Comparative analysis of automated classifiers applied to volcano event identification

The correct classification of several types of volcanic events can be used to determine the intrinsic behavior of a volcano. This information could be useful to provide an early alarm in the case of imminent volcanic activity. Therefore, finding an efficient algorithm capable of identifying seismic activity can be beneficial for this purpose. In such sense, this work evaluates several machine learning techniques, that have been previously applied to classify seismic events, taking into account quality and performance parameters. In order to test the algorithms, a seismic database from the Cotopaxi volcano in Ecuador was used. This database was collected by the Geophysical Institute at Escuela Politécnica Nacional between January and June of 2010. The analysis was focused in two major types of seismic events: long period and volcano tectonic. For each event, 79 key features in time and frequency domain were extracted. These features were used to train 3 well known classifiers: k-nearest neighbors, decision trees and neural networks. Finally, a feature selection technique was employed to find those features with greater impact improving the classifier performance. Our approach allow us to reach an accuracy of 98% by identifying 3 main features and using the k-NN classifier.