Discipline: Mathematics & Statistics
Subcategory: STEM Research
- Central State University
Co-Author(s): Jarrett Barber, Northern Arizona University, Flagstaff, AZ; Victor Ginting, University of Wyoming, Laramie, WY; Felipe Pereira, University of Texas at Dallas, Richardson, TX
In contaminant transport in subsurface we often need to forecast flow patterns. In the flow forecasting, subsurface characterization is an important step. To characterize subsurface properties we establish a statistical description of the subsurface properties that are conditioned to existing dynamic (and static) data. We use a Markov chain Monte Carlo algorithm in a Bayesian statistical description to reconstruct the spatial distribution of two important subsurface properties: permeability and porosity. By using reconstructed permeability and porosity distributions, we predict subsurface flows. In this poster, we present a Bayesian framework for predictive simulation of contaminants in an aquifer.
Funder Acknowledgement(s): NSF's HBCU-UP (Award Abstract No: 1016283)
Faculty Advisor: None Listed,