Identification of Moving Sources in Stochastic Fields: A Bayesian Inferential Approach with Application to Marine Traffic in the Mediterranean Sea

A Bayesian inference approach for inferring the source of marine pollution released from a moving source in an uncertain flow field is proposed. A Markov Chain Monte Carlo (MCMC) algorithm is developed and applied for inferring single and multiple release events from vessels moving at known velocity along a predefined path in the Mediterranean Sea. The likelihood is based on a logistic regression cost function that measures the discrepancy between the modeled spill distribution and a binary representation of the observed images. We assess the performance of the proposed methodology using a synthetic release scenario employing realistic ocean currents to drive a stochastic Lagrangian Particle Tracking (LPT) algorithm to generate a probabilistic representation of the spill distribution. The MCMC algorithm employs an adaptive scheme to robustly ensure convergence and well-mixed chains. The proposed Bayesian framework is tested by inferring the location, or injection time, and relative contributions of single and multiple moving sources, contributing to separate and common observation patches, with a focus on various scenarios that demonstrate the efficiency of our sampling algorithm. The performance of the proposed framework was further assessed by comparing the model predictions with the most probable release parameters predicted by a global optimization algorithm.

Computational Geosciences