S31B-07
Three Ingredients for Improved Global Aftershock Forecasts: Tectonic Region, Time-Dependent Catalog Incompleteness, and Inter-Sequence Variability

Wednesday, 16 December 2015: 09:30
305 (Moscone South)
Morgan T Page, USGS Pasadena Field Office, Pasadena, CA, United States, Jeanne Hardebeck, USGS, Baltimore, MD, United States, Karen R Felzer, USGS, Pasadena, CA, United States and Andrew Jay Michael, USGS California Water Science Center Menlo Park, Menlo Park, CA, United States
Abstract:
Following a large earthquake, seismic hazard can be orders of magnitude higher than the long-term average as a result of aftershock triggering. Due to this heightened hazard, there is a demand from emergency managers and the public for rapid, authoritative, and reliable aftershock forecasts.

In the past, USGS aftershock forecasts following large, global earthquakes have been released on an ad-hoc basis with inconsistent methods, and in some cases, aftershock parameters adapted from California. To remedy this, we are currently developing an automated aftershock product that will generate more accurate forecasts based on the Reasenberg and Jones (Science, 1989) method. To better capture spatial variations in aftershock productivity and decay, we estimate regional aftershock parameters for sequences within the Garcia et al. (BSSA, 2012) tectonic regions. We find that regional variations for mean aftershock productivity exceed a factor of 10.

The Reasenberg and Jones method combines modified-Omori aftershock decay, Utsu productivity scaling, and the Gutenberg-Richter magnitude distribution. We additionally account for a time-dependent magnitude of completeness following large events in the catalog. We generalize the Helmstetter et al. (2005) equation for short-term aftershock incompleteness and solve for incompleteness levels in the global NEIC catalog following large mainshocks.

In addition to estimating average sequence parameters within regions, we quantify the inter-sequence parameter variability. This allows for a more complete quantification of the forecast uncertainties and Bayesian updating of the forecast as sequence-specific information becomes available.