Bayesian belief network modeling of direct numerically simulated imagery variables for sub-surface structure diagnostics

Nicholas V. Scott; Tian-Jian Hsu

doi:10.1117/12.2301255

30 April 2018 Bayesian belief network modeling of direct numerically simulated imagery variables for sub-surface structure diagnostics

Nicholas V. Scott, Tian-Jian Hsu

Author Affiliations +

Proceedings Volume 10649, Pattern Recognition and Tracking XXIX; 1064912 (2018) https://doi.org/10.1117/12.2301255
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Naïve Bayesian belief network modeling is applied to direct numerically simulated imagery of oscillatory sedimentladen flow to illustrate the feasibility of creating a system model which captures the statistical interrelationship of the surface layer sediment concentration, pressure, and vertical velocity eddy scales with the sub-surface Reynolds stress. From a prognostic reasoning viewpoint, preliminary model results suggest that large sediment concentration eddy scales may result from the application of large positive Reynolds stress. However, from a diagnostic reasoning viewpoint, initial results suggest that robustly inferring sub-surface boundary layer stress from surface sediment concentration eddy scales may be a difficult task. The model formulism used allows for the ability to statistically characterize flow structure at depth from observations taken across a surface boundary layer, making the results relevant to image analysis at the airsea interfacial boundary layer in large-scale coastal and riverine systems.

Citation Download Citation

Nicholas V. Scott and Tian-Jian Hsu "Bayesian belief network modeling of direct numerically simulated imagery variables for sub-surface structure diagnostics", Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 1064912 (30 April 2018); https://doi.org/10.1117/12.2301255

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