Real-time prediction of earthquake potential damage:A case study for the January 8,2022 M_(S) 6.9 Menyuan earthquake in Qinghai,China

It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster reduction.Here,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of sites.The EPDor is composed of three parts:(1)predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models;(2)predicting the PGVs at distant sites based on the empirical ground motion prediction equation;(3)generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in(1)and(2).We apply the EPDor to the 2022 M_(S) 6.9 Menyuan earthquake in Qinghai Province,China to predict its potential damage.Within the initial few seconds after the first station is triggered,the EPDor can determine directly whether there is potential damage for some sites to a certain degree.Hence,we infer that the EPDor has potential application for future earthquakes.Meanwhile,it also has potential in Chinese earthquake early warning system.

Continuous prediction method of earthquake early warning magnitude for high-speed railway based on support vector machine

Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.