Probabilistic inversion of seismic data for reservoir petrophysical characterization: Review and examples
The physics that describes the seismic response of an interval of saturated porous rocks with
known petrophysical properties is relatively well understood and includes rock physics …
known petrophysical properties is relatively well understood and includes rock physics …
Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities
J Jia, W Ye - Remote Sensing, 2023 - mdpi.com
Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster
prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in …
prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in …
Reliable amortized variational inference with physics-based latent distribution correction
Bayesian inference for high-dimensional inverse problems is computationally costly and
requires selecting a suitable prior distribution. Amortized variational inference addresses …
requires selecting a suitable prior distribution. Amortized variational inference addresses …
WISE: Full-waveform variational inference via subsurface extensions
We introduce a probabilistic technique for full-waveform inversion, using variational
inference and conditional normalizing flows to quantify uncertainty in migration-velocity …
inference and conditional normalizing flows to quantify uncertainty in migration-velocity …
Reweighted variational full-waveform inversions
Starting from an initial model and predefined priors, a variational full-waveform inversion
(VFWI) seeks posterior distributions of model parameters via optimization using a Bayesian …
(VFWI) seeks posterior distributions of model parameters via optimization using a Bayesian …
Bayesian inversion, uncertainty analysis and interrogation using boosting variational inference
Geoscientists use observed data to estimate properties of the Earth's interior. This often
requires non‐linear inverse problems to be solved and uncertainties to be estimated …
requires non‐linear inverse problems to be solved and uncertainties to be estimated …
Learned multiphysics inversion with differentiable programming and machine learning
Abstract We present the Seismic Laboratory for Imaging and Modeling/Monitoring open-
source software framework for computational geophysics and, more generally, inverse …
source software framework for computational geophysics and, more generally, inverse …
Physically structured variational inference for Bayesian full waveform inversion
Full waveform inversion (FWI) creates high resolution models of the Earth's subsurface
structures from seismic waveform data. Due to the non‐linearity and non‐uniqueness of FWI …
structures from seismic waveform data. Due to the non‐linearity and non‐uniqueness of FWI …
Geostatistical inversion for subsurface characterization using Stein variational gradient descent with autoencoder neural network: an application to geologic carbon …
Geophysical subsurface characterization plays a key role in the success of geologic carbon
sequestration (GCS). While deterministic inversion methods are commonly used due to their …
sequestration (GCS). While deterministic inversion methods are commonly used due to their …
Interrogating subsurface structures using probabilistic tomography: an example assessing the volume of irish sea basins
The ultimate goal of a scientific investigation is usually to find answers to specific, often low‐
dimensional questions: what is the size of a subsurface body? Does a hypothesized …
dimensional questions: what is the size of a subsurface body? Does a hypothesized …