HR: 0830h
AN: NG41C-0069 INVITED     [PDF]
TI: Long-Term Earthquake Forecasts in the San Francisco Bay Area: A Contrarian Perspective
AU: * Lindh, A G
EM: aglindh@cruzio.com
AF: U.S. Geological Survey, 277 Molina Drive, Santa Cruz, CA 95060 United States
AB: In historic time the San Francisco Bay Area (SFBA) has been the site of four large earthquakes, including the M7.8 1906 San Francisco earthquake, and most recently, the M6.9 1989 Loma Prieta earthquake. Of the eight major fault segments considered here, two have not experienced large earthquakes in about 200 years, and the SF Peninsula segment of the 1906 rupture on the San Andreas appears from my calculations to be close to fully reloaded as well. I have used simple geophysical and statistical models (elastic rebound model and Weibull distribution) to estimate the probability of large earthquakes (M7 or larger) in the SFBA in the coming decades. I have used seismicity, geology, and geodesy to estimate segment boundaries, recurrence intervals, and the associated uncertainties. The results indicate that the SFBA has an approximately 80% chance of a large earthquake in the next 30 years, with four segments dominating the 30 yr probabilities; San Francisco Peninsula (32%), Southern Hayward (39%), Northern Hayward (28%) and Rodgers Cr (30%). Because of the proximity of these four segments to the urban portions of San Francisco and Oakland, the probability of these most vulnerable areas experiencing strong ground motion (an M7 within 25 km or less) one or more times within the next 30 years is about 70%. Because of the breadth and quality of our understanding of the earthquake machine in the SFBA, these probabilities depend in large part on the intrinsic variance in the earthquake recurrence process itself -- most conveniently expressed as the ratio of the standard deviation to the mean recurrence time, or intrinsic coefficient of variation ($CV_{I}$). I have applied a new approach to estimating $CV_{I}$, using the time since the last characteristic event (the "open-interval") on well characterized segments. Combined with an estimate of the mean recurrence time on each segment, an estimate of the likelihood of each open interval can be computed, and a simple maximum likelihood procedure used to choose the best fitting value of $CV_{I}$. This approach has significant advantages over the more common approach of using a sequence of paleoseismic dates to estimate $CV_{I}$, since the completeness of paleoseismic records is always in question, and even one missed event seriously biases estimates of $CV_{I}$. The power in the technique lies not in establishing that a sequence of earthquake recurrence dates is very regular, but rather in establishing that there are no very short intervals; large values of $CV_{I}$ imply a large fraction of short recurrence intervals. Analysis of 10 major segments in California for which the requisite data are available yields an estimate for $CV_{I}$ of 0.2 or less; this value was used in the calculations outlined above. If we use this model to "backcast" seismicity rates for the last few centuries, we can fit reasonably well the large decrease in moderate seismicity following the 1906 earthquake, even though each segment is modeled independently. That is, no far-field stress perturbations from 1906 have been used in this work, nor do they appear to be needed to account for the gross variations in seismicity rate. This may call into question the wide-spread assumption that a "stress shadow" is self-evident following 1906, and suggests that more careful modeling is required to establish whether far-field stress changes are necessary to fit the observations. Prior statistical tests have shown that the observed variations are extremely unlikely, given a Poisson model for a null hypothesis. From the work presented here it appears one could not reject a simple time-dependent model in the same manner, even though it includes no far-field stress effects.
DE: 7223 Seismic hazard assessment and prediction
SC: Nonlinear Geophysics [NG]
MN: 2003 Fall Meeting