A Bayesian Fusion Framework for Characterizing Marine Ducts Using Multisource Prior Constraints

The study of surface-layer evaporation ducts (EDs) maintains persistent scientific significance due to their critical role in modulating tropospheric propagation. While multiple ED quantification methodologies exist, the inherent complexity of these atmospheric structures radically constrains the observational completeness achievable through singular technologies. To address this limitation, we develop a novel Bayesian fusion framework that systematically integrates multisource knowledge through probabilistic reasoning to estimate ED from point-to-point (PTP) propagation loss (PL) observations. This approach reformulates the inverse problem as a maximum likelihood estimation challenge, systematically synthesizing prior ED distributions (derived from diverse knowledge sources) with PL-constrained likelihood functions through a rigorous Bayesian approach. This probabilistic integration enables robust determination of posterior ED distributions while inherently quantifying retrieval uncertainties. Validation experiments using a dedicated dataset confirm the framework’s viability, demonstrating sufficient accuracy to support real-world marine applications.