Figure 3.17: Histogram showing the performance of seismic intensity estimate for the existing JMA EEW system (JMA), an algorithm proposed by Tamaribuchi et al. (2014) (TK) and the new algorithm proposed in this chapter (RBIS) based on the selected 71 earthquakes. X-axis shows the error of the Japanese seismic intensity calculated by:
EEW estimates - Catalogue values.
results from Tamaribuchi et al. (2014) and the proposed algorithm in this chapter. Figure 3.17 shows that the algorithm proposed in this chapter slightly improves the seismic intensity estimate from the one proposed by Tamaribuchi et al. (2014). In fact, both algorithms use a probabilistic approach to solve the multi-events cases. One important difference is that the algorithm in this work includes Hi-net stations data for estimating all earthquake parameters, but the other does not. The use of a denser seismic network results in faster and more accurate estimates. Almost the same performance is achieved by using only two features (P-wave picking time and maximum displacement amplitude) in the new algorithm, as compared to the algorithm proposed by Tamaribuchi et al. (2014), which uses a total of four features. Although some treatment of the data is needed in order to integrate data from the two different seismic networks, which may increase the uncertainty in the process, the resulting benefits make it worthwhile to do so.
limitation of EEW, a simple numerical method, Rao-Blackwellized Importance Sampling with a set of sequential proposal PDFs, is used to estimate the earthquake parameters and the necessary equations are derived analytically as much as possible. Also, some suggestions are given to improve the algorithm for practical use.
For illustration purposes, the existing JMA EEW system is taken as an example to demonstrate the process of applying the proposed probabilistic method to an existing deterministic EEW model.
Two features, the P-wave picking time and the maximum displacement amplitude, are chosen for earthquake parameter estimation. A Gaussian model is used for the likelihood function of both features. A real example based on two months data (March 9 to April 30, 2011) around the time of the 2011 Tohoku earthquake is studied to verify the proposed algorithm. The algorithm is able to accurately identify multiple concurrent events. Significant improvement is shown in comparison with the existing JMA EEW system. Also, the importance of having a denser network is demonstrated in the test. The inclusion of the Hi-net stations greatly enhances the average warning lead time and accuracy of the EEW predictions.
Chapter 4
ePAD: Framework for Automated Decision-Making for EEW
Applications
Since EEW predicts earthquake parameters based on incomplete waveform data, high uncertainty is expected from the broadcast warning. Although the uncertainty may reduce as more data is collected, the reduced lead time may not be enough to complete a mitigation action. The decision of what the optimal action is and when to perform the action involves a complicated trade-off between potential missed and false alarms due to the uncertainty of EEW prediction and lead time. In this chapter, I propose an earthquake probability-based automated decision-making (ePAD) framework that can handle this problem. It is designed to include the contributions of lead time and uncertainties from the selected models and EEW information in the automated decision- making based on a cost-benefit model. This framework, which can be flexibly implemented for specific applications, allows users to easily pick their desired decision behavior (i.e., to control how uncertainty influences the decision).
Applying a performance-based earthquake early warning (PBEEW) methodology (Grasso, 2005;
Iervolino, 2011), which is a combination of performance-based earthquake engineering (PBEE) (Porter, 2003) and EEW, ePAD separates the EEW information input from all the pre-calculable user-specific models (e.g., decision model, structural model and/or ground motion prediction equa- tion (GMPE)), and combines the latter into a single function called the Decision Function (DF).
This leads to a simpler representation during calculation and an easier way of performing analyses, as well as allowing the possibility of replacing the pre-calculable function with a surrogate model
for fast computing. The lead time contribution is embedded in the framework in two ways: an incomplete action model and a value of information model. The former considers the case where the benefit and cost of a mitigation action may change if it is not completed before the arrival of the damaging seismic waves; the latter considers the case where a decision may be delayed if there is to be future updates of EEW information and the predicted lead time is more than enough to complete a mitigation action.
This chapter is based on my previous publication in Wu et al. (2012, 2013). First, I start with a brief review on the recently proposed decision methods for EEW applications. Then, the basic theory and details of the ePAD framework is presented. Finally, various decision criteria are compared under the ePAD framework and an illustrative example is presented based on a PEER benchmark office building.
4.1 Review of EEW decision-making methods
The state-of-the-art decision-making method for most of the existing EEW applications in practice is based on the classical threshold method, which takes an action when the probability of a ground shaking intensity measure (IM) exceeding some pre-set threshold im0 is greater than some fixed value P0. In many cases, engineers may simply set a threshold value for the expected value of IM, ignoring most of the influence from the IM uncertainty. In these methods, calibration of the decision parameter values plays an important role. Researchers have converged on the idea of using cost-benefit analysis as a basis for determining an appropriate value for the parameters. Moreover, some researchers suggest directly using the a cost-benefit analysis for the decision criterion. This idea motivated the development of PBEEW, which uses the PEER PBEE model to perform cost- benefit analysis for decision making of EEW applications.
In Grasso et al. (2007), the authors introduced a trade-off between the expected loss due to false alarm and the expected loss due to missed alarm to determineP0 whenim0 has already been determined by some engineering study. An action is taken when the expected loss due to a missed alarm is larger than the expected loss due to a false alarm. They showed that this is equivalent to the probability of IM exceeding a specified threshold im0 being greater than a value P0 that depends on these expected losses.
Iervolino et al. (2007) examined a decision framework to decide when to trigger an earthquake alarm. Instead of the threshold-based method, an expected-loss-based decision criterion using the PBEE approach is considered. In this approach, a thorough loss assessment is performed and the decision is made by monitoring the expected loss due to taking action and no action.
These methods make a significant contribution to automating decisions for mitigation actions.
However, there are still limitations for practical usage. One of the most important limitations is that the decision for most EEW applications is time sensitive due to a short and uncertain lead time, and this uncertainty should be explicitly treated in decision making, including the fact that the ground shaking may start before the mitigation action is completed. The ePAD decision framework presented next provides a flexible platform for further development of the models used in the decision-making process. It includes a model for explicit lead time treatment in the real- time decision process. Also, the concepts embedded in this framework can be used to analyze the differences between previously published decision methods for EEW applications.