Open AccessDiscrimination of seismic waves produced by volcanoes using self-organizing maps
Author(s) -
Aaron Salazar,
Elsa Juliana Vega Salazar,
Óscar Cadena,
R Melanny Hernández
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1702/1/012015
Subject(s) - cluster analysis , computer science , artificial neural network , filter (signal processing) , unsupervised learning , data mining , noise (video) , seismology , signal (programming language) , artificial intelligence , function (biology) , pattern recognition (psychology) , geology , image (mathematics) , evolutionary biology , computer vision , biology , programming language
The analysis and classification of seismic records of volcanoes allow us to determine the alert state in which it is. A timely study of these signs can contribute to decision-making to safeguard the integrity of people in the face of a natural disaster. The present work applies a methodology that combines the analysis of linear prediction coefficients and artificial neural networks to classify earthquakes. Two types of earthquakes that come from Galeras Volcano, Colombia, are studied volcano-tectonic and long-period. The classification is made using the clustering technique based on unsupervised learning. The signals are transformed using the linear prediction filter coefficients technique, which has the function of reducing the size of the vector that contains the original data. MATLAB software is used to generate a self-organizing network that handles clustering. The results show that the best alternative in unsupervised learning is to use the linear prediction coefficients of order 5, 6, and 7 to represent a seismic signal. For lower orders, the necessary information is not captured and for higher orders, noise information is shown.