Derechos de autor 2021 Investigación e Innovación en Ingenierías
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
TagEQ-CN: Clasificación de Eventos Sísmicos Basado en Redes Complejas
Corresponding Author(s) : Hugo Ordoñez Erazo
Investigación e Innovación en Ingenierías,
Vol. 9 Núm. 1 (2021): Enero-Junio
Resumen
Objetivo: Realizar detección y clasificación de señales sísmicas continúa siendo un tema de investigación, dada la diversidad de tipos de señales, sensores y fuentes de vibración. Metodología: Para el proceso de clasificación de eventos utiliza información sobre nodos o lugares que aglomeran eventos sísmicos, así como también comunidades de eventos. Resultados: El sistema se testeó con señales provenientes de la estación La Rusia que pertenece a la Red Sismológica Nacional de Colombia, con resultados prometedores. La aplicabilidad de este sistema, permite incluir información nueva para la anotación automática de sismos, así como también reconocer automáticamente eventos de otras fuentes. Conclusiones: El sistema desarrollado se basa en el paradigma supervisado, el usuario no escoge directamente cuál es el conjunto de señales que se utilizan para el entrenamiento y prueba del clasificador.
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Referencias
J. Akram and D. Eaton, “Adaptive microseismic event detection and automatic time picking.”
M. Beyreuther and J. Wassermann, “Continuous earthquake detection and classification using discrete Hidden Markov Models,” Geophys. J. Int., vol. 175, no. 3, pp. 1055–1066, Dec. 2008, doi: https://doi.org/10.1111/j.1365-246X.2008.03921.x.
P. Bui Quang, P. Gaillard, Y. Cano, and M. Ulzibat, “Detection and classification of seismic events with progressive multi-channel correlation and hidden Markov models,” Comput. Geosci., vol. 83, pp. 110–119, Oct. 2015, DOI: https://doi.org/10.1016/j.cageo.2015.07.002.
O. Lindenbaum, N. Rabin, Y. Bregman, and A. Averbuch, “Multi-channel fusion for seismic event detection and classification,” Nov. 2016, pp. 1–5, DOI: https://doi.org/10.1109/ICSEE.2016.7806088.
J. Havskov and L. Ottemöller, Routine data processing in earthquake seismology: with sample data, exercises and software. Dordrecht ; New York: Springer, 2010.
T. Lay and T. C. Wallace, Modern global seismology. San Diego: Academic Press, 1995.
D. Leon, H. Ordoñez, and V. Bucheli, “TagEQ: Sistema de Clasificación de Eventos Sísmicos Basado en Inteligencia Artificial,” RISTI - Rev. Iber. Sist. E Tecnol. Inf., vol. e27, pp. 359–370, 2020.
B. Sick, M. Guggenmos, and M. Joswig, “Chances and limits of single-station seismic event clustering by unsupervised pattern recognition,” Geophys. J. Int., vol. 201, no. 3, pp. 1801–1813, Apr. 2015, DOI: https://doi.org/10.1093/gji/ggv126.
A. Köhler, M. Ohrnberger, and F. Scherbaum, “Unsupervised pattern recognition in continuous seismic wavefield records using Self-Organizing Maps: Unsupervised seismic pattern recognition,” Geophys. J. Int., vol. 182, no. 3, pp. 1619–1630, Sep. 2010, DOI: https://doi.org/10.1111/j.1365-246X.2010.04709.x.
A. E. Ruano, G. Madureira, O. Barros, H. R. Khosravani, M. G. Ruano, and P. M. Ferreira, “Seismic detection using support vector machines,” Neurocomputing, vol. 135, pp. 273–283, Jul. 2014, DOI: https://doi.org/10.1016/j.neucom.2013.12.020.
S. Gentili and P. Bragato, “A neural-tree-based system for automatic location of earthquakes in Northeastern Italy,” J. Seismol., vol. 10, no. 1, pp. 73–89, Jan. 2006, DOI: https://doi.org/10.1007/s10950-005-9001-z.
J. J. McGuire, M. S. Boettcher, and T. H. Jordan, “Foreshock sequences and short-term earthquake predictability on East Pacific Rise transform faults,” Nature, vol. 434, no. 7032, pp. 457–461, Mar. 2005, DOI: https://doi.org/10.1038/nature03377.
