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Rapid Estimation of Earthquake Source and Ground‐Motion Parameters for Earthquake Early Warning Using Data from a Single Three‐Component Broadband or Strong‐Motion Sensor

Böse, M. and Heaton, T. and Hauksson, E. (2012) Rapid Estimation of Earthquake Source and Ground‐Motion Parameters for Earthquake Early Warning Using Data from a Single Three‐Component Broadband or Strong‐Motion Sensor. Bulletin of the Seismological Society of America, 102 (2). pp. 738-750. ISSN 0037-1106. https://resolver.caltech.edu/CaltechAUTHORS:20120503-080024631

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Abstract

We propose a new algorithm to rapidly determine earthquake source and ground-motion parameters for earthquake early warning (EEW). This algorithm uses the acceleration, velocity, and displacement waveforms of a single three-component broadband (BB) or strong-motion (SM) sensor to perform real-time earthquake/noise discrimination and near/far source classification. When an earthquake is detected, the algorithm estimates the moment magnitude M, epicentral distance Δ, and peak ground velocity (PGV) at the site of observation. The algorithm was constructed by using an artificial neural network (ANN) approach. Our training and test datasets consist of 2431 three-component SM and BB records of 161 crustal earthquakes in California, Japan, and Taiwan with 3.1 ≤ M ≤ 7.6 at Δ ≤ 115 km. First estimates become available at t_0 = 0.25 s after the P pick and are regularly updated. We find that displacement and velocity waveforms are most relevant for the estimation of M and PGV, while acceleration is important for earthquake/noise discrimination. Including site corrections reduces the errors up to 10%. The estimates improve by an additional 10% if we use both the vertical and horizontal components of recorded ground motions. The uncertainties of the predicted parameters decrease with increasing time window length t_0; larger magnitude events show a slower decay of these uncertainties than small earthquakes. We compare our approach with the τ_c algorithm and find that our prediction errors are around 60% smaller. However, in general there is a limitation to the prediction accuracy an EEW system can provide if based on single-sensor observations.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1785/0120110152 DOIArticle
http://www.bssaonline.org/content/102/2/738PublisherArticle
ORCID:
AuthorORCID
Heaton, T.0000-0003-3363-2197
Hauksson, E.0000-0002-6834-5051
Additional Information:© 2012 Seismological Society of America. Manuscript received 18 May 2011. This work is funded through contract G09AC00258 from USGS/ANSS to the California Institute of Technology (Caltech). This is contribution #10058 of the Seismological Laboratory, Geological and Planetary Sciences at Caltech. We would like to thank William H. Bakun and an anonymous reviewer for their helpful comments.
Group:Seismological Laboratory
Funders:
Funding AgencyGrant Number
USGSG09AC00258
Other Numbering System:
Other Numbering System NameOther Numbering System ID
Caltech Seismological Laboratory10058
Issue or Number:2
Record Number:CaltechAUTHORS:20120503-080024631
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20120503-080024631
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:31283
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 May 2012 22:49
Last Modified:03 Oct 2019 03:50

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