Published December 29, 2019 | Version v.2019.12.29
Software Open

pyVERSO - software for building and evaluating Volcano-Seismic Recognition (VSR) system, part of "VULCAN.ears" project - framework

  • 1. Università degli Studi di Udine
  • 2. Université Savoie - Mont Blanc
  • 3. Universidad de Granada

Description

Volcano-seismic recognition - VSR software supporting the VULCAN.ears EU-funded project via H2020-MSCA-IF-2016 Grant

This framework has been developed using Python.3 scientific libraries and wxPython.4 graphical widgets, being composed of:

  1. pyVERSO  - Command Line Interface (CLI) to create and evaluate VSR models given a labelled DB
  2. geoStudio - Graphical User Interface (GUI) acting as a frontend to perform Volcano-Independent VSR (VI.VSR) and volcano-seismic data analysis and visualization
  3. liveVSR      -  tool to remotely  perform a VSR monitoring in real-time of any volcano accessible via FDSN servers

A working snapshot of pyVERSO scripts  is freely distributed in this package, in order to build own VSR-models of volcano-seismic classes given a labelled database of a given volcano. Once they're built, can be used to monitor this volcano in real-time and continuous operation using the liveVSR tool or be exported to geoStudio to perform an offline analysis of non-labelled data. This software includes also a VSR - workpath as an example, prepared to build models from Deception Volcano.

NOTE! that in order to perform VSR tasks, it is mandatory to have a working installation of the HTK tools in your system.You can freely download your copy (after registration) at the HTK web.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).

Files

pyVERSO__VSR.workpath.zip

Files (128.6 MB)

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Additional details

Related works

Is supplemented by
Project milestone: 10.5281/zenodo.3594080 (DOI)
Journal article: 10.5281/zenodo.3594863 (DOI)

Funding

VULCAN.ears – Volcano-seismic Unsupervised Labelling and ClAssificatioN Embedded in A Real-time Scenario 749249
European Commission