5:15 PM - 7:15 PM
[MTT36-P02] Sesmic signals induced by snow avalanche flow
Keywords:snow avalanche, Amplitude Source Location (ASL) method
Seismic signals from snow avalanches have been studied for a long time. Measurements taken during avalanche events reveal a distinct spindle-shaped pattern, significantly different from those produced by natural earthquakes. The overall signal characteristics strongly depend on the topography of the avalanche path, allowing for the estimation of avalanche velocity. Additionally, the running power spectrum shows a meaningful shift in the dominant frequency from the front to the rear of the flow. In fact, seismic detection systems have been widely used for analanche monitoring overseas. Deep learning-basd seismic waveform classifier which automatically detect snow avalanche is also developed. Further, applying the Distributed Acoustic Sensing (DAS), the investigation of the spatio-temporal avalanche evolution from release to stop is now on progress.
On the other hand, avalanches from Mt. Fuji can reach run-out distances of up to 3 km, posing a significant natural hazard. These avalanches exhibit a range of flow types, from typical dry-snow avalanches in winter to slush flows triggered by heavy rainfall in spring. To monitor volcanic activity, a dense, permanent seismic network is installed around Mt. Fuji. In this study, seismic recordings from this network were used to identify avalanche events and determine their timing and location. For the first time, the Amplitude Source Location (ASL) method was applied to avalanche detection. Several avalanche events were seismically identified in each of the three analyzed winter periods (2014, 2016, and 2018). Large avalanches (size class 4–5) were detected by seismic sensors at distances of up to 15 km, while medium-sized events were recorded within a 9 km radius. By analyzing data from multiple seismic stations, we were able to localize and track these avalanches, allowing for the estimation of their run-out distances and velocities. To assess the accuracy of seismic tracking, we simulated avalanches using the Titan2D dynamical model, incorporating aerial photographs and meteorological records to estimate release areas and volumes.
On the other hand, avalanches from Mt. Fuji can reach run-out distances of up to 3 km, posing a significant natural hazard. These avalanches exhibit a range of flow types, from typical dry-snow avalanches in winter to slush flows triggered by heavy rainfall in spring. To monitor volcanic activity, a dense, permanent seismic network is installed around Mt. Fuji. In this study, seismic recordings from this network were used to identify avalanche events and determine their timing and location. For the first time, the Amplitude Source Location (ASL) method was applied to avalanche detection. Several avalanche events were seismically identified in each of the three analyzed winter periods (2014, 2016, and 2018). Large avalanches (size class 4–5) were detected by seismic sensors at distances of up to 15 km, while medium-sized events were recorded within a 9 km radius. By analyzing data from multiple seismic stations, we were able to localize and track these avalanches, allowing for the estimation of their run-out distances and velocities. To assess the accuracy of seismic tracking, we simulated avalanches using the Titan2D dynamical model, incorporating aerial photographs and meteorological records to estimate release areas and volumes.