Rapid Source Parameter Estimations of Southern California Earthquakes Using PreSEIS

Köhler, Nina; Georgia, B.; Wenzel, Friedemann; Böse, Maren

doi:10.1785/gssrl.80.5.748

Cited by 15 publications

(9 citation statements)

References 27 publications

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“…SAFER in close co-operation with the EU-project Network of Research Infrastructures for European Seismology (NERIES) has now implemented this technology in the test cities Istanbul, Bucharest, Naples and Cairo, and by this and after having carefully applied appropriate regional calibrations, has considerably improved the seismic early warning capability in these metropolitan areas Köhler et al 2009). SAFER in close co-operation with the EU-project Network of Research Infrastructures for European Seismology (NERIES) has now implemented this technology in the test cities Istanbul, Bucharest, Naples and Cairo, and by this and after having carefully applied appropriate regional calibrations, has considerably improved the seismic early warning capability in these metropolitan areas Köhler et al 2009).…”

Section: (B) the Real-time "Shake Map" Technology Is Now Implemented mentioning

confidence: 99%

Early Warning for Geological Disasters

Wenzel¹,

Zschau²

2014

Advanced Technologies in Earth Sciences

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Section: (B) the Real-time "Shake Map" Technology Is Now Implemented mentioning

confidence: 99%

Early Warning for Geological Disasters

Wenzel¹,

Zschau²

2014

Advanced Technologies in Earth Sciences

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“…PreSEIS is a neural-network based approach to early warning using P-wave arrival times and ground motions at one or more stations to locate earthquake hypocenters, estimate the magnitude and expected ground motions, and predict the final expansion of the evolving seismic rupture for large magnitude earthquakes . In order to train and test the neural networks, the PreSEIS approach has been applied to the station distributions in Istanbul and southern California using synthetic seismograms for finite-fault scenarios and envelope functions of real earthquakes (Köhler et al 2009, this issue), respectively.…”

Section: Issue)mentioning

confidence: 99%

The Status of Earthquake Early Warning around the World: An Introductory Overview

Allen¹,

Gasparini²,

Kamigaichi³

et al. 2009

Seismological Research Letters

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319

172

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“…PreSEIS was tested in several seismic-active regions around the world, including Istanbul , southern California (Köhler et al, 2009), Japan (Köhler, 2010), and Germany (Hilbring et al, 2010). The datasets used included (1) stochastic simulated strong-motion (SM) records, (2) sets of purely observed SM records, and (3) joint datasets of observed and simulated SM and broadband (BB) records.…”

Section: Introductionmentioning

confidence: 99%

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

Böse¹,

Heaton²,

Hauksson³

2012

Bulletin of the Seismological Society of America

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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.

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Rapid Source Parameter Estimations of Southern California Earthquakes Using PreSEIS

Cited by 15 publications

References 27 publications

Early Warning for Geological Disasters

Early Warning for Geological Disasters

The Status of Earthquake Early Warning around the World: An Introductory Overview

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

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