Fingerprinting subduction margins using principal component analysis profiles: A data science approach to assessing earthquake hazard
File(s)
Accepted version
OA Location
Author(s)
Locher, Valerie A
Bell, Rebecca E
Salah, Parastoo
Platt, Robert
John, Cédric M
Type
Journal Article
Abstract
Giant earthquakes (Mw ≥8.5) along subduction margins pose great hazards to coastal societies. While it is generally accepted that geological margin properties play a role, controls on giant earthquake occurrence remain undetermined. The long intermittence times of giant earthquakes and the comparatively short earthquake record obscure any correlations between margin properties and seismicity. This work presents a new approach to relating margin properties to seismicity. We apply principal component analysis (PCA) to four margin properties to “fingerprint” margins by assigning them a PCA profile, which we compare to giant earthquake occurrence. This approach reduces bias from the short earthquake record because seismicity is not used as a PCA input feature. Using kernel PCA, a nonlinear PCA variant, we find nonlinear patterns in margin properties and suggest that links between properties and seismicity are nonlinear, which helps explain why they have been hard to establish. PCA clusters identify “active and moderate” and “quiet and extreme” margins. We argue that margin segments with “quiet and extreme” PCA profiles but no giant earthquakes since 1900 are as hazardous as those that have ruptured in giant earthquakes recently.
Date Issued
2025-05-01
Date Acceptance
2025-02-25
Citation
Geology, 2025, 53 (5), pp.473-478
ISSN
0091-7613
Publisher
Geological Society of America
Start Page
473
End Page
478
Journal / Book Title
Geology
Volume
53
Issue
5
Copyright Statement
© 2025 Geological Society of America. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published
Date Publish Online
2025-03-12