Citation

Canário JP, de Mello RF, Curilem M, Huenupan F, Rios RA. Data Brief 2020; 30: e105627.

Affiliation

Department of Computer Science, Federal University of Bahia, Brazil.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.dib.2020.105627

PMID

32395588

PMCID

PMC7206203

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 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.

© 2020 The Authors.

Language: en

Keywords

Neural Network; Time Series Analysis; Time Series modelling; Volcano Monitoring