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20192023
most citedModeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression

1 citations · 1 across the 5 of their papers we have counts for

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Showing 2019 · physics.geo-phShow all

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physics.geo-ph2019

Approximate Bayesian inference of seismic velocity and pore pressure uncertainty with basin modeling, rock physics and imaging constraints

Anshuman Pradhan, Huy Q. Le, Nader C. Dutta +2

We present a methodology for quantifying seismic velocity and pore pressure uncertainty that incorporates information regarding the geological history of a basin, rock physics, wel…

physics.geo-ph2019

Seismic Bayesian evidential learning: Estimation and uncertainty quantification of sub-resolution reservoir properties

Anshuman Pradhan, Tapan Mukerji

We present a framework that enables estimation of low-dimensional sub-resolution reservoir properties directly from seismic data, without requiring the solution of a high dimension…