Recent Earthquake Early Warning Research and Developments at the Southern California Seismic Network
Description:
We highlight recent earthquake early warning (EEW) algorithm developments conducted by researchers at the Caltech Southern California Seismic Network (SCSN), with specific application to the ShakeAlert EEW system for the West Coast of the United States (US). ShakeAlert is a cooperative project between the US Geological Survey and partner institutions, including Caltech. Alongside providing seismic data streams to ShakeAlert using the infrastructure of the SCSN, the Caltech SCSN helps operate and develop several of the component algorithms in ShakeAlert, particularly the Finite-Fault Rupture Detector (FinDer) and the Earthquake Information to Ground Motion (eqInfo2GM) algorithms. The FinDer algorithm estimates earthquake source parameters by matching observed shaking to pre-computed templates. Ground-motion-based assessments of FinDer alert performance will help determine whether updating the ground-motion models in the templates will improve alert accuracy. The eqInfo2GM algorithm calculates median-expected shaking distributions used by ShakeAlert to determine alert regions. Proposed refinements to the ground-motion modeling procedures in eqInfo2GM, including adjustments to the ground-motion models and the representative site-effect grid, reconcile ground-motion prediction differences between the ShakeAlert grid and contour alert products as well as improve prediction accuracy.
We also discuss additional developments in EEW approaches that may be considered for future incorporation into ShakeAlert, including the ground-motion-based Propagation of Local Undamped Motion (PLUM) algorithm and Distributed Acoustic Sensing (DAS) applications. Recent PLUM developments include the addition of attenuation into the forward-prediction procedure, which brings PLUM predictions closer to the median-expected ground-motion predictions currently used in ShakeAlert. DAS-related EEW efforts focus on two areas: augmenting seismic data streams with DAS-derived data; and developing DAS-specific EEW algorithms that utilize machine learning and strain-based scaling relationships to rapidly detect and characterize earthquakes.
Session: End-to-End Advancements in Earthquake Early Warning Systems [Poster Session]
Type: Poster
Date: 5/3/2024
Presentation Time: 08:00 AM (local time)
Presenting Author: Jessie
Student Presenter: No
Invited Presentation:
Authors
Jessie Saunders Presenting Author Corresponding Author jsaunder@caltech.edu California Institute of Technology |
Ettore Biondi ebiondi@caltech.edu California Institute of Technology |
Maren Boese maren.boese@sed.ethz.ch ETH Zurich |
Julian Bunn julian.bunn@caltech.edu California Institute of Technology |
Elizabeth Cochran ecochran@usgs.gov U.S. Geological Survey |
Claude Felizardo claude@caltech.edu California Institute of Technology |
Andrew Good agood@caltech.edu California Institute of Technology |
Thomas Heaton heatont@caltech.edu California Institute of Technology |
Allen Husker ahusker@caltech.edu California Institute of Technology |
Recent Earthquake Early Warning Research and Developments at the Southern California Seismic Network
Category
End-to-End Advancements in Earthquake Early Warning Systems