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

Authors: Moreno, Guillermo Cortés; Carniel, Roberto; Lesage, Philippe; Pérez, María Ángeles Mendoza;

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

Abstract

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: pyVERSO - Command Line Interface (CLI) to create and evaluate VSR models given a labelled DB geoStudio - Graphical User Interface (GUI) acting as a frontend to perform Volcano-Independent VSR (VI.VSR) and volcano-seismic data analysis and visualization 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).

Keywords

Machine Learning, Volcano seismology, Volcano-Seismic Recognition (VSR), Eruption Forecasting, Pattern Recognition, VULCAN.ears, geoStudio, liveVSR, pyVERSO

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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