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Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification
This data manuscript presents a set of signals collected from the Llaima volcano located at the western edge of the Andes in Araucania Region, Chile. The signals were recorded from the LAV station between 2010 and 2016. After individually processing and analyzing every signal, specialists from the O...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206203/ https://www.ncbi.nlm.nih.gov/pubmed/32395588 http://dx.doi.org/10.1016/j.dib.2020.105627 |
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author | Canário, João Paulo de Mello, Rodrigo Fernandes Curilem, Millaray Huenupan, Fernando Rios, Ricardo Araujo |
author_facet | Canário, João Paulo de Mello, Rodrigo Fernandes Curilem, Millaray Huenupan, Fernando Rios, Ricardo Araujo |
author_sort | Canário, João Paulo |
collection | PubMed |
description | This data manuscript presents a set of signals collected from the Llaima volcano located at the western edge of the Andes in Araucania Region, Chile. The signals were recorded from the LAV station between 2010 and 2016. After individually processing and analyzing every signal, specialists from the Observatorio Vulcanológico de los Andes Sur (OVDAS) classified them into four class according to their event source: i) Volcano-Tectonic (VT); ii) Long Period (LP); iii) Tremor (TR), and iv) Tectonic (TC). The dataset is composed of 3592 signals separated by class and filtered to select the segment that contains the most representative part of the seismic event. This dataset is important to support researchers interested in studying seismic signals from active volcanoes and developing new methods to model time-dependent data. In this sense, we have published the manuscript “In-Depth Comparison of Deep Artificial Neural Network Architectures on Seismic Events Classification” [1] analyzing such signals with different Deep Neural Networks (DNN). The main contribution of such manuscript is a new DNN architecture called SeismicNet, which provided classification results among the best in the literature without demanding explicit signal pre-processing steps. Therefore, the reader is referred to such manuscript for the interpretation of the data. |
format | Online Article Text |
id | pubmed-7206203 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-72062032020-05-11 Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification Canário, João Paulo de Mello, Rodrigo Fernandes Curilem, Millaray Huenupan, Fernando Rios, Ricardo Araujo Data Brief Computer Science This data manuscript presents a set of signals collected from the Llaima volcano located at the western edge of the Andes in Araucania Region, Chile. The signals were recorded from the LAV station between 2010 and 2016. After individually processing and analyzing every signal, specialists from the Observatorio Vulcanológico de los Andes Sur (OVDAS) classified them into four class according to their event source: i) Volcano-Tectonic (VT); ii) Long Period (LP); iii) Tremor (TR), and iv) Tectonic (TC). The dataset is composed of 3592 signals separated by class and filtered to select the segment that contains the most representative part of the seismic event. This dataset is important to support researchers interested in studying seismic signals from active volcanoes and developing new methods to model time-dependent data. In this sense, we have published the manuscript “In-Depth Comparison of Deep Artificial Neural Network Architectures on Seismic Events Classification” [1] analyzing such signals with different Deep Neural Networks (DNN). The main contribution of such manuscript is a new DNN architecture called SeismicNet, which provided classification results among the best in the literature without demanding explicit signal pre-processing steps. Therefore, the reader is referred to such manuscript for the interpretation of the data. Elsevier 2020-04-30 /pmc/articles/PMC7206203/ /pubmed/32395588 http://dx.doi.org/10.1016/j.dib.2020.105627 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Computer Science Canário, João Paulo de Mello, Rodrigo Fernandes Curilem, Millaray Huenupan, Fernando Rios, Ricardo Araujo Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification |
title | Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification |
title_full | Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification |
title_fullStr | Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification |
title_full_unstemmed | Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification |
title_short | Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification |
title_sort | llaima volcano dataset: in-depth comparison of deep artificial neural network architectures on seismic events classification |
topic | Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206203/ https://www.ncbi.nlm.nih.gov/pubmed/32395588 http://dx.doi.org/10.1016/j.dib.2020.105627 |
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