Severity Classification of a Seismic Event based on the Magnitude-Distance Ratio Using Only One Seismological Station

[摘要] Seismic event characterization is often accomplished using algorithms based only on information received at seismological stations located closest to the particular event, while ignoring historical data received at those stations. These historical data are stored and unseen at this stage. This characterization process can delay the emergency response, costing valuable time in the mitigation of the adverse effects on the affected population. Seismological stations have recorded data during many events that have been characterized by classical methods, and these data can be used as previous "knowledge" to train such stations to recognize patterns. This knowledge can be used to make faster characterizations using only one three-component broadband station by applying bio-inspired algorithms or recently developed stochastic methods, such as kernel methods. We trained a Support Vector Machine (SVM) algorithm with seismograph data recorded by INGEOMINAS's National Seismological Network at a three-component station located near Bogota, Colombia. As input model descriptors, we used the following: (1) the integral of the Fourier transform/power spectrum for each component, divided into 7 windows of 2 seconds and beginning at the P onset time, and (2) the ratio between the calculated logarithm of magnitude (Mb) and epicentral distance. We used 986 events with magnitudes greater than 3 recorded from late 2003 to 2008.

[学科分类] 天文学(综合)

[关键词] Machine learning;seismology;single station;magnitude;distance.