Deep learning for magnitude prediction in earthquake early warning

Autor:	Wang, Yanwei, Wang, Zifa, Cao, Zhenzhong, Lan, Jingyan
Rok vydání:	2019
Předmět:	Physics - Geophysics Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Fast and accurate magnitude prediction is the key to the success of earthquake early warning. We have proposed a new approach based on deep learning for P-wave magnitude prediction (EEWNet), which takes time series data as input instead of feature parameters. The architecture of EEWNet is adaptively adjusted according to the length of the input, thus eliminates the need of complicated tuning of hyperparameters for deep learning. Only the unfiltered accelerograms of vertical components are used. EEWNet is trained on a moderate number of data set (10,000s of records), but it achieves excellent results in magnitude prediction compared with approaches using parameters {\tau}log, {\tau}c and Pd. Comment: 6 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1912.05531 Zobrazit plný text záznamu View this record from Arxiv