Publication Date:
2023-10-26
Description:
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.
Description:
Published
Description:
104531
Description:
8T. Sismologia in tempo reale
Description:
JCR Journal
Keywords:
Volcanoes
;
Software
;
Classification
;
Segmentation
;
Detection
;
04.06. Seismology
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
