Abstract

Abstract Two earthquake sequences occurred a year apart at the Mendocino Triple Junction in northern California: first the 20 December 2021 Mw 6.1 and 6.0 Petrolia sequence, then the 20 December 2022 Mw 6.4 Ferndale sequence. To delineate active faults and understand the relationship between these sequences, we applied an automated deep-learning workflow to create enhanced and relocated earthquake catalogs for both the sequences. The enhanced catalog newly identified more than 14,000 M 0–2 earthquakes and also found 852 of 860 already cataloged events. We found that deep-learning and template-matching approaches complement each other to improve catalog completeness because deep learning finds more M 0–2 background seismicity, whereas template-matching finds the smallest M < 0 events near already known events. The enhanced catalog revealed that the 2021 Petrolia and 2022 Ferndale sequences were distinct in space and time, but adjacent in space. Though both the sequences happened in the downgoing Gorda slab, the shallower Ferndale sequence ruptured within the uppermost slab near the subduction interface, while the onshore Petrolia sequence occurred deeper in the mantle. Deep-learning-enhanced earthquake catalogs could help monitor evolving earthquake sequences, identify detailed seismogenic fault structures, and understand space–time variations in earthquake rupture and sequence behavior in a complex tectonic setting.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call