A team of researchers at the University of Bath developed an artificial intelligence (AI) model which is able to automatically classify underwater environments directly from sonar measurements.
Sonar is usually used to map the ocean floor, and seabed composition affects the way the sound is reflected back. Salinity, depth and water temperature also affect how sound waves are propagated through water.
In collaboration with Systems Engineering & Assessment Ltd (SEA), scientists at the University’s Institute for Mathematical Innovation (IMI) have developed an Artificial Intelligence (AI) algorithm which could improve underwater mapping by making sense of incomplete data and working out how many measurements are needed to give an accurate survey.
The research is included in a project contracted by The Defence and Security Accelerator (DASA), a part of the Ministry of Defence, to improve monitoring of the UK’s vast marine territories using high tech sonar. SEA led the project and provided simulated sonar data to train and test the AI algorithms developed by the IMI.
Senior Lecturer Dr Philippe Blondel, from the University’s Centre for Space, Atmospheric and Oceanic Science, worked on the project alongside Machine Learning expert Professor Mike Tipping from the IMI.
Dr Blondel stated that
There are lots of different variables that affect how sound waves are propagated in water, as some frequencies of sound can travel further than others. If you think about the sound of an orchestra, as you move further away, you might lose the high frequency sound of the violins but still be able to hear the lower frequency notes of the cellos. The beating of drums would be felt even further.
Firstly, the researchers analyzed the many characteristics of underwater environments and classified them into different types. They used Probabilistic Generative Modelling to develop several AI algorithms for identifying underwater environments. After developing the AI algorithm, the researchers tested its performance on a wide range of simulated acoustic data representing a broad spectrum of underwater environments.
Overall, the researchers believe that the technique could be used in the future to monitor the effects of climate change.
