Sampling-Free Bayesian Inference for Local Refinement in Linear Inversion Problems With a Latent Target Property
Author:
Affiliation:
1. Norwegian Computing Center, Oslo, Norway
2. Department of Mathematics, Group for Statistics and Data Science, University of Oslo, Oslo, Norway
Funder
Geophysical Inversion to Geology (GIG) Consortium
Aker BP
ConocoPhillips
Equinor
Lundin Energy Norway
Total
Vår Energi
Wintershall Dea
Sharp Reflections
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10207043.pdf?arnumber=10207043
Reference30 articles.
1. Improved resolution in Bayesian lithology/fluid inversion from prestack seismic data and well observations: Part 1 — Methodology
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3. Use of Machine Learning to Estimate Statistics of the Posterior Distribution in Probabilistic Inverse Problems—An Application to Airborne EM Data
4. Multi-scenario multi-realisation seismic inversion for probabilistic seismic reservoir characterization;waters;Proc 80th EAGE Conf Exhib,2018
5. Rapid spatially coupled AVO inversion
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