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http://hdl.handle.net/2122/14325
Authors: | Bueno, Angel* Zuccarello, Luciano* Díaz-Moreno, Alejandro* Woollam, Jack* Titos, Manuel* Benítez, Carmen* Álvarez, Isaac* Prudencio, Janire* De Angelis, Silvio* |
Title: | PICOSS: Python Interface for the Classification of Seismic Signals | Journal: | Computers & Geosciences | Series/Report no.: | /142 (2020) | Publisher: | Elsevier | Issue Date: | 2020 | DOI: | 10.1016/j.cageo.2020.104531 | Keywords: | Volcanoes Software Classification Segmentation Detection |
Subject Classification: | 04.06. Seismology | Abstract: | Over the last decade machine learning has become increasingly popular for the analysis and characterization of volcano-seismic data. One of the requirements for the application of machine learning methods to the problem of classifying seismic time series is the availability of a training dataset; that is a suite of reference signals, with known classification used for initial validation of the machine outcome. Here, we present PICOSS (Python Interface for the Classification of Seismic Signals), a modular data-curator platform for volcano-seismic data analysis, including detection, segmentation and classification. PICOSS has exportability and standardization at its core; users can select automatic or manual workflows to select and label seismic data from a comprehensive suite of tools, including deep neural networks. The modular implementation of PICOSS includes a portable and intuitive graphical user interface to facilitate essential data labelling tasks for large-scale volcano seismic studies. |
Appears in Collections: | Article published / in press |
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