Non-intrusive mode based analyses to predict uncertainty in sound propagation in the ocean

Ocean dynamics can result in significant sound speed variability that are not well captured by deterministic numerical models and can impact differently high to low frequency acoustic propagation and bottom interactions by constraining horizontal and vertical paths. Uncertainty in the sound speed can, therefore, cause inaccuracies in acoustic based tactical decision tools and severely impact the accuracy of algorithms using sound propagation to estimate ocean volume states (e.g., ocean tomography or data assimilation). In this work, we propose to use a non-intrusive Reduced Order Modelling solution that consists of building a dictionary of static modes from historical ocean simulations with different settings and resolutions and climatology to derive an expedite uncertainty model along a central deterministic forecast. The system will then, through Orthogonal Matching Pursuit along the dictionary, use the available ocean model-data mismatches and/or acoustic innovations (e.g., arrival numbers and times, acoustic energy distribution, etc.) to define an ensemble of possible sound speed volume distributions and associated acoustic propagation outcomes. We will show results using a simplified simulation experiment that extracts “observations” from a nature reference field to document the procedure and benchmark results to reconstruct 3D sound speed fields from ocean-acoustic measurements.