Segmented Estimation Method for Early Warning Magnitude Based on Convolutional Neural Network

doi:10.12068/j.issn.1005-3026.2024.08.002

Abstract

Abstract:

Aiming at magnitude estimation in earthquake early warning， a segmented estimation method based on convolutional neural network （CNN） is proposed. The input is the waveform starting from the P wave onset and lasting 3 s. The output is the estimated magnitude range （local magnitude M_L≥5.0 for large earthquake and M_L<5.0 for small earthquake）. If the waveform belongs to the large earthquake range， the alarm will be sent directly； if the waveform belongs to the small earthquake range， the specific magnitude value will be estimated. For the estimation of magnitude range， the accuracy of the CNN model can reach 98.04%. The mean absolute errors （MAE） of estimating small earthquake magnitudes based on parameters τ_c and P_d are 0.20 and 0.31， respectively. The results demonstrate the efficacy of the proposed segmented magnitude estimation method in accurately early warning large earthquakes and reducing the probability of missed warnings. Additionally， it enhances the precision of small earthquake magnitude estimation.

Key words: earthquake early warning, magnitude early warning, segmented estimation, convolutional neural network （CNN）, magnitude estimation parameters

CLC Number:

TP 399

Tao REN, Xin-liang LIU, Hong-feng CHEN, Yan-lu MA. Segmented Estimation Method for Early Warning Magnitude Based on Convolutional Neural Network[J]. Journal of Northeastern University(Natural Science), 2024, 45(8): 1073-1079.

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