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

Abstract

PurposeUsing the strong motion data of K-net in Japan, the continuous magnitude prediction method based on support vector machine (SVM) was studied.Design/methodology/approachIn 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.FindingsThe 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, the magnitude prediction error of the SVM-HRM model is obviously smaller than that of the traditional τc 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 the magnitude range of 3–5, the single station realization rate of the SVM-HRM model 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 Test Method of EEW and Monitoring System for High-Speed Railway.” For earthquake events with magnitudes ranging from 3 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/valueAt 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.

Performance verification of Shandong earthquake early warning station network based on spatial analysis

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Simulation Method of Port Petrochemical Industry Throughput Development under the Background of Integration of Port, Industry and City

“…Song et al carried out earthquake early warning P-wave prediction on the basis of SVM. After model training, the test error of this method is small [12]. In order to predict PM2.5 of air pollution, Lai X et al carried out relevant prediction by improving SVM algorithm on the basis of feature selection.…”

“…In formula (12), ' H , H and H are mean normalized, original and original average data respectively. Mean Absolute Error (MAE) is used to evaluate the prediction effect, as Formula (13).…”

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“…With the rise of machine learning technology, the Support Vector Machines (SVM) model has been well applied in many research fields [2][3][4]. Based on this, this paper considers the SVM model for speech recognition research.…”