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• W4382751498 abstract "Abstract Earthquake surface-fault rupture location uncertainty is a key factor in fault displacement hazard analysis and informs hazard and risk mitigation strategies. Geologists often predict future rupture locations from fault mapping based on the geomorphology interpreted from remote-sensing data sets. However, surface processes can obscure fault location, fault traces may be mapped in error, and a future rupture may not break every fault trace. We assessed how well geomorphology-based fault mapping predicted surface ruptures for seven earthquakes: 1983 M 6.9 Borah Peak, 2004 M 6.0 Parkfield, 2010 M 7.2 El Mayor–Cucapah, 2011 M 6.7 Fukushima-Hamadori, 2014 M 6.0 South Napa, 2016 M 7.8 Kaikoura, and 2016 M 7 Kumamoto. We trained geoscience students to produce active fault maps using topography and imagery acquired before the earthquakes. A geologic professional completed a “control” map. Mappers used a new “geomorphic indicator ranking” approach to rank fault confidence based on geomorphologic landforms. We determined the accuracy of the mapped faults by comparing the fault maps to published rupture maps. We defined predicted ruptures as ruptures near a fault (50–200 m, depending on the fault confidence) that interacted with the landscape in a similar way to the fault. The mapped faults predicted between 12% to 68% of the principal rupture length for the studied earthquakes. The median separation distances between predicted ruptures and strong, distinct, or weak faults were 15–30 m. Our work highlights that mapping future fault ruptures is an underappreciated challenge of fault displacement hazard analysis—even for experts—with implications for risk management, engineering site assessments, and fault exclusion zones." @default.
• W4382751498 created "2023-07-01" @default.
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• W4382751498 date "2023-07-20" @default.
• W4382751498 modified "2023-10-15" @default.
• W4382751498 title "Evaluating how well active fault mapping predicts earthquake surface-rupture locations" @default.
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• W4382751498 doi "https://doi.org/10.1130/ges02611.1" @default.
• W4382751498 hasPublicationYear "2023" @default.
• W4382751498 type Work @default.

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