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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification

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Author(s):
Canario, Joao Paulo [1] ; de Mello, Rodrigo Fernandes [2] ; Curilem, Millaray [3] ; Huenupan, Ferno [3] ; Rios, Ricardo Araujo [1]
Total Authors: 5
Affiliation:
[1] Univ Fed Bahia, Dept Comp Sci, Av Ademar Barros Ave, BR-40170110 Salvador, BA - Brazil
[2] Univ Sao Paulo, Inst Math & Comp Sci, Sao Paulo - Brazil
[3] Univ La Frontera, Dept Elect Engn, Temuco - Chile
Total Affiliations: 3
Document type: Journal article
Source: DATA IN BRIEF; v. 30, JUN 2020.
Web of Science Citations: 0
Abstract

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 Vulcanologico 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. (C) 2020 The Authors. Published by Elsevier Inc. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC