Too-Late Warnings by Estimating Mw: Earthquake Early Warning in the Near-Fault Region

2020 ◽  
Vol 110 (3) ◽  
pp. 1276-1288
Author(s):  
Mitsuyuki Hoshiba
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Strong Motion ◽  
Earthquake Early Warning ◽  
Motion Generation ◽  
Near Fault ◽  
Time Gap ◽  
Fault Region ◽  
Strong Ground

ABSTRACT Earthquake early warning (EEW) systems aim to provide advance warnings of impending strong ground shaking. Many EEW systems are based on a strategy in which precise and rapid estimates of source parameters, such as hypocentral location and moment magnitude (Mw), are used in a ground-motion prediction equation (GMPE) to predict the strength of ground motion. For large earthquakes with long rupture duration, the process is repeated, and the prediction is updated in accordance with the growth of Mw during the ongoing rupture. However, in some regions near the causative fault this approach leads to late warnings, because strong ground motions often occur during earthquake ruptures before Mw can be confirmed. Mw increases monotonically with elapsed time and reaches its maximum at the end of rupture, and ground motion predicted by a GMPE similarly reaches its maximum at the end of rupture, but actual generation of strong motion is earlier than the end of rupture. A time gap between maximum Mw and strong-motion generation is the first factor contributing to late warnings. Because this time gap exists at any point of time during the rupture, a late warning is inherently caused even when the growth of Mw can be monitored in real time. In the near-fault region, a weak subevent can be the main contributor to strong ground motion at a site if the distance from the subevent to the site is small. A contribution from a weaker but nearby subevent early in the rupture is the second factor contributing to late warnings. Thus, an EEW strategy based on rapid estimation of Mw is not suitable for near-fault regions where strong shaking is usually recorded. Real-time monitoring of ground motion provides direct information for real-time prediction for these near-fault locations.

Strong Ground Motion and Pulse‐Like Velocity Observations in the Near‐Fault Region of the 2018 Mw 6.4 Hualien, Taiwan, Earthquake

2018 ◽  
Vol 90 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Chun‐Hsiang Kuo ◽  
Jyun‐Yan Huang ◽  
Che‐Min Lin ◽  
Ting‐Yu Hsu ◽  
Shu‐Hsien Chao ◽  
...  
Keyword(s):  
Ground Motion ◽  
Strong Ground Motion ◽  
Near Fault ◽  
Fault Region ◽  
Taiwan Earthquake ◽  
Strong Ground

End-to-end PGA estimation for earthquake early warning using transformer networks

2020 ◽  
Author(s):  
Jannes Münchmeyer ◽  
Dino Bindi ◽  
Ulf Leser ◽  
Frederik Tilmann
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Lead Times ◽  
Ground Shaking ◽  
Target Sites ◽  
End To End

<p>The key task of earthquake early warning is to provide timely and accurate estimates of the ground shaking at target sites. Current approaches use either source or propagation based methods. Source based methods calculate fast estimates of the earthquake source parameters and apply ground motion prediction equations to estimate shaking. They suffer from saturation effects for large events, simplified assumptions and the need for a well known hypocentral location, which usually requires arrivals at multiple stations. Propagation based methods estimate levels of shaking from the shaking at neighboring stations and therefore have short warning times and possibly large blind zones. Both methods only use specific features from the waveform. In contrast, we present a multi-station neural network method to estimate horizontal peak ground acceleration (PGA) anywhere in the target region directly from raw accelerometer waveforms in real time.</p><p>The three main components of our model are a convolutional neural network (CNN) for extracting features from the single-station three-component accelerograms, a transformer network for combining features from multiple stations and for transferring them to the target site features and a mixture density network to generate probabilistic PGA estimates. By using a transformer network, our model is able to handle a varying set and number of stations as well as target sites. We train our model end-to-end using recorded waveforms and PGAs. We use data augmentation to enable the model to provide estimations at targets without waveform recordings. Starting with the arrival of a P wave at any station of the network, our model issues real-time predictions at each new sample. The predictions are Gaussian mixtures, giving estimates of both expected value and uncertainties. The model can be used to predict PGA at specific target sites, as well as to generate ground motion maps.</p><p>We analyze the model on two strong motion data sets from Japan and Italy in terms of standard deviation and lead times. Through the probabilistic predictions we are able to give lead times for different levels of uncertainty and ground shaking. This allows to control the ratio of missed detections to false alerts. Preliminary analysis suggest that for levels between 1%g and 10%g our model achieves multi-second lead times even for the closest stations at a false-positive rate below 25%. For an example event at 50 km depth, lead times at the closest stations with epicentral distances below 20 km are 6 s and 7.5 s. This suggests that our model is able to effectively use the difference between P and S travel time and accurately assess the future level of ground shaking from the first parts of the P wave. It additionally makes effective use of the information contained in the absence of signal at other stations.</p>

scholarly journals Real-Time Characterization of Finite Rupture and Its Implication for Earthquake Early Warning: Application of FinDer to Existing and Planned Stations in Southwest China

2021 ◽  
Vol 9 ◽  
Author(s):  
Jiawei Li ◽  
Maren Böse ◽  
Yu Feng ◽  
Chen Yang
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Line Source ◽  
Average Distance ◽  
Strong Motion ◽  
Earthquake Early Warning ◽  
Time Data ◽  
Yunnan Region ◽  
Motion Time ◽  
Source Models

