Please use this identifier to cite or link to this item: 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 Classification04.06. 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.
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