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Marine Soundscapes – discriminating and understanding biological and anthropogenic sources from autonomous vehicles


Project Description

Project Rationale:
Sound is central to the way marine mammals and many fish species communicate, search for prey, and navigate to avoid predators and hazards. Anthropogenic noise sources may mask these biological biologically important sounds and so may cause physiological and behavioural impacts, that may extend over large areas of the marine environment.
One of the new frontier areas of ocean exploration is using fleets of marine autonomous vehicles to collect oceanographic data. Improvements in battery efficiency and computer technology mean that Passive Acoustic Monitoring (PAM) devices can now be carried on these vehicles enabling soundscapes within the oceans to be determined at unprecedented temporal and spatial resolution. These PAM devices record huge volumes of data that require new signal processing methodologies and big data approaches to enable effective real-time data transfer and post-mission analysis. This project will therefore develop machine-learning techniques to discriminate between different sources of sound, using PAM data collected from fleets of marine autonomous vehicles.
As well as developing our understanding of sound in the oceans, the techniques developed in this project are directly relevant to monitoring of natural seabed seeps as well as leaks from industrial infrastructure (pipelines and rigs), and from carbon capture and storage projects.

Methodology :
This project will determine the relative intensities of natural and anthropogenic sounds in the marine environment using data from acoustic recorders mounted on autonomous systems and fixed buoys. A particular focus for this project will be data collected using Gliders - underwater autonomous systems that use an internal pump to change their buoyancy to move up and down in the water. These systems are intrinsically quiet, and so are platforms well suited for recording sounds in the oceans. Data will come from the MASSMO project (http://projects.noc.ac.uk/massmo/frontpage) as well as from other datasets collected around the world’s oceans. The PhD student will go to sea to collect data, and process and interpret this data.
The student will develop software tools for analysing large acoustic datasets. After training, the student will apply machine learning techniques to enable discrimination of sounds from different sources (biological, seabed seeps, industrial infrastructure, shipping, hydrocarbon exploration). We shall exploit supervised learning techniques to manually label a subset of the data for training purposes. The student will also consider methods for determining the location of sounds sources using multiple receivers on a mobile platform. The propagation of the various sound sources in the ocean will be determined.

Training:
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. The student will be registered at the University of Southampton and hosted at the School of Ocean and Earth Science at the National Oceanography Centre Southampton. Specific training will include attendance at courses in the Faculty of Engineering and Physical Sciences on marine acoustics, signal processing and/or machine learning. In addition, there are a wide range of masters level modules available in Oceanography at NOCS, and it would be expected that some of these modules would be taken, depending on the background of the candidate (e.g. Physical Oceanography, Marine Geology and Geophysics).

Funding Notes

You can apply for fully-funded studentships (stipend and fees) from INSPIRE if you:
Are a UK or EU national.
Have no restrictions on how long you can stay in the UK.
Have been 'ordinarily resident' in the UK for 3 years prior to the start of the project.

Please click link to View Website for more information on eligibilty and how to apply

References

1. Solan, M., Hauton, C., Godbold , J.A., Wood, C.L., Leighton, T.G. and White, P.,Anthropogenic sources of underwater sound can modify how sediment-dwelling invertebrates mediate ecosystem properties, Scientific Reports, 6, 1-24, 2016, (doi:10.1038/srep20540).
2. Gruden, P. and White, P. R., Automated tracking of dolphin whistles using Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters, The Journal of the Acoustical Society of America, 140(3), 1981-1991, 2016 (doi: 10.1121/1.4962980).
3. Zhang, Z, and White, P.R.. A blind source separation approach for humpback whale song separation. The Journal of the Acoustical Society of America 141(4), 2017: 2705-2714.(doi:10.1121/1.4980856)

How good is research at University of Southampton in Earth Systems and Environmental Sciences?

FTE Category A staff submitted: 68.62

Research output data provided by the Research Excellence Framework (REF)

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