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The University of Southampton
Ocean and Earth Science, National Oceanography Centre Southampton

Research project: Machine Learning for Optimal Unexploded Ordnance Mitigation

Currently Active: 
Yes

Unexploded Ordnance (UXO) pose a significant threat for onshore and offshore infrastructure projects throughout the world, but especially in north-west Europe. Characterization of offshore buried objects through the integration of magnetic and 3D chirp data has the potential to significantly reduce UXO mitigation costs.

Funding dates: Nov 2018 – May 2022

Unexploded Ordnance (UXO) pose a significant threat for onshore and offshore infrastructure projects throughout the world, but especially in north-west Europe. Current UK/EU legislation requires all infrastructure projects to conduct a UXO site investigation and mitigate the risk of any suspect items identified, through avoidance or removal. Offshore mitigation can be hugely time consuming and expensive, often involving the inspection and avoidance/removal of 1000s of targets using divers for shallow water and remote submersibles for deeper water. The weakest link in this process is our inability to differentiate during site investigation a potential UXO (which requires mitigating) from a false positive (e.g., boulder, which does not require mitigating unless it is a piling/drilling hazard). Multiple geophysical survey techniques (multibeam echosounder, sidescan sonar, transverse gradiometer, and decimetre-resolution 3D seismic reflection) can each characterise the bed and shallow subsurface in different ways at high resolution, but with significant ambiguity in target characterisation. Historically, these different data sets are treated in isolation, preventing effective reduction in ambiguity. However, during a recent major infrastructure project on the Thames, a joint qualitative integration of multiple data sources produced a 60% reduction in remediation operations.

Methodology: 

The research project will take this proof-of-concept work flow and further develop it using modern machine learning and big data methods. A range of machine learning techniques, including Neural Networks and Markov Chain methods, will be tested and compared to tackle two critical problems. Firstly, the optimal integration of different field data sets and automatic target identification. Secondly, the comparison of identified targets with a detailed library of UXO parameters based on previous detailed characterisation of relevant sites, to provide a probabilistic characterisation. Such a machine learning approach has the ability to learn from historical surveys and be easily scalable for large-scale application, reducing interpreter bias, cost, and lead-time.

Funding provider:

SAND Geophysics

SMMI

SPITFIRE

Related research groups

Geology and Geophysics
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