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Published November 2013 | public
Journal Article

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


The benefits and feasibility of earthquake early warning (EEW) are becoming more appreciated throughout the world. An EEW system detects an earthquake initiation based on a seismic sensor network and broadcasts a warning of the predicted location and magnitude shortly before an earthquake hits a site. The typical range of this lead time is very short, for example, from a few seconds up to a minute in California, which is a huge challenge for applications taking advantage of EEW. As a result, a robust automated decision process about whether to initiate a mitigation action is essential. Recent approaches based on cost–benefit analyses to properly treat the trade-off between false alarms and missed alarms still face challenges in practical use, such as the exclusion of an important factor, lead time, in the real-time decision process. In this study, we lay out an earthquake probability-based automated decision-making (ePAD) framework to give a general decision criterion based on basic decision theory and an existing cost–benefit analysis procedure. The concepts of decision function, decision contour, and surrogate model are utilized to achieve fast computation and to allow comparison between various decision criteria. A value of information model is developed to handle the lead time of EEW and its uncertainty to reduce the "false response rate" in the cost–benefit trade-off. An illustrative example is presented to demonstrate how this framework allows more flexibility for users to adapt ePAD to correspond to their desired rational decision behavior.

Additional Information

© 2013 Computer-Aided Civil and Infrastructure Engineering. Article first published online: 17 Sep. 2013. The authors would like to gratefully acknowledge funding from the U.S. Geological Survey and Gordon and Betty Moore Foundation for this project. The authors would also like to thank Prof. Hiroo Kanamori and Dr. Maren Böse at California Institute of Technology for providing valuable information on recent EEW developments, and Shuo Han at California Institute of Technology for providing valuable advice on the value of information theory.

Additional details

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October 25, 2023