Automated Seismo-Volcanic Event Detection Applied to Stromboli (Italy) - Fenner, Darius; Rümpker, Georg; Li, Wei; Chakraborty, Megha; Faber, Johannes; Köhler, Jonas; Stöcker, Horst; Srivastava, Nishtha (Frontiers in Earth Science, 2022)
Many active volcanoes exhibit Strombolian activity, which is typically characterized by relatively frequent mild volcanic explosions and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major and minor events can help to understand the eruptive behavior of volcanoes and the underlying physical and chemical processes. Catalogs of these eruptions and, specifically, seismo-volcanic events may be generated using continuous seismic recordings at stations in the proximity of volcanoes. However, in many cases, the analysis of the recordings relies heavily on the manual picking of events by human experts. Recently developed Machine Learning-based approaches require large training data sets which may not be available a priori. Here, we propose an alternative user-friendly, time-saving, automated approach labelled as: the Adaptive-Window Volcanic Event Selection Analysis Module (AWESAM). This strategy of creating seismo-volcanic event catalogs consists of three main steps: (i) identification of potential volcanic events based on squared ground-velocity amplitudes, an adaptive MaxFilter, and a prominence threshold. (ii) catalog consolidation by comparing and verifying the initial detections based on recordings from two different seismic stations. (iii) identification and exclusion of signals from regional tectonic earthquakes. The strength of the python package is the reliable detection of very small and frequent events as well as major explosions and paroxysms. Here, it is applied to publicly accessible continuous seismic recordings from two almost equidistant stations at Stromboli volcano in Italy. We tested AWESAM by comparison with a hand-picked catalog and found that around 95 % of the seismo-volcanic events with a signal-to-noise ratio above three are detected. In a first application, we derive a new amplitude-frequency relationship from over 290.000 seismo-volcanic events at Stromboli during 2019-2020 which were detected by AWESAM. The module allows for a straightforward generalization and application to other volcanoes with frequent Strombolian activity worldwide. Furthermore, this module can be implemented for volcanoes with rarer explosions.