Automated Seismo-Volcanic Event Detection Applied to Stromboli (Italy)

Abstract

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: 1) identification of potential volcanic events based on squared ground-velocity amplitudes, an adaptive MaxFilter, and a prominence threshold. 2) catalog consolidation by comparing and verifying the initial detections based on recordings from two different seismic stations. 3) 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.

Earthquake monitoring using deep learning with a case study of the Kahramanmaras Turkey earthquake aftershock sequence

“…Thus, the use of deep learning has been widely embraced in first-motion polarity identification of earthquake waveforms (Chakraborty et al, 2022a), seismic event detection (Perol et al, 2018;Mousavi et al, 2019b;Fenner et al, 2022;Li et al, 2022b), earthquake magnitude classification and estimation (Chakraborty et al, 2021(Chakraborty et al, , 2022b, and seismic phase picking (Ross et al, 2018;Zhu and Beroza, 2019;Mousavi et al, 2020;Li et al, 2021aLi et al, , 2022a. Stepnov et al (2021) stated that seismic phase picking approaches can be roughly divided into two main streams: continuous seismic waveform-based and small window-format-based methods.…”

Comparison between Automated and Manual Detection of Lava Fountains from Fixed Monitoring Thermal Cameras at Etna Volcano, Italy

“…However, in order to assess the impact on the population, the amount of solely the pyroclastics component erupted during a lava fountain event needs to be established, because this affects the stability of roofs, the cleaning up of roads and motorways, the impact on the nearby Catania international airport, and the health effects on the local population [49,50]. Automated routines for volcanic activity detection and characterization have been recently developed [23,45,51,52], and they will probably resolve most of the issues related with early warning alarms. In this paper, we presented a new automated routine that, when applied to the images recorded by the thermal monitoring cameras, allowed us to calculate (1) lava fountain height, (2) area of the lava fountain jet, and (3) volume of the erupted tephra, using the formula applied by Calvari et al [3,8].…”

EPick: Attention-based multi-scale UNet for earthquake detection and seismic phase picking

“…Unlike traditional automated methods, where only a limited set of defined features of seismograms is used, deep learning facilitates more abundant feature extraction from seismic data. Recent years have witnessed remarkable achievements in the application of deep learning in seismic data processing tasks, especially for seismic event detection and seismic phase picking (Pardo et al, 2019;Wang et al, 2019;Zhou et al, 2019;Zhu and Beroza, 2019;Mousavi et al, 2020;Chakraborty et al, 2021;Li et al, 2021;Chakraborty et al, 2022;Fenner et al, 2022;Li et al, 2022). For instance, EQTtransformer (Mousavi et al (2020)) had a multi-task structure consisting of one very-deep encoder and three separate decoders for simultaneous detection of earthquake signals and picking the first P and S phases, where the Gaussian form label is used.…”