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As my colleague Mohsen Rahnama reminded us in his recent blog, the last destructive earthquake to strike Northern California was on October 17, 1989. Loma Prieta was a magnitude 6.9 earthquake which resulted in 63 deaths and about four thousand injuries. The epicenter was about ten miles northeast of Santa Cruz, and seismic waves took about 30 seconds to reach San Francisco. But there was no way of communicating any earthquake early warning to residents of the Marina district of San Francisco, which suffered some of the worst damage from shaking and fire outbreak.

On October 17, 2019, the thirtieth anniversary of this earthquake, the California Governor’s Office of Emergency Services unveiled a smartphone app from the University of California, Berkeley Seismological Lab that will give all Californians the opportunity to receive earthquake early warnings.

Governor Gavin Newsom, who happened to be in the Marina district at the time of the 1989 earthquake, has urged people to download the MyShake app. This app (myshake.berkeley.edu) is available on the Apple App Store and Google Play, and relies on the ShakeAlert earthquake early warning system, developed by the U.S. Geological Survey (USGS).

The concept of an earthquake early warning system dates way back to the 1868 Hayward earthquake in East Bay, when a physician figured out that an early warning might be sent across to San Francisco by telegraph. It has taken all these years to develop the seismological science and technology to provide useful public earthquake early warnings. Another hurdle has been financial: federal funding is restricted for state-specific risk mitigation measures. In February 2015, Dianne Feinstein, the senior Senator from California, said she hoped the California state and the private sector would contribute their fair share.

To assess what might be considered a fair contribution to the cost of operating an earthquake early warning system (EEWS) in California, an economic cost-benefit analysis is desirable. Such a quantitative analysis demands the use of a catastrophe earthquake model to estimate the reduction in fatalities and serious injuries such a system might produce for a range of major earthquake scenarios. 

The RMS® United States Earthquake Model was the first to be used for this purpose, and demonstrate that EEWS is cost-effective. The RMS model runs were undertaken at our Newark headquarters by Maurizio Gobbato and Nilesh Shome. EEWS cost-effectiveness is not self-evident.  People who happen to be close to the epicenter will receive little or no warning. Furthermore, the quality of earthquake construction in California is high; there have been fewer than two hundred earthquake deaths in the past sixty years.

The RMS modeling results were first presented at a Berkeley seminar on February 16, 2016, and will be presented again at an invited USGS seminar on October 30, 2019.  With encouragement from USGS, a technical paper on the RMS cost-effectiveness study, co-authored with Maurizio Gobbato and Nilesh Shome, has been written for open publication, to help guide future investment in EEWS.

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Gordon Woo
Catastrophist, RMS

Gordon is a catastrophe-risk expert, with 30 years’ experience in catastrophe science, covering both natural and man-made hazards. Gordon is the chief architect of the RMS terrorism risk model, which he started work on a year after joining RMS in December 2000. For his thought leadership in terrorism risk modeling, he was named by Treasury & Risk magazine as one of the 100 most influential people in finance in 2004. He has since lectured on terrorism at the NATO Center of Excellence for the Defense against Terrorism, and testified before the U.S. Congress on terrorism-risk modeling. As an acknowledged, international expert on catastrophes, Gordon is the author of two acclaimed books: “The Mathematics of Natural Catastrophes” (1999) and “Calculating Catastrophe” (2011). Dr. Woo graduated as the best mathematician of his year at Cambridge University and he completed his doctorate at MIT as a Kennedy Scholar and was a member of the Harvard Society of Fellows. He also has an Master of Science in computer science from Cambridge University.

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