the spatial
heterogeneity of rock properties from seismic inversion. Nevertheless, these generic NNs …

Any search or sampling algorithm for solution of inverse problems needs guidance to be
efficient. Many algorithms collect and apply information about the problem on the fly, and …

In this paper we propose a general framework for the uncertainty quantification of quantities
of interest for high-contrast single-phase flow problems. It is based on the generalized …

In this paper, we investigate inverse obstacle scattering problems in acoustic waveguides
with low-frequency data. A Bayesian inference scheme, combining a multi-fidelity strategy …