We use nonlinear least squares methods and Bayesian inference to calibrate soil properties using models for heat and groundwater transport in the shallow subsurface. We first assume a constant saturation in our domain and use the analytic solution to the heat equation as a model for heat transport. We compare our results to those using the finite element code, Adaptive Hydrology (ADH). We then use ADH to simulate heat and groundwater transport in an unsaturated domain. We use the Model-Independent Parameter Estimation (PEST) software to solve the least squares problem with ADH as our model. In using Bayesian inference, we employ the Delayed Rejection Adaptive Metropolis (DRAM) Markov chain Monte Carlo algorithm to sample from the posterior densities of parameters in both models. We find our results are consistent with those found using soil samples with empirical methods.
|School:||North Carolina State University|
|School Location:||United States -- North Carolina|
|Source:||DAI-B 77/10(E), Dissertation Abstracts International|
|Subjects:||Applied Mathematics, Statistics, Soil sciences|
|Keywords:||Groundwater transport, Shallow subsurface, Soil properties, Thermal diffusivity|
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