Quantifying temporal and spatial causalities between climate change and slope failures
Lead Research Organisation:
University of Strathclyde
Department Name: Electronic and Electrical Engineering
Abstract
The goal of this high-risk research project is to develop data-driven methodological tools to identify influencing or triggering factors and indicator signals that characterise the early stages of slope instabilities, which escalate to landslide displacements, debris flow and rockfalls. These geological hazards negatively impact the economy and public, through disruption, damage of infrastructure and even loss of life. The intended project outcome is feasibility of timely landslide displacement prediction from particular continuous monitoring data streams, providing the basis for landslide early warning systems.
Conventional approaches to slope monitoring rely heavily on surface observations (aerial, UAV and satellite image and GPS data). There is a large volume of work on detecting landslides once they have happened and there are early attempts at identifying locations prone to landslides, i.e., susceptibility assessments, from multi-scale spatial information from field surveys and aerial/satellite data at the catchment-to-regional-scale. However, timely prediction of imminent landslides at the slope-scale remains a challenging problem because precursory signals from subsurface recordings are as yet not fully understood or quantified.
The generation and recording of seismic signals from a detached soft soil mass that is moving downwards a mountain slope has been documented, but the presence of precursory signals for such failures has only been shown in the lab. The presence of precursory signals in the field has been documented for rock failure, i.e., in the shape of the formation and propagation of cracks. We know of no publicly available catalogues/labels of such events for soft soils. We hypothesise that soft soil failure does generate seismic signals that can be recorded by seismometers and identified through advanced signal processing but the evidence to fully support this statement is yet to be found.
To determine to what extent early detection and characterisation of slope instabilities is possible, this project will investigate the precursors to a landslide and the underlying subsurface processes. We will quantify the instrumentation/sensor modalities, density/granularity and geographic area around a hill slope, in conjunction with advanced signal information processing and machine learning (instrumentation and advanced analysis are traditionally treated in isolation), to determine the feasibility of an effective real-time warning system.
This approach will radically transform our very limited understanding of temporal and spatial causalities between precipitation, temperature, and landslide induced seismicity. Current climate modelling (e.g., UKCIP) is predicting wetter winters and higher intensity of rainfall due to climate change, and the Met Office with BGS have demonstrated a marked increase in the number of landslides at times of heavy rainfall. Understanding these causalities will enable the development of new fields of research into data-driven engineering solutions to (i) accurately extract seismic predictor signals from large, noisy and continuous recordings, (ii) make linkages between instrumentation that make surface observations of landslides, measures seismicity at subsurface and geophysical approaches that interrogate the subsurface, (iii) augment climate impact programme (e.g., UKCIP) to include effect on landslides, (iv) predict an impending landslide and its scale. Ultimately, these will enable us mitigate the devastating effect of slope instabilities on humans and the economy.
Conventional approaches to slope monitoring rely heavily on surface observations (aerial, UAV and satellite image and GPS data). There is a large volume of work on detecting landslides once they have happened and there are early attempts at identifying locations prone to landslides, i.e., susceptibility assessments, from multi-scale spatial information from field surveys and aerial/satellite data at the catchment-to-regional-scale. However, timely prediction of imminent landslides at the slope-scale remains a challenging problem because precursory signals from subsurface recordings are as yet not fully understood or quantified.
The generation and recording of seismic signals from a detached soft soil mass that is moving downwards a mountain slope has been documented, but the presence of precursory signals for such failures has only been shown in the lab. The presence of precursory signals in the field has been documented for rock failure, i.e., in the shape of the formation and propagation of cracks. We know of no publicly available catalogues/labels of such events for soft soils. We hypothesise that soft soil failure does generate seismic signals that can be recorded by seismometers and identified through advanced signal processing but the evidence to fully support this statement is yet to be found.
To determine to what extent early detection and characterisation of slope instabilities is possible, this project will investigate the precursors to a landslide and the underlying subsurface processes. We will quantify the instrumentation/sensor modalities, density/granularity and geographic area around a hill slope, in conjunction with advanced signal information processing and machine learning (instrumentation and advanced analysis are traditionally treated in isolation), to determine the feasibility of an effective real-time warning system.
This approach will radically transform our very limited understanding of temporal and spatial causalities between precipitation, temperature, and landslide induced seismicity. Current climate modelling (e.g., UKCIP) is predicting wetter winters and higher intensity of rainfall due to climate change, and the Met Office with BGS have demonstrated a marked increase in the number of landslides at times of heavy rainfall. Understanding these causalities will enable the development of new fields of research into data-driven engineering solutions to (i) accurately extract seismic predictor signals from large, noisy and continuous recordings, (ii) make linkages between instrumentation that make surface observations of landslides, measures seismicity at subsurface and geophysical approaches that interrogate the subsurface, (iii) augment climate impact programme (e.g., UKCIP) to include effect on landslides, (iv) predict an impending landslide and its scale. Ultimately, these will enable us mitigate the devastating effect of slope instabilities on humans and the economy.
