ePAD: Earthquake Probability‐Based Automated Decision‐Making Framework for Earthquake Early Warning

Wu, S. C.; Beck, James L.; Heaton, Thomas H.

doi:10.1111/mice.12048

Cited by 29 publications

(31 citation statements)

References 32 publications

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“…For a special work, Wu et al (2013) developed a robust automated decision process called the earthquake probabilitybased automated decision-making (ePAD) framework. The ePAD framework was used to broadcast a warning of the predicted location and magnitude shortly before an earthquake hits a site as part of the EEW in California: CISN ShakeAlert System.…”

Section: Introductionmentioning

confidence: 99%

Backpropagation neural network as earthquake early warning tool using a new modified elementary Levenberg–Marquardt Algorithm to minimise backpropagation errors

Lin

Chao

Chiou

2018

Geosci. Instrum. Method. Data Syst.

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Abstract.A new modified elementary LevenbergMarquardt Algorithm (M-LMA) was used to minimise backpropagation errors in training a backpropagation neural network (BPNN) to predict the records related to the ChiChi earthquake from four seismic stations: Station-TAP003, Station-TAP005, Station-TCU084, and Station-TCU078 belonging to the Free Field Strong Earthquake Observation Network, with the learning rates of 0.3, 0.05, 0.2, and 0.28, respectively. For these four recording stations, the M-LMA has been shown to produce smaller predicted errors compared to the Levenberg-Marquardt Algorithm (LMA). A sudden predicted error could be an indicator for Early Earthquake Warning (EEW), which indicated the initiation of strong motion due to large earthquakes. A trade-Off decision-making process with BPNN (TDPB), using two alarms, adjusted the threshold of the magnitude of predicted error without a mistaken alarm. With this approach, it is unnecessary to consider the problems of characterising the wave phases and pre-processing, and does not require complex hardware; an existing seismic monitoring network-covered research area was already sufficient for these purposes.

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Section: Introductionmentioning

confidence: 99%

Backpropagation neural network as earthquake early warning tool using a new modified elementary Levenberg–Marquardt Algorithm to minimise backpropagation errors

Lin

Chao

Chiou

2018

Geosci. Instrum. Method. Data Syst.

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“…Ground motion prediction equation (GMPE) development is an important task in structural engineering and engineering seismology (Douglas, ; Zhou and Adeli, ; Alimoradi et al., ; Panakkat and Adeli, ; Wu et al., ; Alimoradi and Beck, ; Chaulagain et al., ), and a number of GMPEs have been developed (Abrahamson et al., ; Campbell and Bozorgnia, ; Chiou and Youngs, ; Idriss, ; Melo et al., ; Stewart et al., ; Stewart et al., ). A typical GMPE for predicting peak ground acceleration (PGA) is:

lo {normalg}_{10} PGA = F_{M} + F_{D} + F_{S} + ε

where

F_{M}

F_{D}

, and

F_{S}

represent magnitude, distance, and site effects, respectively; ε is the zero‐mean Gaussian prediction error.…”

Section: Introductionmentioning

confidence: 99%

Ground Motion Prediction Equation Development by Heterogeneous Bayesian Learning

Yuen

2016

Computer aided Civil Eng

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In ground motion prediction, the key is to develop a suitable and reliable GMPE (ground motion prediction equation) characterizing the ground motion pattern of the target seismic region. There are two critical goals encountered in GMPE development. Proposing a suitable predictive formula applicable to target seismic region has attracted much of the attention in previous studies. On the other hand, dependence between prediction–error variance and ground motion data has been observed and the study on this kind of heterogeneous relation becomes an important task yet to be explored. In this article, a novel HEteRogeneous BAyesian Learning (HERBAL) approach is proposed for achieving these two goals simultaneously. The homogeneity assumption on error in the traditional learning approach is relaxed, so the proposed approach is applicable for more general heterogeneous cases. With the generalization made on the traditional Bayesian learning by embedding the derived closed form expression for error variance parameter optimization component into the hyperparameter optimization of ARD (automatic relevance determination) prior, the proposed learning approach is capable of performing continuous model training on a prescribed predictive formula with unknown error pattern. A database of strong ground motion records in the Tangshan region of China is obtained for the analysis. It is shown that the trained optimal model class by the proposed approach is promising as that, the trained optimal model class retains model simplicity of the predictive formula with capability on both robustness enhancement ground motion prediction and precise determination of the error pattern.

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“…Many approaches have been proposed for earthquake prediction and estimation (Panakkat and Adeli, , , ; Yuen and Mu ; Wu et al. ; Adeli and Panakkat, ). Based on the requirement of information for the algorithms to estimate earthquake parameters, EEW techniques can be divided into two groups, namely regional warning and on‐site warning techniques.…”

Section: Introductionmentioning

confidence: 99%

Two Novel Approaches to Reduce False Alarm Due to Non‐Earthquake Events for On‐Site Earthquake Early Warning System

Hsu

Chang

2016

Computer aided Civil Eng

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An on‐site earthquake early warning system (EEWS) can provide more lead‐time at regions that are close to the epicenter of an earthquake because only seismic information of a target site is required. Instead of leveraging the information of several stations, the on‐site system extracts some P‐wave features from the first few seconds of vertical ground acceleration of a single station. It then predicts the intensity of the forthcoming earthquake at the same station according to these features. However, the system may be triggered by some vibration signals that are not caused by an earthquake or by interference from electronic signals, which may consequently result in a false alarm at the station. Thus, this study proposes two approaches to distinguish the vibration signals caused by non‐earthquake events from those caused by earthquake events based on support vector classification (SVC) and singular spectrum analysis (SSA). In the first approach (Approach I), the fast Fourier transform algorithm and the established SVC model are employed to classify the vibration signals. In the second approach (Approach II), a SSA criterion is added to Approach I for the purpose of identifying earthquake events that are classified as non‐earthquake events by the SVC model with increased accuracy. Both approaches are verified by using data collected from the Taiwan Strong Motion Instrumentation Program and EEW stations of the National Center for Research on Earthquake Engineering. The results indicate that both of the proposed approaches effectively reduce the possibility of false alarms caused by an unknown vibration event.

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ePAD: Earthquake Probability‐Based Automated Decision‐Making Framework for Earthquake Early Warning

Cited by 29 publications

References 32 publications

Backpropagation neural network as earthquake early warning tool using a new modified elementary Levenberg–Marquardt Algorithm to minimise backpropagation errors

Backpropagation neural network as earthquake early warning tool using a new modified elementary Levenberg–Marquardt Algorithm to minimise backpropagation errors

Ground Motion Prediction Equation Development by Heterogeneous Bayesian Learning

Two Novel Approaches to Reduce False Alarm Due to Non‐Earthquake Events for On‐Site Earthquake Early Warning System

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