Support Vector Machine Classification of Seismic Events in the Tianshan Orogenic Belt

Discriminating between various types of seismic events is of significant scientific and societal importance. We use a machine learning method employing support vector machine (SVM) to classify tectonic earthquakes (TEs), quarry blasts (QBs), and induced earthquakes (IEs) among 30,181 1.5 < M_L <2.9 seismic events that occurred in the Tianshan orogenic belt in China from 2009 to 2017. SVM classifiers are derived based on discriminant features of a training data set consisting of 1,400 TEs selected from the aftershock sequences of 18 M_L ≥ 5.0 earthquakes, 2,881 QBs from repeating events occurring in those areas with a percentage of event daytime occurrence greater than 0.9, and 987 IEs from events in the known oil/gas fields and water reservoirs. The discriminant features include spectral amplitudes of observed P and S wave signals in a frequency range of 1–15 Hz normalized by the P spectrum and averaged over the entire seismic network, and an optional feature of the percentage of event daytime occurrence. Statistics analyses indicate that the accuracies of the SVM classifiers are 99.81% for TEs, 99.93% for QBs, and 99.62% for IEs. Our classification indicates that 37.57% of the seismic events are QBs occurring in possible mine areas and appearing mostly as clusters with a percentage of event daytime occurrence greater than 0.9, 50.12% are TEs occurring in various thrust faults in the Tianshan orogenic belt, and 12.31% are IEs or shallow tectonic earthquakes occurring mostly as clusters near oil and gas fields and water reservoirs. We reevaluate b values in the region and obtain relatively uniform values for the classified TEs with most of them below 1.0, as opposed to a large range of values (0.5–2.7) when all the seismic events are used in the analysis.