Project: A new framework for Experimental Design in Earth Sciences using Bayesian Evidential Learning (BEL4ED)
2019-01-01 – 2022-12-31
- Abstract
Earth Sciences predictions are facing large uncertainty related to the complexity and the lack of knowledge of the subsurface. Acquiring the most informative data set to reduce uncertainty is therefore highly valuable. However, its identification rapidly becomes intractable for large scale
problems. We propose to stochastically solve this problem under large uncertainty using our newly developed Bayesian Evidential Learning framework.
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Machine learning for Bayesian experimental design in the subsurface
(2023) -
- Journal Article
- A1
- open access
Comparing well and geophysical data for temperature monitoring within a Bayesian experimental design framework
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- Conference Paper
- C3
- open access
Comparing well and geophysical data for temperature monitoring within a Bayesian Experimental Design framework
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- Conference Paper
- C3
- open access
A new framework for experimental design using Bayesian Evidential Learning : the case of wellhead protection area