Real-time numerical shake prediction and updating for earthquake early warning

Abstract

Ground motion prediction is important for earthquake early warning systems, because the region's peak ground motion indicates the potential disaster. In order to predict the peak ground motion quickly and precisely with limited station wave records, we propose a realtime numerical shake prediction and updating method. Our method first predicts the ground motion based on the ground motion prediction equation after P waves detection of several stations, denoted as the initial prediction. In order to correct the prediction error of the initial prediction, an updating scheme based on real-time simulation of wave propagation is designed. Data assimilation technique is incorporated to predict the distribution of seismic wave energy precisely. Radiative transfer theory and Monte Carlo simulation are used for modeling wave propagation in 2-D space, and the peak ground motion is calculated as quickly as possible. Our method has potential to predict shakemap, making the potential disaster be predicted before the real disaster happens. 2008 M S 8.0 Wenchuan earthquake is studied as an example to show the validity of the proposed method.

Earthquake early warning: Recent advances and perspectives

“…For example, de Matteis and Convertito (2015) developed a procedure for updating the PGA parameters of a GMM (and hence reducing its associated standard deviation) to account for the specific features of a seismic source and propagation medium, which uses real-time maximum acceleration amplitudes recorded during an event at one-second intervals. Wang et al (2017) proposed a technique for replacing initial PGV estimates from a GMM with more certain real-time amplitude predictions calculated using recorded seismogram envelopes and wave propogation (i.e., radiative transfer) modelling. Hoshiba and Aoki (2015); Kodera et al (2018Kodera et al ( , 2016…”

Use of OR in earthquake operations management: A review of the literature and roadmap for future research

“…Mathematical programing [89,[119][120][121][122][123][124]] Heuristic [125][126][127] Decision analysis [122] Emergency response & relief chain coordination Soft OR [128] Early warning system Earthquake/tsunami prediction and notification Machine learning [129][130][131][132][133][134][135][136] Simulation [137,138]…”

“…Machine learning [133][134][135] Earthquake detection Machine learning [136] Early warning lead time and reliability estimation Simulation [137] Ground motion prediction Simulation [138] Seismometer/tsunameter location Mathematical programing [139] Heuristic [140] As seen in Table 10, machine learning is the most commonly used approach in EEWS prediction and performance, comprising 8 (67%) of the 12 studies reviewed here. An example application of machine learning is to reduce false alarms by rapidly and reliably discriminating real earthquake signals from other signals [133][134][135].…”

“…Somewhat surprisingly, only a couple examples of simulation being applied in EEWS were found in the literature. Wang et al [138], for example, propose a Monte Carlo simulation approach to predict peak ground motion quickly and precisely given limited seismic data. Information about ground motion is crucial in early warning systems because a region's peak ground motion provides an indication of the scale of the potential disaster in terms of building damage and threat to life.…”