L. N. Doda, V. L. Natyaganov, and I. V. Stepanov, “An empirical scheme of short-term earthquake prediction,” Dokl. Earth Sci., vol. 453, no. 2, pp. 1257–1263, Dec. 2013, DOI: https://doi.org/10.1134/S1028334X1312009X.
P. E. Nikolopoulos D, “Radon-222: A Potential Short-Term Earthquake Precursor,” J. Earth Sci. Clim. Change, vol. 06, no. 06, 2015, DOI: https://doi.org/10.4172/2157-7617.1000282.
A. Donnellan et al., “Potential for a large earthquake near Los Angeles inferred from the 2014 La Habra earthquake: POTENTIAL FOR A LARGE EARTHQUAKE NEAR LA,” Earth Space Sci., vol. 2, no. 9, pp. 378–385, Sep. 2015, DOI: https://doi.org/10.1002/2015EA000113.
S. Abe and N. Suzuki, “Law for the distance between successive earthquakes: A NEW LAW FOR DISTANCE BETWEEN EARTHQUAKES,” J. Geophys. Res. Solid Earth, vol. 108, no. B2, Feb. 2003, DOI: https://doi.org/10.1029/2002JB002220.
M. Baiesi and M. Paczuski, “Scale-free networks of earthquakes and aftershocks,” Phys. Rev. E, vol. 69, no. 6, p. 066106, 2004.
D. Pastén, F. Torres, B. Toledo, V. Mu’f1oz, J. Rogan, and J. A. Valdivia, “Time-Based Network Analysis Before and After the $$M_w$$ M w 8.3 Illapel Earthquake 2015 Chile,” Pure Appl. Geophys., vol. 173, no. 7, pp. 2267–2275, Jul. 2016, DOI: https://doi.org/10.1007/s00024-016-1335-7.
D. Chorozoglou, D. Kugiumtzis, and E. Papadimitriou, “Application of complex network theory to the recent foreshock sequences of Methoni (2008) and Kefalonia (2014) in Greece,” Acta Geophys., vol. 65, no. 3, pp. 543–553, Jun. 2017, DOI: https://doi.org/10.1007/s11600-017-0039-4.
S. Abe and N. Suzuki, “Earthquake networks, Complex,” in Extreme Environmental Events, R. A. Meyers, Ed. New York, NY: Springer New York, 2011, pp. 312–319.
D. A. León, J. A. Valdivia, and V. A. Bucheli, “Modeling of Colombian Seismicity as Smalluc0u8208 World Networks,” Seismol. Res. Lett., vol. 89, no. 5, pp. 1807–1816, Sep. 2018, DOI: https://doi.org/10.1785/0220180076.
T. Christiano Silva and L. Zhao, Machine Learning in Complex Networks. Cham: Springer International Publishing, 2016.
M. Joswig, “Knowledge-based seismogram processing by mental images,” IEEE Trans. Syst. Man Cybern., vol. 24, no. 3, pp. 429–439, Mar. 1994, DOI: https://doi.org/10.1109/21.278992.
C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1–27, Apr. 2011, DOI: https://doi.org/10.1145/1961189.1961199.
M. N. Wright and A. Ziegler, “textbfranger : A Fast Implementation of Random Forests for High Dimensional Data in textitC++ and textitR,” J. Stat. Softw., vol. 77, no. 1, 2017, DOI: https://doi.org/10.18637/jss.v077.i01.
D. Henao - león, A. Camilo Báez - Alarcón, y J. Bethsaid Pedroza - Rojas, “Metodología para determinar la viabilidad de generación de energía eléctrica por medio del recurso eólico”, Revista Investigación e Innovación en Ingenierías, vol. 6, n°. 2, 2018. DOI: https://doi.org/10.17081/invinno.6.2.3108
R. Avellaneda, S. Cabrera, P. A. Martínez, y C. G. Donoso Albarracín, "Apoyo tecnológico para la fidelización y captación de nuevos clientes por medio de una aplicación móvil", Investigación e Innovación en Ingenierías, vol. 5, n.º 1, pp. 92-101, 2017. DOI: https://doi.org/10.17081/invinno.5.1.2618
L. Dong, X. Li, and G. Xie, “Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification,” Abstr. Appl. Anal., vol. 2014, pp. 1–8, 2014, DOI: https://doi.org/10.1155/2014/459137.