Support Vector Machine-Based Rapid Magnitude Estimation Using Transfer Learning for the Sichuan–Yunnan Region, China

Abstract

ABSTRACTThe Sichuan–Yunnan region is a seismically active area. To explore the feasibility of using the support vector machine (SVM) method for magnitude estimation in the area and to improve the rapid magnitude estimation accuracy, we construct an SVM magnitude estimation model using transfer learning (TLSVM-M model) based on a single-station record in this study. We find that the magnitude estimation of a single station shows that for the test dataset, within the 3 s time window after the P-wave arrival, the average absolute error (which reflects the size of the estimated magnitude error as a whole) and standard deviation (which reflects the scatter of magnitude estimation error) of the magnitudes estimated by the TLSVM-M model are 0.31 and 0.41, respectively, which are less than those of the SVM magnitude estimation model without transfer learning (0.44 and 0.55, respectively), the τc method (1.35 and 1.74, respectively) and the Pd method (0.44 and 0.56, respectively). In addition, in test involving five earthquake events via the TLSVM-M model, at 1 s after the first station is triggered, the magnitudes of three events (Ms 4.2, 5.2, and 6.3) are estimated within an error range of ±0.3 magnitude units. For the other two earthquakes (Ms 6.6 and 7.0), there is an obvious magnitude underestimation problem at 1 s after the first station is triggered, with less underestimation by increasing time after the first station is triggered. Meanwhile, for these two events (Ms 6.6 and 7.0), within 13 s after the first station was triggered, the magnitude estimation errors are both within ±0.3 magnitude units. The TLSVM-M model has the capability of rapid magnitude estimation for small-to-moderate events in the Sichuan–Yunnan region. Meanwhile, we infer that the proposed model may have potential in earthquake early warning.