1 citations · 1 across the 5 of their papers we have counts for
5 papers
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression
Anshuman Pradhan, Kyra H. Adams, Venkat Chandrasekaran +4
Modeling groundwater levels continuously across California's Central Valley (CV) hydrological system is challenging due to low-quality well data which is sparsely and noisily sampl…
Bayesian Geophysical Basin Modeling with Seismic Kinematics Metrics to Quantify Uncertainty for Pore Pressure Prediction
Josue Fonseca, Anshuman Pradhan, Tapan Mukerji
Bayesian geophysical basin modeling (BGBM) methodology is an interdisciplinary workflow that incorporates data, geological expertise, and physical processes through Bayesian infere…
Consistency and prior falsification of training data in seismic deep learning: Application to offshore deltaic reservoir characterization
Anshuman Pradhan, Tapan Mukerji
Deep learning applications of seismic reservoir characterization often require generation of synthetic data to augment available sparse labeled data. An approach for generating syn…
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…
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…