S021-0014
Deployment of a long-term seismic network on a moisture-induced, slow moving, clay-rich landslide

Wednesday, 9 December 2020
Poster
Arnaud Watlet1, Jim Whiteley2,3, Victoria Lane4, Ben Dashwood5, Dave Morgan5, Jonathan Edward Chambers5 and Thomas Lecocq6, (1)British Geological Survey Keyworth, Nottinghamshire, NG12, United Kingdom, (2)University of Bristol, School of Earth Sciences, Bristol, United Kingdom, (3)British Geological Survey, Nottingham, United Kingdom, (4)University of Leicester, Leicester, LE1, United Kingdom, (5)British Geological Survey Keyworth, Nottinghamshire, United Kingdom, (6)Royal Observatory of Belgium, Seismology-Gravimetry, Brussels, Belgium
Abstract:
Landslides pose a risk to human safety and socioeconomic activities across the world. A complete spatiotemporal, geoscientific understanding of the evolving subsurface processes acting to destabilise vulnerable slopes is critical for mitigating and managing landslide hazards and providing early-warning of impending failure. Seismological monitoring applied to landslides can provide high temporal resolution information on the destabilization of a slope. The detection and characterization of local seismic events provide evidence of landslide activity, and are increasingly applied to unstable slopes. Changes in seismic velocity identified via cross-correlation of seismic noise can also provide crucial information on the precursory conditions of slope failure.

This study aims to assess the feasibility of, and identify challenges related to, the seismological monitoring of a slow-moving clay-rich landslide. We present the deployment of a passive seismic network composed of 7 broadband seismometers (Güralp 6TD) at the Hollin Hill Landslide Observatory, North Yorkshire, UK. The Hollin Hill landslide is a slow moving, clay-rich landslide, which is seasonally reactivated when soil moisture increases in winter, and experiences high shrinkage and surface fissuring in summer. Seismic stations were dispatched over a ~4 ha hill slope and installed in different zones of the landslide. Preliminary results from a first 6-month dataset cover tilt analyses, early phases of detection and classification of seismic signals attributed to landslide activity, and seismic noise analyses. These results are integrated with data from a co-located hydrogeological and geotechnical monitoring network comprising piezometers, and sensors measuring soil moisture, suction and displacement.