Annual Meeting - SCEC.org

References
SCEC Research Accomplishments | Report
Gerstenberger, M. C., L. M. Jones, and S. Wiemer (2007). Short-term aftershock probabilities: Case studies in California,
Seismol. Res. Lett., 78, 66-77.
Gerstenberger, M.C. & D. A. Rhoades (2010), New Zealand Earthquake Forecast Testing Centre, Pure Appl. Geophys., 167,
877-892.
Gerstenberger, M. C., S. Wiemer, L. M. Jones, and P. A. Reasenberg (2005). Real-time forecasts of tomorrow's earthquakes in
California, Nature, 435, 328-331.
Field, E. H. (2007). Overview of the working group for the development of regional earthquake likelihood models (RELM),
Seismol. Res. Lett., 78, 7-16.
Jolliffe, I. T., and D. B. Stephenson (2003). Forecast Verification – A Practitioner's Guide in Atmospheric Science, John Wiley &
Sons, Chichester, 254pp.
Jordan, T. H. (2006). Earthquake predictability, brick by brick, Seismol. Res. Lett., 77, 3-6.
Marzocchi, W., D. Schorlemmer & S. Wiemer, eds. (2010), An Earthquake Forecast Experiment in Italy, Special volume, Ann.
Geofisica, 53 (3), 163 pp.
Nanjo, K. Z., H. Tsuruoka, N. Hirata & T. H. Jordan (2011), Overview of the first earthquake forecast testing experiment in
Japan, Earth Planets Space, 63, 159–169, doi:10.5047/eps.2010.10.003.
Schorlemmer, D., M. C. Gerstenberger, S. Wiemer, D. D. Jackson, and D. A. Rhoades (2007). Earthquake likelihood model
testing, Seismol. Res. Lett., 78, 17-29.
Schorlemmer, D., J. D. Zechar, M. J. Werner, E. H. Field, D. D. Jackson, T. H. Jordan, and the RELM Working Group (2010).
First results of the Regional Earthquake Likelihood Models experiment, Pure Appl. Geophys., 167, 859-876,
doi:10.1007/s00024-010-0081-5.
Wyss, M., and D. C. Booth (1997). The IASPEI procedure for the evaluation of earthquake precursors, Geophys. J. Int., 131, 423-
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Zechar, J. D., and T. H. Jordan (2008). Testing alarm-based earthquake predictions, Geophys. J. Int., 172, 715-724.
Zechar, J. D., D. Schorlemmer, M. Liukis, J. Yu, F. Euchner, P. J. Maechling, and T. H. Jordan (2010). The Collaboratory for
the Study of Earthquake Predictability perspective on computational earthquake science, Concurr. Comput.-Pract. Exp.,
22, 1836-1847, doi:10.1002/cpe.1519.
Community Modeling Environment
The SCEC Community Modeling Environment (CME) collaboration conducts earthquake system science and computational
science research with funding from the National Science Foundation (NSF), the U.S. Geological Survey (USGS), and other
sources. Many important seismic hazard data products are computationally intensive and computational improvements can
lead to improved seismic hazard information. CME researchers are developing new ways to use high performance computing
to advance seismic hazard research. The CME research program provides an avenue through which basic SCEC research
advancements can be implemented in computational form and integrated into standard, broad-impact, ground motion
forecast calculations. More than thirty researchers, graduate students, and staff participated in this year’s CME collaborative
research activities. The following sections summarize several CME research accomplishments between August 2010 and
August 2011. More detailed information about these, and other, CME research accomplishments are presented in the SCEC
Annual Meeting poster sessions.
California 3D Velocity Model Development
Earthquake simulations require accurate 3D seismic velocity models to produce accurate ground motion estimates. To support
quantitative comparison between alternative CVM’s, we established a standardized and automated velocity model evaluation
system, and we used this system to evaluate existing SCEC CVM models. Our CVM evaluation system uses wave propagation
simulations to evaluate 3D velocity models in the following way. Our CVM evaluation system builds a 3D velocity mesh
using the CVM under test, runs a forward wave propagation simulation at 1Hz for a well-recorded southern California
earthquake. The simulated ground motion records (seismograms) are compared to observed seismograms for the event using
standard goodness of fit metrics including map-based plots that show geographical variations in goodness of fit results.
With a CVM evaluation system in place, we developed, evaluated, and released an updated version of CVM-H. Released in
February 2011, CVM-H v11.2 updates an earlier version of the CVM-Harvard velocity model called CVM-H v6.3. CVM-H
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