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Found 3 papers, 3 papers with code
Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework
We use Bayesian Evidential Learning (BEL), a Monte Carlo-based training approach, to optimize the design of a 4D temperature field monitoring experiment.
A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area
The uncertainty range of the posterior WHPA distribution is affected by the number and position of data sources (injection wells).
Deep generative models in inversion: a review and development of a new approach based on a variational autoencoder
When solving inverse problems in geophysical imaging, deep generative models (DGMs) may be used to enforce the solution to display highly structured spatial patterns which are supported by independent information (e. g. the geological setting) of the subsurface.
Geophysics
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