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Discrimination of seismic waves produced by volcanoes using self-organizing maps

Abstract:

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 pbkp_rediction 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 pbkp_rediction 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 pbkp_rediction 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.