Exploring coastal cliff instability with distributed fibre optic sensors

One of the significant environmental challenges the UK faces now is coastal erosion and cliff instability. Along its east and south coast, the soft rock cliffs suffer rapid erosion that causes landward retreat of the coast. The erosion rate was estimated to be at a sub-meter level along the Norfolk coast, which has retreated about 1 to 2 km over the past 900 years. The fast and active coastal erosion casts shadows on the coastal community's livelihood and endangers critical infrastructure.

Coastal erosion is a natural process that is affected by a series of factors, such as the strength of the rocks making up the coast, weather (rainfall, storms etc) and its seasonal variations, as well as wave action. Human factors also play critical roles in shaping the erosion process. Man-made structures, such as coastal defences, may help slow down the eroding rate. According to the National Tidal and Sea Level Facility, the mean sea level rose 1.5 meters from 1900 to 2010. The changing sea level, arguably associated with global warming, adds further complexities to understanding the main factors controlling the current coastal erosion rate.

Active cliff erosion involves three stages of repeating processes: basal undercutting, cliff failure (landslides or slump), and deposition of falling debris. This project focuses on understanding the processes leading up to cliff failure and the underlying factors that affect the timing and size of failure. By monitoring ground deformation and understanding how it changes with weather, geology and wave action, we may identify the informative signal leading up to cliff failure. Such information would be valuable for the early identification of developing cliff failure risk.

We will use the state-of-the-art distributed fibre optic sensors (DFOS) to monitor tiny ground movements at an unprecedented resolution. The DFOS take advantage of imperfections in optical fibre and convert it into an array of distributed sensors that can detect...