About the project
The project will investigate passive acoustic monitoring in under-ice environments, combining acoustic propagation modelling with machine learning and data analytics to characterise environmental conditions, improve understanding of ice-generated acoustic processes and their influence on sonar performance, and exploit this information to enhance under-ice situational awareness and maritime sensing capabilities.
The Arctic and High North are becoming increasingly important operating environments, however the under-ice acoustic environment is a highly dynamic and complex physical system which remains difficult to observe, predict and exploit, and for sonar systems to operate reliably. Sea ice processes such as fracturing, ridging, deformation and movement influence the generation, propagation and scattering of sound and, consequently, sonar performance. Improved understanding of these system interactions is critical for sensing and situational awareness in under-ice environments.
This project will investigate how passive acoustic monitoring can be used to characterise and understand the under-ice environment. Rather than relying solely on active sonar, the research will examine whether naturally occurring ice-generated sounds can provide exploitable information about environmental conditions. A particular focus will be placed on identifying, classifying and interpreting the acoustic signatures associated with physical ice processes and understanding their relationship to environment properties and acoustic behaviour.
The project will combine physics-based acoustic propagation modelling, signal processing, statistical analysis and machine learning to develop methods for extracting environmental information from passive acoustic observations. By integrating physics-based and data-driven approaches the research aims to identify links between observed acoustic characteristics and key environmental parameters, enabling improved environmental characterisation where direct measurements are challenging or unavailable.
The outcomes will improve understanding of under-ice acoustic environments and demonstrate how passive acoustic information can support environmental characterisation, sonar performance prediction and operational decision making. Potential applications include improved under-ice situational awareness and more effective use of maritime sensing systems in complex ice-covered regions.
