Machine Learning Inference of the Ocean Environment from Acoustic Data - Ref 42

Description

This PhD project is part of the CDT in Distributed Algorithms: The What, How and where of Next-Generation Data Science.

The University of Liverpool’s Centre for Doctoral Training in Distributed Algorithms (CDT) is working in partnership with the STFC Hartree Centre and 20+ external partners from the manufacturing, defence and security sectors to provide a 4-year innovative PhD training course that will equip over 60 students with: the essential skills needed to become future leaders in distributed algorithms; the technical and professional networks needed to launch a career in next generation data science and future computing; and the confidence to make a positive difference in society, the economy and beyond.

The successful PhD student will be co-supervised by Dr Stewart Haslinger and work alongside our external partner Dr Duncan Williams: Chief Scientist Acoustics, at Dstl.  Dstl - the science, technology and engineering arm of the UK’s Ministry of Defence (MOD). Dstl’s research and development activity in underwater acoustics has a high impact on MOD decision-making for current and future Royal Navy platforms during a time of accelerated development of underwater science and technology and enables the maximum operational effectiveness for UK submarines, ships, and other underwater systems.

PROJECT DESCRIPTION

The use of machine learning models to determine physical quantities of a complex underwater environment via acoustic data is relatively underdeveloped. Traditionally, research in underwater acoustics (the study of sound wave generation, propagation, scattering and reception in water) has mainly been applied for the use of sound wave navigation and ranging (SONAR) systems for communication, long-range sensing, target detection, marine wildlife monitoring and exploration.

However, recent breakthroughs in data mining and analysis, supported by powerful super-computing capabilities, promise exciting new possibilities for the implementation of machine and deep learning approaches to develop methods to extract and predict important properties of the ocean that are relevant to underwater acoustics. The main properties of interest have been the sound speed profile and sediment geo-acoustics for which there are established model-based inversion schemes. It is expected that these and many other acoustically-relevant properties, across multiple scales, from internal waves and eddies to ocean spice, together with their spatial and temporal variability could now be derived from acoustic data using new machine learning inference methods. The result would be a much richer and truer description of the ocean environment which would improve the prediction accuracy of acoustic models and the associated performance of sonar systems.

UK maritime forces are deployed 365 days a year across the world to provide a forward national presence that projects influence to safeguard our interests. This pattern of operational deployment depends on our ability to use and exploit the ocean environment. Consequently, the operational use of sonar systems depends on having a description of the ocean environment that includes all acoustically-relevant properties of the environment – the more complete this description is, the greater our ability to operate successfully.

Unfortunately, direct measurement of such properties is difficult and expensive and the quality of the output from acoustic models that need this description are typically limited by the information that is available. The availability of ocean environment information is equally important for our understanding of complex ocean processes and sustainable use of the oceans. Healthy oceans are vital for life on Earth: they buffer the globe from the effects of anthropogenic CO2, support biodiversity and marine life, provide critical resources and a means of global transportation.

The process of extracting information indirectly from measured acoustic data is called inversion. This acoustic measurement results from an acoustic signal that has propagated through the environment (changes to the signal result from different acoustically-relevant properties of the environment) before being received by a hydrophone or similar system. The acoustic measurement therefore contains information about the ocean environment that can be derived using suitable models and methods. The aim of this project is to use data-driven machine learning models to improve this resulting description of the ocean environment, to represent a wider range of ocean properties that are relevant to underwater acoustics, and to support our understanding, use and exploitation of the ocean environment.

The focus of the project will be on machine learning models that can be use acoustic data collected by in-situ sensors and remote sensors, modelled data, historical data, and data from other sources, to infer acoustically relevant properties of the ocean environment, from which to build an up-to-date and accurate representation of the acoustic environment for any sonar deployment.

This studentship is open to British and EU nationals who are willing and able to obtain  UK gov security clearance.

This project is due to commence on 1 October 2023.

Students will be based at the University of Liverpool and will be part of the CDT and Signal Processing  research community - a large, social and creative research group that works together solving tough research problems.  Students have two academic supervisors and an industrial partner who provide co-supervision, placements and the opportunity to work on real world challenges. In addition, students attend technical and professional training to gain unparalleled expertise to make a difference now and in the future.

The CDT is committed to providing an inclusive environment in which diverse students can thrive. The CDT particularly encourages applications from women, disabled and Black, Asian and Minority Ethnic candidates, who are currently under-represented in the sector.  We can also consider part time PhD students.  We also encourage talented individuals from various backgrounds, with either an UG or MSc in a numerate subject and people with ambition and an interest in making a difference. 

Please visit the Distributed Algorithms CDT website to discover more about the research work and the people who make it happen.

Contact the named supervisors in the first instance or visit the CDT website for Director, Student Ambassador and Centre Manager details.

Name and email address to direct enquiries to:  www.liverpool.ac.uk/distributed-algorithms-cdt

Application Web Address: Visit the CDT website for application instructions, FAQs, interview timelines and guidance.