Generic Source Parameter Determination and Ground-Motion Prediction for Earthquake Early Warning

2020 ◽  
Vol 110 (1) ◽  
pp. 345-356 ◽  
Author(s):  
Itzhak Lior ◽  
Alon Ziv
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Stress Drop ◽  
Seismic Moment ◽  
Source Parameter ◽  
Parameter Determination ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Empirical Relations

ABSTRACT Currently available earthquake early warning systems employ region-specific empirical relations for magnitude determination and ground-motion prediction. Consequently, the setting up of such systems requires lengthy calibration and parameter tuning. This situation is most problematic in low seismicity and/or poorly instrumented regions, where the data available for inferring those empirical relations are scarce. To address this issue, a generic approach for real-time magnitude, stress drop, and ground-motion prediction is introduced that is based on the omega-squared model. This approach leads to the following approximate expressions for seismic moment: M0∝RT0.5Drms1.5/Vrms0.5, and stress drop: Δτ∝RT0.5Arms3/Vrms2, in which R is the hypocentral distance; T is the data interval; and Drms, Vrms, and Arms are the displacement, velocity, and acceleration root mean squares, respectively, which may be calculated in the time domain. The potential of these relations for early warning applications is demonstrated using a large composite data set that includes the two 2019 Ridgecrest earthquakes. A quality parameter is introduced that identifies inconsistent earthquake magnitude and stress-drop estimates. Once initial estimates of the seismic moment and stress drop become available, the peak ground velocity and acceleration may be estimated in real time using the generic ground-motion prediction equation of Lior and Ziv (2018). The use of stress drop for ground-motion prediction is shown to be critical for strong ground accelerations. The main advantages of the generic approach with respect to the empirical approach are that it is readily implementable in any seismic region, allows for the easy update of magnitude, stress drop, and shaking intensity with time, and uses source parameter determination and peak ground motion predictions that are subject to the same model assumptions, thus constituting a self-consistent early warning method.

Generic Source Parameter Determination for Earthquake Early Warning: Theory, Observations and Implications for the Mw 7.1 Ridgecrest Earthquake

2020 ◽  
Author(s):  
alon ziv ◽  
Itzhak Lior
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Stress Drop ◽  
Epicentral Distance ◽  
Strong Motion ◽  
Parameter Determination ◽  
Real Time Processing ◽  
Tectonic Environment ◽  
The Time Domain

<p>A generic approach for real-time magnitude and stress drop is introduced that is based on the omega-squared model (Brune, 1970) and results from Lior and Ziv (2018). This approach leads to approximate expressions for earthquake magnitude and stress drop as functions of epicentral distance and ground motion root-mean-squares (rms). Because the rms of the ground motion (acceleration, velocity and displacement) may be calculated directly from the seismogram in the time domain, the use of this approach for automated real-time processing is rather straightforward. Once the seismic moment and stress drop are known, they may be plugged in the ground motion prediction equations (GMPE) of Lior and Ziv (2018) to map the predicted peak shaking.</p><p>This method is generic in the sense that it is readily implementable in any tectonic environment, without having to go through a calibration phase. The potential of these results for automated early warning applications is demonstrated using a large dataset of about 6000 seismograms recorded by strong-motion and broadband velocity sensors from different tectonic environments. Optimal real-time performance is achieved by integrating magnitude and stress drop estimates into an evolutionary algorithm. The result of such an evolutionary calculation for the Mw 7.1 Ridgecrest earthquake indicates close agreement with the true magnitude.</p>

scholarly journals Developments of the Nationwide Earthquake Early Warning System in Japan After the 2011 Mw9.0 Tohoku-Oki Earthquake

2021 ◽  
Vol 9 ◽  
Author(s):  
Yuki Kodera ◽  
Naoki Hayashimoto ◽  
Koji Tamaribuchi ◽  
Keishi Noguchi ◽  
Ken Moriwaki ◽  
...  
Keyword(s):  
Point Source ◽  
Ground Motion ◽  
Early Warning ◽  
Prediction Accuracy ◽  
Ground Motions ◽  
Earthquake Early Warning ◽  
Japan Meteorological Agency ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Strong Ground

