RT Journal Article
SR Electronic
T1 Long‐Term Time‐Dependent Probabilities for the Third Uniform California Earthquake Rupture Forecast (UCERF3)
JF Bulletin of the Seismological Society of America
FD Seismological Society of America
DO 10.1785/0120140093
A1 Field, Edward H.
A1 Biasi, Glenn P.
A1 Bird, Peter
A1 Dawson, Timothy E.
A1 Felzer, Karen R.
A1 Jackson, David D.
A1 Johnson, Kaj M.
A1 Jordan, Thomas H.
A1 Madden, Christopher
A1 Michael, Andrew J.
A1 Milner, Kevin R.
A1 Page, Morgan T.
A1 Parsons, Tom
A1 Powers, Peter M.
A1 Shaw, Bruce E.
A1 Thatcher, Wayne R.
A1 Weldon, Ray J.
A1 Zeng, Yuehua
YR 2015
UL http://www.bssaonline.org/content/early/2015/03/05/0120140093.abstract
AB The 2014 Working Group on California Earthquake Probabilities (WGCEP 2014) presents time‐dependent earthquake probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3). Building on the UCERF3 time‐independent model published previously, renewal models are utilized to represent elastic‐rebound‐implied probabilities. A new methodology has been developed that solves applicability issues in the previous approach for unsegmented models. The new methodology also supports magnitude‐dependent aperiodicity and accounts for the historic open interval on faults that lack a date‐of‐last‐event constraint. Epistemic uncertainties are represented with a logic tree, producing 5760 different forecasts. Results for a variety of evaluation metrics are presented, including logic‐tree sensitivity analyses and comparisons to the previous model (UCERF2). For 30 yr M≥6.7 probabilities, the most significant changes from UCERF2 are a threefold increase on the Calaveras fault and a threefold decrease on the San Jacinto fault. Such changes are due mostly to differences in the time‐independent models (e.g., fault‐slip rates), with relaxation of segmentation and inclusion of multifault ruptures being particularly influential. In fact, some UCERF2 faults were simply too long to produce M 6.7 size events given the segmentation assumptions in that study. Probability model differences are also influential, with the implied gains (relative to a Poisson model) being generally higher in UCERF3. Accounting for the historic open interval is one reason. Another is an effective 27% increase in the total elastic‐rebound‐model weight. The exact factors influencing differences between UCERF2 and UCERF3, as well as the relative importance of logic‐tree branches, vary throughout the region and depend on the evaluation metric of interest. For example, M≥6.7 probabilities may not be a good proxy for other hazard or loss measures. This sensitivity, coupled with the approximate nature of the model and known limitations, means the applicability of UCERF3 should be evaluated on a case‐by‐case basis.