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

Abstract

The objective of this research is to develop a new approach to estimate earthquake arrival azimuth 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 series descriptors of 863 events recorded 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 typical seismic activity in the area. During training stages of SVMs, several combinations of kernel exponent and complexity factor were applied to time series 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 windows of 5 seconds and earthquake magnitudes greater than 3.0 ML with kernel exponent of 10 and complexity factor of 2, showing an accuracy of 45.4 degrees. This research is an improvement of previous works related to earthquake arrival azimuth determination from single station data employing machine learning techniques.