Cutting-Edge Sensing Technology Aids Study on Coastal Erosion and Cliff Collapse

Cutting-Edge Sensing Technology Aids Study on Coastal Erosion and Cliff Collapse

(IN BRIEF) The University of East Anglia’s School of Environmental Sciences has acquired cutting-edge fibre optic ground sensing equipment, funded by a £460k grant from the National Environment Research Council (NERC). This technology will be used to detect coastal erosion and cliff collapse processes on the North Norfolk Coast. The DiSTANS (Distributed Strain, Temperature and Acoustic seNsing Suite) Project, in collaboration with local authorities and the British Geological Survey, aims to map weak areas of cliffs prone to failure, monitor signs of weakness, and understand processes leading to collapse. The technology utilizes distributed sensing (DS), a novel approach using optical fibers, offering high spatial and temporal monitoring simultaneously. The project has potential applications in predicting cliff collapses and managing coastal erosion.

(PRESS RELEASE) NORWICH, 10-Aug-2023 — /EuropaWire/ — Following a successful capital grant of £460k from the National Environment Research Council (NERC) in 2022, the University of East Anglia’s (UEA) School of Environmental Sciences has acquired state-of-the-art fibre optic ground sensing equipment that will be deployed to detect the processes that lead to coastal erosion and cliff collapse on the North Norfolk Coast between Sidestrand and Trimingham.

On the national scale, coastal erosion is widespread, with around 28% of the English and Welsh coastline experiencing erosion rates of at least 10 cm/year. It is widely understood that the North Norfolk Coast is one of the hardest hit areas to experience the hazards associated with this process, such as cliff slumping and collapse – which impacts people living, working, and visiting the area.

Environmental change due to an altering climate will lead to a significant increase in these erosion rates. Cliff-top communities will have to live with increased erosion risk, making it crucial to understand erosional processes, including how and when they might threaten cliff-top buildings and other infrastructure. To address these issues, geophysical monitoring of the ground is required.

The DiSTANS (Distributed Strain, Temperature and Acoustic seNsing Suite) Project, led by UEA’s School of Environmental Sciences, in collaboration with North Norfolk District Council and the British Geological Survey, aims to map areas of the cliff that are weaker and more likely to fail in the future, monitor the coastline to find areas that show signs of weakness, and use collected data to further understand the processes that lead to cliff collapse.

Jessica Johnson, Associate Professor in Solid Earth Geophysics at UEA, said:

“We are proud to be working with Trimingham Parish Council and North Norfolk District Council, the people who are most affected by coastal erosion, on this pilot project. If successful, the project can easily be scaled up to monitor greater stretches of coastline continuously, which will aid decision-makers in planning and mitigating risk.”

The project utilises Distributed Sensing (DS), a brand-new technology that employs optical fibre in a similar way to radar. Special signal generators shoot light along the fibres at up to 100 kHz, with a proportion of the light reflecting off impurities in the fibre and returning to source. Changes to the travel of the pulses mean something has altered along the cable and precise measurements are taken of their timing and angle, allowing the researchers to work out whether this was due to the ground moving, changing temperature, or vibrating.

The 16km cable measures only 6mm wide and will work best buried at a depth of 10cm as close as is safely possible to the edge of the cliff. Traditional methods of subsurface monitoring can be restrictive, while DS introduces a new solution to geophysical monitoring – allowing high spatial and temporal monitoring to be carried out simultaneously.

The DiSTANS Project aims to answer a series of questions:

• Can DS be used to characterise erosion events and causes on the coast?
• How does recession rate vary over time and space in the context of climate change and degrading defences
• Can we monitor subsurface fluid and stresses using DS?
• Which areas display increased subsurface cracking and/or slumping?
• Ultimately, can we forecast cliff collapses?

Cllr. Harry Blathwayt, Portfolio Holder for the Coast at North Norfolk District Council said:

“We are pleased to work with UEA’s School of Environmental Sciences and the British Geological Survey on a technologically-advanced pilot project to study on erosion patterns.

“The impacts of coastal erosion are strongly felt here in North Norfolk as one of the hardest hit coastlines, and our communities are witness to the risks that rapid erosion poses. This is a really interesting area of research to understand if this type of technology could be used to help to predict cliff collapses and will complement Coastwise, which will be looking to work with communities to prepare for coastal erosion transition.”

To interpret the masses of information that will be produced through the project, two PhD students, Harry Whitelam and Dom Seager from UEA’s School of Computing Science will utilise a variety of innovative techniques and technologies to interpret this ‘big data’, including machine learning. Machine Learning is a subfield of Artificial Intelligence (AI) and is focused on enabling systems to learn and improve from experience without needing to be specifically coded to do so, essentially imitating the ways in which humans tackle complex tasks and solve problems.

Dom is keen to start work later in the year, he said:

“We’re looking forward to starting on this project in October, it’s a great example of utilising skills founded in Computing Science to aid in a project from a different discipline, particularly one that could make a tangible difference to people’s lives. UEA’s commitment to collaboration, both internally and externally, means Computing Science students here have opportunities to work beyond traditional boundaries and in impactful contexts, which is something that has always appealed to us.”

“Using Distributed Sensing we will be able to gather a wealth of data, allowing us to conduct a thorough investigation and hopefully reveal useful insights into these natural processes. This information can then inform the latter stages of the project, so we can hone in on the impactful causes and discover how they impact the scale and frequency of coastal activity.”

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SOURCE: University of East Anglia