BayesiaLab
Bayesian Structural Field Analysis

Bayesian Structural Field Analysis

Presented at the 7th Annual BayesiaLab Conference at the North Carolina Biotechnology Center.

Abstract

Environmental engineering remote sensing platforms using hyperspectral imagery are often responsible for monitoring coastal regions in order to safeguard national waters. This objective requires determining subsurface turbulent structure from surface water spatial measurements for flow state assessment and decision-making. The inability of remote sensing platforms to penetrate the water column at depth because of turbulence-induced sediment-concentration modulation necessitates using models that dynamically link surface and subsurface structures. A hidden Markov model is applied to large-eddy simulated three-dimensional turbulent flow for the purpose of exploring the feasibility of constructing a system model possessing turbulent state evolution diagnostic/prognostic statistical power. Parameters for a temporal Bayesian network model are estimated from data based on the Markov assumption utilizing data statistical covariance structure. Initial results suggest a strong nonlinear coupling between the mean flow-directed vorticity, cross-mean flow velocity, and sediment concentration. In addition, a Bayesian-based state-action estimation algorithm is employed that demonstrates which turbulent feature variables should be focused on at specific times, given the desire to reach a known goal state and given only a limited number of observations. Such a model gives experimentalists time- and resource-saving guidance for determining what turbulent variables to measure at different times in order to reach a known turbulent goal state. Overall, preliminary model analysis results set the stage for implementing and exploiting algorithms using high-level industrial Bayesian belief network software such as BayesiaLab.

Presentation Video

Presentation Slides

About the Presenter

Dr. Nicholas Scott is a modeling scientist and physical oceanographer and has been a member of the professional staff at Riverside Research in Dayton, OH, since October 2012. He investigates the applicability of traditional and non-traditional signal and image processing techniques to the extraction of information from remotely sensed imagery. This includes hyperspectral and multispectral imagery. His present work includes cognitive modeling of geo-intelligence information, sensor array time series analysis of environmental data, and the application of pattern recognition techniques to turbulent flow imagery and numerically simulated data. He is also involved in the application of probabilistic graphical modeling algorithms for information fusion and statistical inference.


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