In Japan, the nationwide earthquake early warning (EEW) system has been being operated by the Japan Meteorological Agency (JMA) since 2007, disseminating information on imminent strong ground motion to the general public and advanced technical users. In the beginning of the operation, the system ran based mainly on standard source-based algorithms with a point-source location estimate and ground motion prediction equation. The point-source algorithms successfully provided ground motion predictions with high accuracy during the initial operation; however, the 2011 Mw9.0 Tohoku-Oki earthquake and the subsequent intense aftershock and triggered earthquake activities underscored the weaknesses of the source-based approach. In this paper, we summarize major system developments after the Tohoku-Oki event to overcome the limits of the standard point-source algorithms and to enhance the EEW performance further. In addition, we evaluate how the system performance was influenced by the updates. One of significant improvements in the JMA EEW system was the implementation of two new ground motion prediction methods: the integrated particle filter (IPF) and propagation of local undamped motion (PLUM) algorithms. IPF is a robust point-source algorithm based on the Bayesian inference, and PLUM is a wavefield-based algorithm that predicts ground motions directly from observed shakings. Another notable update was the incorporation of new observation facilities including S-net, a large-scale ocean bottom seismometer network deployed along the Japan and Kuril trenches. The prediction accuracy and warning issuance performance analysis for the updated JMA EEW system showed that IPF improved the source-based ground motion prediction accuracy and reduced the risk of issuing overpredicted warnings. PLUM made the system less likely to underpredict strong ground motions and improved the warning issuance timeliness. The detection time analysis for the S-net incorporation suggested that S-net enabled the system to issue the first EEW report earlier than before the S-net incorporation for earthquakes around the Japan and Kuril trenches. Those findings indicate that the JMA EEW system has made substantial progress both on software and hardware aspects over the 10 years after the Tohoku-Oki earthquake.

scholarly journals Earthquake Early Warning System in Israel—Towards an Operational Stage

2021 ◽  
Vol 9 ◽  
Author(s):  
Ran N. Nof ◽  
Itzhak Lior ◽  
Ittai Kurzon
Keyword(s):  
Ground Motion ◽  
Early Warning ◽  
Seismic Network ◽  
Earthquake Early Warning ◽  
Performance Estimation ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Origin Time

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.

scholarly journals Ground motion prediction maps using seismic microzonation data and machine learning

2021 ◽  
Author(s):  
Federico Mori ◽  
Amerigo Mendicelli ◽  
Gaetano Falcone ◽  
Gianluca Acunzo ◽  
Rose Line Spacagna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Ground Motion ◽  
Scale Effects ◽  
Earthquake Ground Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Intensity Measures ◽  
Local Site ◽  
Ground Motion Variability

Abstract. Past seismic events worldwide demonstrated that damage and death toll depend on both the strong ground motion (i.e., source effects) and the local site effects. The variability of earthquake ground motion distribution is caused by local stratigraphic and/or topographic setting and buried morphologies, that can give rise to amplification and resonances with respect to the ground motion expected at the reference site. Therefore, local site conditions can affect an area with damage related to the full collapse or loss in functionality of facilities, roads, pipelines, and other lifelines. To this concern, the near real time prediction of damage pattern over large areas is a crucial issue to support the rescue and operational interventions. A machine learning approach was adopted to produce ground motion prediction maps considering both stratigraphic and morphological conditions. A set of about 16'000 accelometric data and about 46'000 geological and geophysical data were retrieved from Italian and European databases. The intensity measures of interest were estimated based on 9 input proxies. The adopted machine learning regression model (i.e., Gaussian Process Regression) allows to improve both the precision and the accuracy in the estimation of the intensity measures with respect to the available near real time predictions methods (i.e., Ground Motion Prediction Equation and shaking maps). In addition, maps with a 50 × 50 m resolution were generated providing a ground motion variability in agreement with the results of advanced numerical simulations based on detailed sub-soil models. The variability at short distances (hundreds of meters) was demonstrated to be responsible for 30–40 % of the total variability of the predicted IM maps, making it desirable that seismic hazard maps also consider short-scale effects.

scholarly journals CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning

2014 ◽  
Vol 198 (3) ◽  
pp. 1438-1457 ◽  
Author(s):  
Maren Böse ◽  
Robert W. Graves ◽  
David Gill ◽  
Scott Callaghan ◽  
Philip J. Maechling
Keyword(s):  
Los Angeles ◽  
Ground Motion ◽  
Early Warning ◽  
Prediction Models ◽  
Earthquake Early Warning ◽  
Motion Prediction ◽  
Ground Motion Prediction
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