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Support vector machines applied to fast determination of the geographical coordinates of earthquakes, The case of El Rosal seismological station, Bogotá Colombia
Máquinas de vectores de soporte aplicadas a la determinación rápida de coordenadas geográficas de terremotos Caso de la estación sismológica El Rosal, Bogotá Colombia
DOI:
https://doi.org/10.15446/dyna.v86n209.75444Palabras clave:
earthquake early warning, rapid response, earthquake geographic coordinates, latitude, longitude, seismic event, Bogotá Colombia, support vector machine (SVM), seismology (en)alerta temprana de terremoto, respuesta rápida, coordenadas geográficas, latitud, longitud, evento sísmico, Bogotá Colombia, máquina de vector de soporte (MVS), sismología (es)
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Abstract
The objective of this research was to determine seismic events latitude and longitude using support vector machines (SVMs) and seismic records from “El Rosal” station, which is located 40 kilometers northwest of Bogotá, Colombia. A total of 504 SVMs models were tested to determine latitude and 504 models for longitude, with various combinations of complexity factor and kernel function exponent, applied to earthquakes of 2, 2.5, 3 and 3.5 ML in time windows of 15 , 10 and 5 seconds. The best results showed errors of 40 kilometers for latitude and 30 kilometers for longitude, with respect to the place where the earthquakes were generated. These outcomes might be improved by applying additional descriptors during SVMs training stages, such descriptors can be related to Fourier frequency spectra, predominant period and wavelet transform coefficients.
Resumen
El objetivo de esta investigación fue determinar la latitud y la longitud de eventos sísmicos utilizando algoritmos de máquinas de vectores de soporte (MVS) y registros sísmicos de la estación "El Rosal", ubicada a 40 kilómetros al noroeste de Bogotá. Un total de 504 modelos de MVS fueron probados para determinar la latitud y 504 modelos para la longitud, con varias combinaciones de factor de complejidad y exponente de la función kernel, aplicados a terremotos de 2, 2.5, 3 y 3.5 ML en ventanas de tiempo de 15, 10 y 5 segundos. Los mejores resultados mostraron errores de 40 kilómetros para la latitud y 30 kilómetros para la longitud, con respecto al lugar donde se generaron los terremotos. Estos resultados podrían mejorarse mediante la aplicación de descriptores adicionales durante las etapas de entrenamiento de las MVS, dichos descriptores pueden estar relacionados con los espectros de frecuencia de Fourier, el período predominante y los coeficientes de transformada de la ondícula.
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1. Michail Nikolaevich Brykov, Ivan Petryshynets, Catalin Iulian Pruncu, Vasily Georgievich Efremenko, Danil Yurievich Pimenov, Khaled Giasin, Serhii Anatolievich Sylenko, Szymon Wojciechowski. (2020). Machine Learning Modelling and Feature Engineering in Seismology Experiment. Sensors, 20(15), p.4228. https://doi.org/10.3390/s20154228.
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