Physics-Informed Multi-Scale Deep Learning Framework for Natural Hydrocarbon Seep Detection in SAR Imagery: A Methodological Proposal

The continuous monitoring of natural hydrocarbon seeps is critical for assessing marine ecosystem health and serving as a geophysical indicator of seafloor petroleum reserves. Synthetic Aperture Radar (SAR) systems, particularly Sentinel-1, provide weather-independent operational capabilities for detecting these seeps. However, purely data-driven deep learning models frequently struggle to distinguish true oil films from oceanographic look-alikes (e.g., wind shadows, biogenic slicks), generating high false-positive rates. To overcome this limitation, this study proposes a novel conceptual framework named "Physics-Informed Multi-Scale Deep Learning" (PI-MSDL). Unlike conventional segmentation architectures, PI-MSDL integrates classical fluid dynamics—specifically, simplified Navier-Stokes viscous dispersion principles—into the neural network's loss function. This methodological proposal outlines the architecture, expected operational metrics, and theoretical advantages of incorporating physical constraints to penalize non-physical predictions, thereby significantly enhancing detection reliability in complex marine environments.