AWESAM - Adaptive-Window Volcanic Event Selection Analysis Module

Module for creating seismo-volcanic event catalogs from seismic data for volcanoes with frequent activity.

See https://doi.org/10.3389/feart.2022.809037 for a detailed description. The process consists of three steps:

1. EventDetection: Identification of potential volcanic events based on squared ground-velocity amplitudes, an adaptive MaxFilter, and a prominence threshold.
2. CatalogConsolidation: By comparing and verifying the initial detections based on recordings from two different seismic stations.
3. EarthquakeClassification: Identification of signals from regional tectonic earthquakes (based on an earthquake catalog)

Tutorial

• Tutorial Notebook AwesamTutorial.ipynb
• Tutorial Video AWESAM Tutorial (Youtube)

Dependencies

When using awesamlib (after compilation with e.g. gcc): numpy 1.21.5, obspy 1.3.0, scipy 1.8.0, pandas 1.4.1, torch 1.11.0. When using the python-backend, additionally numba 0.55.1 is needed. Developed with python 3.8.10.