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Published online by Cambridge University Press: 05 June 2012
Abstract. Making sense of earthquake forecasts is surprisingly difficult. In part, this is because the forecasts are based on a complicated mixture of geological maps, rules of thumb, expert opinion, physical models, stochastic models, and numerical simulations, as well as geodetic, seismic, and paleoseismic data. Even the concept of probability is hard to define in this context. For instance, the U.S. Geological Survey developed a probability model according to which the chance of an earthquake of magnitude 6.7 or greater before the year 2030 in the San Francisco Bay Area is 0.7 ± 0.1. How is that to be understood? Standard interpretations of probability cannot be applied. Despite their careful work, the USGS probability estimate is shaky, as is the uncertainty estimate.
Introduction
What is the chance that an earthquake of magnitude 6.7 or greater will occur before the year 2030 in the San Francisco Bay Area? The U.S. Geological Survey estimated the chance to be 0.7 ± 0.1 (USGS, 1999). In this chapter, we try to interpret such probabilities.
Making sense of earthquake forecasts is surprisingly difficult. In part, this is because the forecasts are based on a complicated mixture of geological maps, rules of thumb, expert opinion, physical models, stochastic models, numerical simulations, as well as geodetic, seismic, and paleoseismic data.
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