Enhancing Deep-Sea Sediment Plume Tracking Using Adaptive Sampling on Autonomous Underwater Vehicles

Deep sea mining, if pursued, will have an associated environmental impact. In particular, sediment plumes have the potential to spread beyond the immediate mining site, smothering benthic habitats and disrupting biological processes. The temporal and spatial variability of the plumes presents a challenge to monitor using static measurement techniques such as moorings and landers. This paper considers the potential of utilising Autonomous Underwater Vehicles (AUVs) conducting adaptive sampling for tracking and sampling within sediment plumes generated by deep-sea mining operations. The proposed approach enables real-time decision-making for efficient plume boundary and concentration tracking, leveraging collaborative multi-AUV strategies. Simulation results demonstrate the effectiveness of the approach in improving coverage and accuracy, with clear advantages over traditional static sampling methods.