Seismic location and tracking of snow avalanches and slush flows on Mt. Fuji, Japan

Abstract

Avalanches are often released at the dormant stratovolcano Mt. Fuji, which is the highest mountain of Japan (3776 m a.s.l.). These avalanches exhibit different flow types from dry-snow avalanches in winter to slush flows triggered by heavy rainfall in late winter to early spring. Avalanches from different flanks represent a major natural hazard as they can reach large dimensions with run-out distances up to 4 km, destroy parts of the forest, and sometimes damage infrastructure. To monitor the volcanic activity of Mt. Fuji, a permanent and dense seismic network is installed around the volcano. The small distance between the seismic sensors and the volcano flank ( < 10 km) allowed us to detect numerous avalanche events from the seismic recordings and locate them in time and space. We present the detailed analysis of three avalanche or slush flow periods in the winters of 2014, 2016, and 2018. The largest events (size class 4-5) are detected by the seismic network at maximum distances of about 15 km, and medium-size events (size class 3-4) within a radius of 9 km. To localize the seismic events, we used the automated approach of amplitude source location (ASL) based on the decay of the seismic amplitudes with distance from the moving flow. The recorded amplitudes at each station have to be corrected by the site amplification factors, which are estimated by the coda method using data from local earthquakes. Our results show the feasibility of tracking the flow path of avalanches and slush flows with considerable precision (on the order of magnitude of 100 m) and thus estimating information such as the approximate run-out distance and the average front speed of the flows, which are usually poorly known. To estimate the precision of the seismic tracking, we analyzed aerial photos of the release area and determined the flow path and run-out distance, estimated the release volume from the meteorological records, and conducted numerical simulations with Titan2D to reconstruct the dynamics of the flow. The precision as a function of time is deduced from the comparison with the numerical simulations, showing mean location errors ranging between 85 and 271 m. The average front speeds estimated seismically, which ranged from 27 to 51 m s −1 , are consistent with the numerically predicted speeds. In addition, we deduced two scaling relationships based on seismic parameters to quantify the size of the mass flow events. Our results are indispensable for assessing avalanche risk in the Mt. Fuji region as seismic records are often the only available dataset for this natural hazard. The approach presented here could be applied in the development of an early-detection and location system for avalanches based on seismic sensors.Published by Copernicus Publications on behalf of the European Geosciences Union. 990 C. Pérez-Guillén et al.: Seismic avalanche tracking on Mt. Fuji

Application of an Inertia Dependent Flow Friction Model to Snow Avalanches: Exploration of the Model Using a Ping-Pong Ball Experiment

“…In Titan2D, the Savage-Hutter model was extended to be used with an arbitrary 3D-terrain model. Titan2D is widely used for simulating debris flows, volcanic pyroclastic flows, and snow avalanches [15][16][17][18].…”

“…Furthermore, it is essential to validate this model with observed data in order to apply this model to the realistic snow avalanches. Avalanche velocity was estimated based on the seismic signals (e.g., [18,40,41]), and the Doppler radar measurements [42][43][44]. We can use these observational results for the validation of the model.…”

Locating Rockfalls Using Inter‐Station Ratios of Seismic Energy at Dolomieu Crater, Piton de la Fournaise Volcano

“…This might suggest that recorded signals are dominated by a localized high-energy radiating source area, which we will further discuss in Section 5.4. Using a similar approach based on analyzing the seismic signals in a sliding time window, Pérez-Guillén et al (2019) are also able to track the distributed and moving seismic sources generated by snow avalanches and slush flows.…”

Seismic constraints on rock damaging related to a failing mountain peak: the Hochvogel, Allgäu

“…In general, seismic networks can i) detect mass wasting activity, ii) locate and track the process in space, and iii) infer kinetic and anatomic details of a process event. The application fields of environmental seismology (Burtin et al ., 2014; Larose et al ., 2015) comprise, for example, time‐resolved investigation of the evolution of slope instabilities (e.g., Mainsant et al ., 2012; Schöpa et al ., 2018), detection and quantification of event activity from the catchment to the global scale (e.g., Dammeier et al ., 2011; Ekström and Stark, 2013; Fuchs et al ., 2018; Lacroix and Helmstetter, 2011), tracking of mass movements in space (e.g., Burtin et al ., 2016; Cook et al ., 2018; Pérez‐Guillén et al ., 2019; Walter et al ., 2020), inversion of seismic signals for event kinetics (e.g., Allstadt, 2013; Ekström and Stark, 2013), and attribution of events to drivers and triggers (e.g., Burtin et al ., 2013; Dietze et al ., 2017; Helmstetter and Garambois, 2010).…”