EGU25-13632, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-13632
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Automated seismic detection of surficial mass movements for volcano monitoring: the Stromboli case study
Gaia Zanella, Sergio Gammaldi, Massimo Orazi, Walter De Cesare, Antonietta Esposito, Rosario Peluso, and Dario Delle Donne
Gaia Zanella et al.
  • Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Italy

Gravitational instabilities on active volcanic islands present a significant tsunami hazard, with waves capable of travelling vast distances and impacting far-off coastlines. A notable example is the tsunami triggered by Anak Krakatau's activity in 2018, along with earlier events that affected Montserrat in 1997 and 2003 and Rabaul in 1994. However, monitoring gravitational mass movements in volcanic settings remains challenging due to limited data and the complex dynamics of volcano-landslide interactions. This hampers accurately identifying some landslide key source parameters such as the path location, run-out distances, flow velocity, and mobilized volumes.

Stromboli, an active volcano in the Tyrrhenian Sea, frequently experiences various types of surficial mass movements—such as rockfalls, debris avalanches, and pyroclastic flows—along its Northwest flank, known as the Sciara del Fuoco. These events are closely monitored due to their tsunami-generating potential, as demonstrated during the 2002 eruption when two landslides produced ~2m high waves along the coast. Landslide activity at Stromboli is often linked to volcanic phenomena, such as effusive eruptions and paroxysmal explosions.

Here we used seismic data from the Stromboli monitoring network to investigate patterns of landslide activity along the Sciara del Fuoco and their relationship with the persistent Strombolian activity. The primary objective is to develop near-real-time automatic algorithms aimed at retrieving some landslide key parameters, such as duration, run-out distances, path location, flow velocity, and rate of occurrence. Monitoring these parameters provides valuable insights into ongoing volcanic processes and can help identify early warning signals for potential tsunami triggering.

The study focused on the year 2020, a period marked by varying volcanic activity levels. Automatic landslide detections were validated by manual inspection of seismic record. A total of 457 landslide events, with an average duration of ~200 seconds, were automatically detected and analyzed during the study period. The daily landslide event rate was ranging from 1 to 17 events per day. These findings are vital for improving volcano monitoring at Stromboli volcano as the developed automatic algorithm can be incorporated into the real-time monitoring systems, improving early warnings of volcanic eruptions and tsunamis.

How to cite: Zanella, G., Gammaldi, S., Orazi, M., De Cesare, W., Esposito, A., Peluso, R., and Delle Donne, D.: Automated seismic detection of surficial mass movements for volcano monitoring: the Stromboli case study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13632, https://doi.org/10.5194/egusphere-egu25-13632, 2025.