Improving the Classification of Volcanic Seismic Events Extracting New Seismic and Speech Features
This paper presents a study on features extracted from the seismic and speech domains that were used to classify four groups of seismic events of the Llaima volcano, located in the Araucanía Region of Chile. 63 features were extracted from 769 events that were labeled and segmented by experts. A feature selection process based on a genetic algorithm was implemented to select the best descriptors for the classifying structure formed by one SVM for each class. The process identified a few features for each class, and a performance that overcame the results of previous similar works, reaching over that 95% of exactitude and showing the importance of the feature selection process to improve classification. These are the newest results obtained from a technology transfer project in which advanced signal processing tools are being applied, in collaboration with the Southern Andes Volcano Observatory (OVDAS), to develop a support system for the monitoring of the Llaima volcano.
KeywordsVolcanic seismicity Pattern recognition SVM Feature selection
We would like to thank FONDEF as this study is being supported by the project FONDEF IDeA IT15I10027.
- 2.Lahr, J.C., Chouet, B.A., Stephens, C.D., Power, J.A., Page, R.A.: Earthquake classification, location and error analysis in a volcanic environment: implications for the magmatic system of the 1989–1990 eruptions at Redoubt Volcano, Alaska. J. Volcanol. Geoth. Res. 62(1–4), 137–151 (1994)CrossRefGoogle Scholar
- 3.Curilem, M., Vergara, J., San Martin, C., Fuentealba, G., Cardona, C., Huenupan, F., Chacón, M., Khan, S., Hussein, W., Becerra, N.: Pattern recognition applied to seismic signals of the Llaima volcano (Chile): an analysis of the events’ features. J. Volcanol. Geoth. Res. 282, 134–177 (2014)CrossRefGoogle Scholar
- 12.San-Martin, C., Melgarejo, C., Gallegos, C., Soto, G., Curilem, M., Fuentealba, G.: Feature extraction using circular statistics applied to volcano monitoring. In: Bloch, I., Cesar, Roberto M. (eds.) CIARP 2010. LNCS, vol. 6419, pp. 458–466. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16687-7_61 CrossRefGoogle Scholar
- 13.Joevivek, V., Chandrasekar, N., Srinivas, Y.: Improving seismic monitoring system for small to intermediate earthquake detection. Int. J. Comput. Sci. Secur. 4(3), 308–315 (2010)Google Scholar
- 17.Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. The MIT Press, Cambridge (1992). 228 p.Google Scholar