Earthquake early warning (EEW) not only improves resilience against the risk of earthquake disasters, but also provides new insights into seismological processes. The Finite-Fault Rupture Detector (FinDer) is an efficient algorithm to retrieve line-source models of an ongoing earthquake from seismic real-time data. In this study, we test the performance of FinDer in the Sichuan-Yunnan region (98.5oE–106.0oE, 22.0oN–34.0oN) of China for two datasets: the first consists of seismic broadband and strong-motion records of 58 earthquakes with 5.0 ≤ MS ≤ 8.0; the second comprises additional waveform simulations at sites where new stations will be deployed in the near future. We utilize observed waveforms to optimize the simulation approach to generate ground-motion time series. For both datasets the resulting FinDer line-source models agree well with the reported epicenters, focal mechanisms, and finite-source models, while they are computed faster compared to what traditional methods can achieve. Based on these outputs, we determine a theoretical relation that can predict for which magnitudes and station densities FinDer is expected to trigger, assuming that at least three neighboring stations must have recorded accelerations of 4.6 cm/s2 or more. We find that FinDer likely triggers and sends out a report, if the average distance between the epicenter and the three closest stations, Depi, is equal or smaller than log10 (Ma + b) + c, where a = 1.91, b = 5.93, and c = 2.34 for M = MW ≥ 4.8, and c = 2.49 for M = MS ≥ 5.0, respectively. If the data used in this study had been available in real-time, 40–70% of sites experiencing seismic intensities of V-VIII (on both Chinese and MMI scales) and 20% experiencing IX-X could have been issued a warning 5–10 s before the S-wave arrives. Our offline tests provide a useful reference for the planned installation of FinDer in the nationwide EEW system of Chinese mainland.

scholarly journals One minute after: Strong-motion map, effective epicenter, and effective magnitude

1997 ◽  
Vol 87 (5) ◽  
pp. 1209-1219 ◽  
Author(s):  
Ta-liang Teng ◽  
Ludan Wu ◽  
Tzay-Chyn Shin ◽  
Yi-Ben Tsai ◽  
William H. K. Lee
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Initial Point ◽  
Strong Motion ◽  
Large Earthquake ◽  
Seismic Monitoring ◽  
Earthquake Early Warning ◽  
Force Balance ◽  
Total Energy Release ◽  
Intensity Map

Abstract This article reports the recent progress on real-time seismic monitoring in Taiwan, particularly the real-time strong-motion monitoring by the Taiwan Central Weather Bureau's telemetered seismic network (CWBSN), which is presently aiming at rapid reporting immediately after a large earthquake occurrence. If rapid reporting can be achieved before the arrival of the strong shaking, earthquake early warning will become possible. CWBSN has achieved the generation of the intensity map, epicenter, and magnitude within 1 min of the occurrence of a large earthquake. Both rapid reporting and early warning are principally applied to large (M ≫ 5) events; the requirement of on-scale waveform recording prompted CWBSN in 1995 to integrate strong-motion sensors (e.g., force-balance accelerometers) into its telemetered seismic monitoring system. Time-domain recursive processing is applied to the multi-channel incoming seismic signals by a group of networked personal computers to generate the intensity map. From the isoseismal contours, an effective epicenter is immediately identified that resides in the middle of the largest (usually the 100-gal) contour curve of the intensity map. An effective magnitude is also defined that can be derived immediately from the surface area covered by the largest (usually the 100-gal) contour curve. For a large event with a finite rupture surface, the epicenter and magnitude so derived are more adequate estimates of the source location and of the strength of destruction. The effective epicenter gives the center of the damage area; it stands in contrast with the conventional epicenter location, which only gives the initial point of rupture nucleation. The effective magnitude reflects more closely the earthquake damage potential, instead of the classical magnitude definition that emphasizes the total energy release. The CWBSN has achieved in obtaining the above crucial source information well within 1 min. This time can further be reduced to better than 30 sec, as illustrated by the example in this article, showing that earthquake early warning is indeed an achievable goal. The rapid reporting and early warning information is electronically transmitted to users to allow rapid response actions, with or without further human intervention.

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.

Could a Decentralized Onsite Earthquake Early Warning System Help in Mitigating Seismic Risk in Northeastern Italy? The Case of the 1976 Ms 6.5 Friuli Earthquake

2020 ◽  
Vol 91 (6) ◽  
pp. 3323-3333
Author(s):  
Stefano Parolai ◽  
Luca Moratto ◽  
Michele Bertoni ◽  
Chiara Scaini ◽  
Alessandro Rebez
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Seismic Risk ◽  
Warning System ◽  
Strong Motion ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
Industrial Facilities ◽  
Earthquake Early Warning System ◽  
The Impact

Abstract In May 1976, a devastating earthquake of magnitude Ms 6.5 occurred in Friuli, Italy, resulting in 976 deaths, 2000 injured, and 60,000 homeless. It is notable that, at the time of the earthquake, only one station was installed in the affected region. The resulting lack of information, combined with a dearth of mitigation planning for responding to such events, lead to a clear picture of the impact of the disaster being available only after a few days. This region is now covered by nearly 100 seismological and strong-motion stations operating in real time. Furthermore, 30 average-cost strong-motion stations have been recently added, with the goals of improving the density of real-time ground-motion observations and measuring the level of shaking recorded at selected buildings. The final goal is to allow rapid impact estimations to be made to improve the response of civil protection authorities. Today, considering the higher density seismological network, new efforts in terms of the implementation and testing of earthquake early warning systems as a possible tool for mitigating seismic risk are certainly worthwhile. In this article, we show the results obtained by analyzing in playback and using an algorithm for decentralized onsite earthquake early warning, broadband synthetic strong-motion data calculated at 18 of the stations installed in the region, while considering the magnitude and location of the 1976 Friuli earthquake. The analysis shows that the anisotropy of the lead times is related not only to the finite nature of the source but also to the slip distribution. A reduction of 10% of injured persons appears to be possible if appropriate mitigating actions are employed, such as the development of efficient automatic procedures that improve the safety of strategic industrial facilities.

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