Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published September 2014 | Supplemental Material + Published
Journal Article Open

Bayesian historical earthquake relocation: an example from the 1909 Taipei earthquake

Abstract

Locating earthquakes from the beginning of the modern instrumental period is complicated by the fact that there are few good-quality seismograms and what traveltimes do exist may be corrupted by both large phase-pick errors and clock errors. Here, we outline a Bayesian approach to simultaneous inference of not only the hypocentre location but also the clock errors at each station and the origin time of the earthquake. This methodology improves the solution for the source location and also provides an uncertainty analysis on all of the parameters included in the inversion. As an example, we applied this Bayesian approach to the well-studied 1909 M_w 7 Taipei earthquake. While our epicentre location and origin time for the 1909 Taipei earthquake are consistent with earlier studies, our focal depth is significantly shallower suggesting a higher seismic hazard to the populous Taipei metropolitan area than previously supposed.

Additional Information

© The Authors 2014. Published by Oxford University Press on behalf of The Royal Astronomical Society. Accepted 2014 May 28; Received 2014 May 27; in original form 2014 February 18. The authors wish to acknowledge Dr. Fred Klein and Dr. Jim Savage for helpful reviews of our original manuscript draft. We thank Prof. Duncan Agnew, the editor, Dr. Anthony Lomax, the reviewer, and one anonymous reviewer for their careful and detailed reviews of our submitted manuscript. The comments and suggestions we received greatly improved our manuscript.

Attached Files

Published - Geophys._J._Int.-2014-Minson-1419-30.pdf

Supplemental Material - Supporting_Information.zip

Files

Supporting_Information.zip
Files (2.3 MB)
Name Size Download all
md5:6e1e732315fc835fad2db347a32bbdc6
79.6 kB Preview Download
md5:ebdb8178f41147c0a693fb38347b8f6a
2.2 MB Preview Download

Additional details

Created:
August 22, 2023
Modified:
October 17, 2023