Abstract
Abstract. We evaluate the performance of the low-cost seismic sensor Raspberry Shake to identify and monitor rockfall activity in alpine environments. The test area is a slope adjacent to the Great Aletsch Glacier in the Swiss Alps, i.e. the Moosfluh deep-seated instability, which has recently undergone a critical acceleration phase. A local seismic network composed of three Raspberry Shake was deployed starting from May 2017 in order to record rockfall activity and its relation with the progressive rock-slope degradation potentially leading to a large rock-slope failure. Here we present a first assessment of the seismic data acquired from our network after a monitoring period of 1 year. We show that our network performed well during the whole duration of the experiment, including the winter period in severe alpine conditions, and that the seismic data acquired allowed us to clearly discriminate between rockfalls and other events. This work also provides general information on the potential use of such low-cost sensors in environmental seismology.
Highlights
Rockfalls constitute a major hazard in most steep natural rock slopes
We show the results of a 1-year pilot test performed in the Swiss Alps, deploying a network of three Raspberry Shake seismometers to monitor rock-slope failure events associated with a large, deep-seated slope instability
In this work we show the performance of a network of three Raspberry Shake (RS) during a 1-year pilot project aimed at testing such low-cost seismic sensors to study rockfall activity in alpine environments
Summary
Rockfalls constitute a major hazard in most steep natural rock slopes. The growing number of residential buildings and transport infrastructure in mountain areas has progressively increased the exposure to such processes, making the development of reliable detection systems crucial for early warning and rapid response (Stähli et al, 2015). Usual approaches to build catalogues are based on chronicles and observations of past events; catalogues may lack completeness, as the information is often qualitative and constrained to limited time windows and/or specific locations This is especially true for smallto medium-size rockfall events (Paranunzio et al, 2016). Lowprocurement, as well as limited installation and maintenance efforts, is envisaged in case of the deployment of seismic networks including tens (or even hundreds) of sensors In this scenario, a recently developed low-cost seismic sensor, i.e. the Raspberry Shake (RS) seismometers, provides an interesting low-cost plug-and-play solution. We show the results of a 1-year pilot test performed in the Swiss Alps, deploying a network of three Raspberry Shake seismometers to monitor rock-slope failure events associated with a large, deep-seated slope instability. We provide a short technical description of the sensor, introduce the study area selected, and provide details on the performance of the Raspberry Shake
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