Abstract
This study describes the on-line operation of a seismic detection system to act at the level of a seismic station providing similar role to that of a STA / LTA ratio- based detection algorithms. The intelligent detector is a Support Vector Machine (SVM), trained with data consisting of 2903 patterns extracted from records of the PVAQ station, one of the seismographic network’s stations of the Institute of Meteorology of Portugal (IM). Records’ spectral variations in time and characteristics were reflected in the SVM input patterns, as a set of values of power spectral density at selected frequencies. To ensure that all patterns of the sample data were within the range of variation of the training set, we used an algorithm to separate the universe of data by hyper-convex polyhedrons, determining in this manner a set of patterns that have a mandatory part of the training set. Additionally, an active learning strategy was conducted, by iteratively incorporating poorly classified cases in the training set. After having been trained, the proposed system was experimented in continuous operation for unseen (out of sample) data, and the SVM detector obtained 97.7% and 98.7% of sensitivity and selectivity, respectively. The same type of ANN presented 88.4 % and 99.4% of sensitivity and selectivity when applied to data of a different seismic station of IM.
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References
Adeli, H., Panakkat, A.: A probabilistic neural network for earthquake magnitude prediction. Neural Networks 22, 1018–1024 (2009)
Suratgar, A., Setoudeh, F., Salemi, A.H., Negarestani, A.: Magnitude of Earthquake Prediction Using Neural Network. In: Fourth International Conference on Natural Computation, ICNC 2008, Los Alamitos, vol. 2, pp. 448–452 (2008)
Andreadis, I., Tsiftzis, I., Elenas, A.: Intelligent seismic acceleration signal processing for damage classification in buildings. IEEE Transactions on Instrumentation and Measurement 56, 1555–1564 (2007)
Furuta, H., Frangopol, D.M., Nakatsu, K.: Life-cycle cost of civil infrastructure with emphasis on balancing structural performance and seismic risk of road network. Structure and Infrastructure Engineering 7, 65–74 (2011)
Orlic, N., Loncaric, S.: Earthquake-explosion discrimination using genetic algorithm-based boosting approach. Computers & Geosciences 36, 179–185 (2010)
Scarpetta, S., et al.: Automatic classification of seismic signals at Mt. Vesuvius volcano, Italy, using neural networks. Bulletin of the Seismological Society of America 95, 185–196 (2005)
Gentili, S., Michelini, A.: Automatic picking of P and S phases using a neural tree. Journal of Seismology 10, 39–63 (2006)
Lancieri, M., Zollo, A.: A Bayesian approach to the real-time estimation of magnitude from the early P and S wave displacement peaks. Journal of Geophysical Research-Solid Earth 113, 17 (2008)
Tasic, I., Runovc, F.: Automatic S-phase arrival identification for local earthquakes. Acta Geotechnica Slovenica 6, 46–55 (2009)
Valet, L., Mauris, G., Bolon, P., Keskes, N.: A fuzzy linguistic-based software tool for seismic image interpretation. IEEE Transactions on Instrumentation and Measurement 52, 675–680 (2003)
Sharma, B.K., Kumar, A., Murthy, V.M.: Evaluation of seismic events detection algorithms. Journal of the Geological Society of India 75, 533–538 (2010)
Dai, H.C., MacBeth, C.: The application of back-propagation neural network to automatic picking seismic arrivals from single-component recordings. Journal of Geophysical Research-Solid Earth 102, 15105–15113 (1997)
Wang, J., Teng, T.L.: Artificial neural-network-based seismic detector. Bulletin of the Seismological Society of America 85, 308–319 (1995)
Madureira, G., Ruano, A.E.: A Neural Network Seismic Detector. In: 2nd IFAC International Conference on Intelligent Control Systems and Signal Processing (ICONS 2009), Istambul, Turkey (2009)
Frieß, T., Cristianini, N., Campbel, C.: The Kernel Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines. In: 15th Intl. Conf. Machine Learning. Morgan Kaufmann Publishers (1998)
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Madureira, G., Ruano, A.E., Ruano, M.G. (2013). On-Line Operation of an Intelligent Seismic Detector. In: Balas, V., Fodor, J., Várkonyi-Kóczy, A., Dombi, J., Jain, L. (eds) Soft Computing Applications. Advances in Intelligent Systems and Computing, vol 195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33941-7_47
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DOI: https://doi.org/10.1007/978-3-642-33941-7_47
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