Support vector machines applied to fast determination of the geographical coordinates of earthquakes, The case of El Rosal seismological station, Bogotá Colombia

Abstract

The objective of this research was to determine the latitude and longitude of seismic events 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 SVM models were tested to determine latitude and 504 models for longitude, with various combinations of the 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 when identifying where tremors occurred. These outcomes might be improved by applying additional descriptors during the SVMs' training stages, such descriptors can be related to Fourier frequency spectra, predominant period and wavelet transform coefficients.

Machine Learning Modelling and Feature Engineering in Seismology Experiment

“…There have been a number of cases that have shown successful implementation of ML in earthquake warning systems for predicting real earthquakes [20,21], their geographical coordinates [22,23] and other aspects. Combining physically based and ML-based models, a synergetic effect can be obtained when physical intuition insights are empowered by the strength of data-driven methods [15,18,24].…”