Precursory remote triggering is absent before great earthquakes
Abstract
Recently, there have been numerous great (MW ≥ 8), devastating earthquakes, with a rate in the last 7 years that is 260% of the average rate over the 111-year seismological history. Each great earthquake presents an opportunity to study a major fault at the very end and very beginning of the inferred seismic cycle. In this work, we use these events as both targets and sources to probe susceptibility to dynamic triggering in the epicentral region before and after a large earthquake. This study also carefully addresses the possibility that large earthquakes interact in a cascade of remotely triggered sequences that culminate in further large earthquakes. We seek evidence of triggering associated with the 16 great MW ≥ 8 events that occurred between 1998 and 2011, using regional and global earthquake catalogs, to measure changes in inter-event time statistics. Statistical significance is calculated with respect to a non-stationary reference model that includes mainshock-aftershock clustering. In only a few cases do we detect triggering near the epicenters of M ≥ 8 earthquakes separated by more than 10 degrees. The number of detections is not significant, given the number of detection attempts. Systematic triggered rate changes are less than 15% at 95% confidence, and thus cannot account for the large increase in MW ≥ 8 earthquake rate. The catalogs are insufficiently complete to resolve more moderate triggering expected from previous studies. We calculate that an improvement in completeness magnitude from 3.7 to 3.5 could resolve the expected triggering signal in the ISC catalog taken as a whole, but an improvement to M 2.0 would be needed to consistently resolve triggering on a regional basis.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2012
- Bibcode:
- 2012AGUFM.S51F..07V
- Keywords:
-
- 4415 NONLINEAR GEOPHYSICS / Cascades;
- 7223 SEISMOLOGY / Earthquake interaction;
- forecasting;
- and prediction;
- 4318 NATURAL HAZARDS / Statistical analysis