PERFORMANCE EVALUATION OF GROUND MOTION PREDICTION EQUATIONS FOR ABSOLUTE VELOCITY RESPONSE SPECTRA (1-10 S) IN JAPAN FOR AN EARTHQUAKE EARLY WARNING

The Geological Survey of Israel has upgraded and expanded the national Israeli Seismic Network (ISN), with more than 110 stations country-wide, as part of the implementation of a governmental decision to build a national Earthquake Early Warning (EEW) system named TRUAA. This upgraded seismic network exhibits a high station density and fast telemetry. The stations are distributed mainly along the main fault systems, the Dead Sea Transform, and the Carmel-Zfira Fault, which may potentially produce Mw 7.5 earthquakes. The system has recently entered a limited operational phase, allowing for initial performance estimation. Real-time performance during eight months of operation (41 earthquakes) matches expectations. Alert delays (interval between origin-time and Earthquake Early Warning alert time) are reduced to as low as 3 s, and source parameter errorstatistics are within expected values found in previous works using historical data playbacks. An evolutionary alert policy is implemented based on a magnitude threshold of Mw 4.2 and peak ground accelerations exceeding 2 cm/s2. A comparison between different ground motion prediction equations (GMPE) is presented for earthquakes from Israel and California using median ground motion prediction equations values. This analysis shows that a theoretical GMPE produced the best agreement with observed ground motions, with less bias and lower uncertainties. The performance of this GMPE was found to improve when an earthquake specific stress drop is implemented.

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Ground‐Motion Prediction Equations for Arias Intensity, Cumulative Absolute Velocity, and Peak Incremental Ground Velocity for Rock Sites in Different Tectonic Environments

Bulletin of the Seismological Society of America ◽

10.1785/0120160388 ◽

2017 ◽

Vol 107 (5) ◽

pp. 2293-2309 ◽

Cited By ~ 15

Author(s):

Zach Bullock ◽

Shideh Dashti ◽

Abbie Liel ◽

Keith Porter ◽

Zana Karimi ◽

...

Keyword(s):

Ground Motion ◽

Absolute Velocity ◽

Motion Prediction ◽

Ground Motion Prediction Equations ◽

Prediction Equations ◽

Ground Motion Prediction ◽

Arias Intensity ◽

Cumulative Absolute Velocity ◽

Tectonic Environments

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A suite of ground motion prediction equations for cumulative absolute velocity in shallow crustal earthquakes including epistemic uncertainty

Earthquake Spectra ◽

10.1177/8755293020957342 ◽

2020 ◽

pp. 875529302095734

Author(s):

Zach Bullock ◽

Abbie B Liel ◽

Shideh Dashti ◽

Keith A. Porter

Keyword(s):

Ground Motion ◽

Epistemic Uncertainty ◽

Absolute Velocity ◽

Motion Prediction ◽

Ground Motion Prediction Equations ◽

Prediction Equations ◽

Ground Motion Prediction ◽

Crustal Earthquakes ◽

Cumulative Absolute Velocity ◽

Earthquake Characteristics

Recent research has highlighted the usefulness of cumulative absolute velocity [Formula: see text] in several contexts, including using the [Formula: see text] at the ground surface for earthquake early warning and using the [Formula: see text] at rock reference conditions for evaluation of the liquefaction risk facing structures. However, there are relatively few ground motion prediction equations for CAV, they are based on relatively small data sets, and they give relatively similar results. This study develops nine ground motion prediction equations for [Formula: see text] based on a global database of ground motion records from shallow crustal earthquakes. Its provision of nine models enables characterization of epistemic uncertainty for ranges of earthquake characteristics that are sparsely populated in the regression database. The functional forms provide different perspectives on extrapolation to important ranges of earthquake characteristics, particularly large magnitude events and short distances. The variability and epistemic uncertainty in the models are characterized. Spatial autocorrelation of the models’ errors is investigated. The models’ predictions agree with existing broadly applicable models at small to moderate magnitudes and moderate to long distances. These models can be used to improve hazard analysis of [Formula: see text] that incorporates the influence of epistemic uncertainty.

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