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Fast estimation of earthquake arrival azimuth using a single seismological station and machine learning techniques
Estimación rápida del azimut de llegada de un terremoto utilizando registros de una sola estación sismológica y técnicas de aprendizaje de máquinas
DOI:
https://doi.org/10.15446/esrj.v23n2.70581Palabras clave:
Earthquake early warning, rapid response, earthquake arrival azimuth, seismic event, Bogota – Colombia, support vector machine (SVM), seismology, earthquakes (en)Alerta temprana de terremoto, respuesta rápida, azimuth de llegada, evento sísmico, Bogotá - Colombia, máquina de soporte vectorial (MSV), sismología, terremotos. (es)
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The objective of this research is to apply a new approach to estimate arrival azimuth of seismic events using seismological records of the “El Rosal” station, near to the city of Bogota – Colombia, by applying support vector machines (SVMs). The algorithm was trained with time signal descriptors of 863 seismic events acquired from January 1998 to October 2008; considering only events with magnitude ≥ 2 ML. The earthquake signals were filtered in order to remove diverse kind of low and high frequency noise not related to such events. During training stages of SVMs, several combinations of kernel function exponent and complexity factor were applied to time signals of 5, 10 and 15 seconds along with earthquake magnitudes of 2.0, 2.5, 3.0 and 3.5 ML. The best classification of SVMs was obtained using time signals of 5 seconds and earthquake magnitudes greater than 3.0 ML with kernel exponent of 10 and complexity factor of 2, showing accuracy of 45.4 degrees. This research is an improvement of previous works related to earthquake arrival azimuth determination from data of one single seismic station employing machine learning techniques.
El propósito de esta investigación es aplicar un nuevo enfoque para estimar el azimut de llegada de eventos sísmicos utilizando registros sismológicos de la estación El Rosal, cercana a la ciudad de Bogotá – Colombia, mediante la aplicación de máquinas de vectores de soporte (MVS). El algoritmo fue entrenado con descriptores de señales de tiempo de 863 eventos sísmicos adquiridos desde Enero 1998 hasta Octubre de 2008; considerando solamente eventos con magnitudes ≥ 2 ML. Las señales de los terremotos fueron filtradas para remover diversos tipos de ruidos de alta y baja frecuencia no relacionados con dichos eventos. Durante las etapas de entrenamiento de la MVS fueron aplicadas varias combinaciones del exponente de la función kernel y factor de complejidad, a señales de tiempo de 5, 10 y 15 segundos junto con terremotos de magnitudes mayores a 2.0, 2.5, 3.0 y 3.5 ML. La mejor clasificación de la MVS fue obtenida utilizando señales de tiempo de 5 segundos y terremotos de magnitud mayor a 3.0 ML con exponente kernel de 10 y factor de complejidad de 2, mostrando precisión de 45.4 grados. Esta investigación es una mejora a trabajos previos relacionados con determinación del azimut de llegada de un terremoto a partir de datos de una única estación sismológica empleando técnicas de aprendizaje de máquinas.
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