Fast Determination of Earthquake Depth Using Seismic Records of a Single Station, Implementing Machine Learning Techniques

Abstract

The purpose of this research is to apply a new approach to make a fast determination of earthquake depth using seismic records of the “El Rosal” station, near to the city of Bogota – Colombia, by applying support vector machine regression (SVMR). The algorithm was trained with descriptors obtained from time signals of 863 seismic events acquired between January 1998 and October 2008; only earthquakes with magnitude ≥ 2 were contemplated, filtering its signals to remove diverse kind of noises not related to earth tremors. During training stages of SVMR several combinations of kernel function exponent and complexity factor were considered for 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 SVMR was obtained using time signals of 15 seconds and earthquake magnitudes of 3.5 with kernel exponent of 10 and complexity factor of 2, showing accuracy of 0.6 ± 16.5 kilometers, which is good enough to be used in an early warning system for the city of Bogota. It is recommended to provide this model with a previous phase of deep-shallow classification.