Spatial Evaluation and Verification of Earthquake Simulators
In this paper, we address the problem of verifying earthquake simulators with observed data. Earthquake simulators are a class of computational simulations which attempt to mirror the topological complexity of fault systems on which earthquakes occur. In addition, the physics of friction and elastic interactions between fault elements are included in these simulations. Simulation parameters are adjusted so that natural earthquake sequences are matched in their scaling properties. Physically based earthquake simulators can generate many thousands of years of simulated seismicity, allowing for a robust capture of the statistical properties of large, damaging earthquakes that have long recurrence time scales. Verification of simulations against current observed earthquake seismicity is necessary, and following past simulator and forecast model verification methods, we approach the challenges in spatial forecast verification to simulators; namely, that simulator outputs are confined to the modeled faults, while observed earthquake epicenters often occur off of known faults. We present two methods for addressing this discrepancy: a simplistic approach whereby observed earthquakes are shifted to the nearest fault element and a smoothing method based on the power laws of the epidemic-type aftershock (ETAS) model, which distributes the seismicity of each simulated earthquake over the entire test region at a decaying rate with epicentral distance. To test these methods, a receiver operating characteristic plot was produced by comparing the rate maps to observed \(m>6.0\) earthquakes in California since 1980. We found that the nearest-neighbor mapping produced poor forecasts, while the ETAS power-law method produced rate maps that agreed reasonably well with observations.
KeywordsEarthquake simulators ETAS Earthquake forecasting RELM
JMW and JBR would like to acknowledge support for this research from NASA Grant NNX12A22G and SCEC/USC Grant USC32774854-NSF FFT.
- Chernick, M. R. (2011). Bootstrap methods: A guide for practitioners and researchers (vol. 619). Hoboken, New jersey: Wiley.Google Scholar
- Glasscoe, M., Rosinski, A., Vaughan, D., & Morentz, J. (2014). Disaster response and decision support in partnership with the california earthquake clearinghouse. In AGU Fall Meeting Abstracts (vol. 1, p. 07).Google Scholar
- Gutenberg, B. & Richter, C. (1954). Seismicity of the earth and associated phenomena. Princeton, New Jersey: Princeton University Press.Google Scholar
- Jolliffe, I. (2014). Principal Component Analysis. Wiley StatsRef: Statistics Reference Online.Google Scholar
- Jolliffe, I. T., & Stephenson, D. B. (2003). Forecast verification: a practitioner’s guide in atmospheric science. Chichester, West Sussex, England: WileyGoogle Scholar
- Nanjo, K. Z. (2010). Earthquake forecast models for italy based on the ri algorithm. Annals of Geophysics, 53(3), 117–127.Google Scholar
- Parsons, T. (2008). Appendix c: Monte carlo method for determining earthquake recurrence parameters from short paleoseismic catalogs: Example calculations for california. US Geological Survey Open File Report, 1437-C, 32.Google Scholar
- Petersen, M. D., Moschetti, M. P., Powers, P. M., Mueller, C. S., Haller, K. M., Frankel, A. D., et al. (2014). Documentation for the 2014 update of the united states national seismic hazard maps. Technical report, US Geological Survey.Google Scholar
- Schultz, K. W., Sachs, M. K., Heien, E. M., Yoder, M. R., Rundle, J. B., Turcotte, D. L., & Donnellan, A. (2015). Virtual quake: Statistics, co-seismic deformations and gravity changes for driven earthquake fault systems. In International Association of Geodesy Symposia (pp. 1–9). doi: 10.1007/1345_2015_134.
- Yoder, M. R., Schultz, K. W., Heien, E. M., Rundle, J. B., Turcotte, D. L., Parker, J. W., & Donnellan, A. (2015b). The Virtual Quake earthquake simulator: a simulation-based forecast of the El Mayor-Cucapah region and evidence of predictability in simulated earthquake sequences. Geophysical Journal International, 203(3), 1587–1604. CrossRefGoogle Scholar
- Yoder, M. R., Turcotte, D. L., & Rundle, J. (2011). Record-breaking earthquake precursors. PhD thesis.Google Scholar
- Zechar, J. D., Schorlemmer, D., Liukis, M., Yu, J., Euchner, F., Maechling, P. J., et al. (2010). The collaboratory for the study of earthquake predictability perspective on computational earthquake science. Concurrency and Computation: Practice and Experience, 22(12), 1836–1847.CrossRefGoogle Scholar