Towards better volcanic risk-assessment systems by applying ensemble classification methods to triaxial seismic-volcanic signals

Analysis of seismic data is the most important method for volcano monitoring. Such data typically consists in digital signals acquired with an arrangement of triaxial seismic sensors which are strategically deployed on the volcano and its surrounding areas. Very rich measurements of the underlying phenomena are obtained from the arrangement because each sensor, through their corresponding three sensing axes, uninterruptedly acquires data at a relatively high sampling rate. Such an uninterrupted acquisition, however, turns manual classification of seismic signals into an inefficient and often error-prone task. As a solution, several systems for automated classi- fication of seismic-volcanic signals have been proposed. All these systems, however, are limited to the usage of only one direction of acquisition; typically the vertical one. In this paper we make a step forward, exploring the potential benefit of using information from the three axes of the signals gathered by a single sensor. Integration is performed by classifier combining techniques, applied at different levels: this permits to take into account in the classification all the three orthogonal orientations \u2014vertical, East-West and North-South\u2014 of the phenomenon. Preliminary experimental results on a set of volcanic signals gathered at Nevado del Ruiz volcano in Colombia confirmed the richness of this information