In an effort to inform the Senior Seismic Hazard Analysis Committee (SSHAC) Level 3 project for the Idaho National Laboratory, we provide an improved aftershock catalog related to the March 31, 2020, Mw6.5 Stanley, Idaho earthquake from picks related to a temporary network of two real-time and 15 non-telemetered seismometers within the epicentral area. From the permanent and temporary (XP) real-time network, the USGS cataloged 1,946 aftershocks between April 1, 2020 and October 31, 2020. To improve aftershock location and magnitudes, we manually picked arrival times of P and S waves from off-line stations in the XP temporary network, generated a new crustal velocity model, and independently relocated each event using the HypoDD double-difference earthquake algorithm. We created our new velocity model from existing broadband and active source seismic campaign data that were acquired near the epicentral region prior to the 2020 earthquake. We compare arrival time differences, epicentral locations and depths between aftershocks recorded with the two catalogs. We find the addition of local stations provides tighter aftershock clustering that suggests an improved aftershock locations. To detect lower magnitude events, we employed deep learning. Our method solves common problems associated with detecting many events that have a low signal-to-noise ratio. From the machine learning database, we detected more than 74,000 aftershocks. Based on the number of identified earthquakes and Gutenberg-Richter relationships derived from the USGS catalog, we estimate that we have reduced the completion magnitude for the Stanley earthquake sequence to below M1 using this machine learning approach. We located each aftershock with our new velocity model. Our new velocity model and picks suggests aftershocks occurred mostly at shallower depths than assessed in the USGS catalog. These aftershocks align along two linear trends that suggest the activation of two unnamed primary faults.
I explore spatial and temporal aftershock patterns related to three instrumentally recorded earthquakes in Idaho -- the Sulphur Peak, the Challis, and the Stanley earthquakes. These three M > 5 earthquakes border the eastern Snake River Plain and lie within the Intermountain Seismic Belt and Centennial Tectonic Belt. Using machine learning for event detection and phase picking from local and regional seismic networks, I generate new aftershock catalogs. I locate more aftershocks than in the USGS catalog due to lower signal-to-noise detections. Using my phase picks, I locate aftershocks using a range of velocity models and select a catalog that represents the smallest residuals in hypocenter locations. I compare my results with handpicked phases and previously published velocity models. My 2014-2017 Challis catalog is consistent with the work of Pang et al. (2018), with more high-quality events with similar average vertical error. My one-month aftershock catalog for the 2017 Sulphur Peak earthquake is spatially consistent with the results of Koper et al. (2018); however, I show that my machine-learning approach produced relatively few aftershocks because afterslip events were not matched using a coseismic training dataset. Finally, I locate a factor of five more aftershocks from the 2020 Stanley earthquake when compared to the USGS catalog. I relocate the mainshock using biases computed by differencing my aftershock epicenters with the same aftershocks in the USGS catalog. The revised mainshock location now lies within a large and pronounced aftershock zone. My catalog suggests no motion along the active Sawtooth Fault, but instead I map a new N10W trending fault that accommodated the mainshock and much of the aftershock slip. I conclude that aftershock catalogs derived from a machine-learning approach can enhance seismic detection and aid in determining the driving mechanisms responsible for a coseismically driven earthquakes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.