Physics-Informed Deep Learning-Based Inversion of Marine Atmospheric Boundary-Layer Optical Duct Parameters from Free-Space Optical Link Measurements
Abstract
Marine boundary-layer temperature inversions—warm air advecting over a cold sea surface—can bend near-horizontal light rays back toward the sea surface, forming an optical duct that dramatically alters the propagation of free-space optical (FSO) links over the ocean: ducted rays yield anomalous low-loss, over-the-horizon reception alongside strong multipath interference and scintillation. Although extensive inversion techniques exist for microwave evaporation ducts, no study to date has inverted optical duct parameters directly from optical channel observations. This paper presents, for the first time, a physics-informed deep learning framework that reconstructs the inversion-layer parameters—inversion gradient, layer-top height, and surface air temperature—from multi-wavelength FSO channel measurements. A forward model based on Bouguer ray tracing in a spherically stratified atmosphere (validated by split-step Fourier parabolic-equation analysis) generates the training dataset: thousands of inversion profiles sampled by Latin hypercube design, propagated at 850/1550 nm over 5–50 km maritime links, yielding receiver arrival angles, multipath structures, geometric focusing gains, and chromatic dispersion separations. A physics-guided neural network with residual learning—the refractivity gradient constraint embedded in the loss—inverts these observables into profile parameters. Numerical experiments demonstrate high-accuracy inversion of the inversion gradient, the parameter…
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