Fast estimation of earthquake arrival azimuth using a single seismological station and machine learning techniques

Abstract

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.