Geo-intelligence remote sensing platforms situated over spatially diverse areas are often tasked with geo-intelligence surveillance and adversarial monitoring for military organizations. Limited resources disallow continuous sampling of local areas at the same time, necessitating a need for smart sensing of diverse environments according to a rational evidence-based rule. Such algorithms should not only provide insight into which local region should be focused on, but should also facilitate decisions as to which environmental features should be measured over time once a local site has been selected. Multicomponent optimal learning observational arrays are demonstrated using numerically simulated data of turbulent flow to show not only the feasibility of how individual observational platforms should be chosen in a Bayesian sense, but also how goal state directed sampling of complex systems or turbulent processes over local regions can be accomplished. A Bayesian amalgamation algorithm guides which observational arrays perform knowledge gradient policy based optimal learning to smartly sample observations in local regions. Machine learning and operations research algorithms function as data agnostic, Bayesian processors demonstrating how geo-intelligence information can be efficiently captured to help solve data-driven problems.
Direct numerically simulated data can serve as a proxy for understanding many issues concerning multidimensional remotely sensed data. As a step towards performing operational Bayesian belief network modeling for rivers, which is of practical utility to naval intelligence, direct numerically simulated sediment-laden oscillatory flow is used to estimate statistical surface layer spatial eddy scales. This is done using spatial realizations of the sediment concentration, vertical velocity, and pressure fields along with feature extraction algorithms which utilize self-organizing mapping, independent component analysis, and two-dimensional omnidirectional Morlet wavelet analysis. Stress versus scale distributions exhibit distinct phase modulation over the three ambient forcing phases of maximum negative velocity, zero velocity, and maximum positive velocity. The stress versus sediment concentration scale distribution, which is of great pertinence to riverine remote sensing, exhibits a significant amount of large eddy scales suggesting coherent large-scale sediment structure formation possibly due to particle interstitial forces. The estimated statistical results can serve as feature parameters for naïve Bayesian belief network prediction of bottom boundary layer stress from surface eddy scale observations.
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.
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