scholarly journals Comparing the Performance of Regional Earthquake Early Warning Algorithms in Europe

2021 ◽  
Vol 9 ◽  
Author(s):  
Elisa Zuccolo ◽  
Gemma Cremen ◽  
Carmine Galasso
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Risk Mitigation ◽  
Source Parameters ◽  
Warning System ◽  
Optimal Choice ◽  
Earthquake Early Warning ◽  
Ground Shaking ◽  
Processing Times

Several earthquake early warning (EEW) algorithms have been developed worldwide for rapidly estimating real-time information (i.e., location, magnitude, ground shaking, and/or potential consequences) about ongoing seismic events. This study quantitatively compares the operational performance of two popular regional EEW algorithms for European conditions of seismicity and network configurations. We specifically test PRobabilistic and Evolutionary early warning SysTem (PRESTo) and the implementation of the Virtual Seismologist magnitude component within SeisComP, VS(SC), which we use jointly with the SeisComP scanloc module for locating events. We first evaluate the timeliness and accuracy of the location and magnitude estimates computed by both algorithms in real-time simulation mode, accounting for the continuous streaming of data and effective processing times. Then, we focus on the alert-triggering (decision-making) phase of EEW and investigate both algorithms’ ability to yield accurate ground-motion predictions at the various temporal instances that provide a range of warning times at target sites. We find that the two algorithms show comparable performances in terms of source parameters. In addition, PRESTo produces better rapid estimates of ground motion (i.e., those that facilitate the largest lead times); therefore, we conclude that PRESTo may have a greater risk-mitigation potential than VS(SC) in general. However, VS(SC) is the optimal choice of EEW algorithm if shorter warning times are permissible. The findings of this study can be used to inform current and future implementations of EEW systems in Europe.

scholarly journals Real-Time Prediction of Impending Ground Shaking: Review of Wavefield-Based (Ground-Motion-Based) Method for Earthquake Early Warning

2021 ◽  
Vol 9 ◽  
Author(s):  
Mitsuyuki Hoshiba
Keyword(s):  
Real Time ◽  
Ground Motion ◽  
Early Warning ◽  
Ground Motions ◽  
Source Parameters ◽  
Earthquake Early Warning ◽  
Peak Ground Velocity ◽  
Ground Shaking ◽  
Time Prediction ◽  
Seismic Waveform

Earthquake early warning (EEW) systems aim to provide advance warning of impending ground shaking, and the technique used for real-time prediction of shaking is a crucial element of EEW systems. Many EEW systems are designed to predict the strength of seismic ground motions (peak ground acceleration, peak ground velocity, or seismic intensity) based on rapidly estimated source parameters (the source-based method), such as hypocentral location, origin time, magnitude, and extent of fault rupture. Recently, however, the wavefield-based (or ground-motion-based) method has been developed to predict future ground motions based directly on the current wavefield, i.e., ground motions monitored in real-time at neighboring sites, skipping the process of estimation of the source parameters. The wavefield-based method works well even for large earthquakes with long duration and huge rupture extents, highly energetic earthquakes that deviate from standard empirical relations, and multiple simultaneous earthquakes, for which the conventional source-based method sometimes performs inadequately. The wavefield-based method also enables prediction of the ongoing seismic waveform itself using the physics of wave propagation, thus providing information on the duration, in addition to the strength of strong ground motion for various frequency bands. In this paper, I review recent developments of the wavefield-based method, from simple applications using relatively sparse observation networks to sophisticated data assimilation techniques exploiting dense networks.

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>

Optimizing Earthquake Early Warning Alert Distance Strategies Using the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, Earthquakes

2020 ◽  
Vol 110 (4) ◽  
pp. 1872-1886 ◽  
Author(s):  
Jessie K. Saunders ◽  
Brad T. Aagaard ◽  
Annemarie S. Baltay ◽  
Sarah E. Minson
Keyword(s):  
Ground Motion ◽  
Early Warning ◽  
Ground Motions ◽  
Protective Action ◽  
Warning System ◽  
Prediction Equation ◽  
Earthquake Early Warning ◽  
Source Characterization ◽  
Earthquake Early Warning System ◽  
Alert Distance

ABSTRACT The ShakeAlert earthquake early warning system aims to alert people who experience modified Mercalli intensity (MMI) IV+ shaking during an earthquake using source estimates (magnitude and location) to estimate median-expected peak ground motions with distance, then using these ground motions to determine median-expected MMI and thus the extent of MMI IV shaking. Because median ground motions are used, even if magnitude and location are correct, there will be people outside the alert region who experience MMI IV shaking but do not receive an alert (missed alerts). We use 91,000 “Did You Feel It?” survey responses to the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, earthquakes to determine which ground-motion to intensity conversion equation (GMICE) best fits median MMI with distance. We then explore how incorporating uncertainty from the ground-motion prediction equation and the GMICE in the alert distance calculation can produce more accurate MMI IV alert regions for a desired alerting strategy (e.g., aiming to alert 95% of people who experience MMI IV+ shaking), assuming accurate source characterization. Without incorporating ground-motion uncertainties, we find MMI IV alert regions using median-expected ground motions alert fewer than 20% of the population that experiences MMI IV+ shaking. In contrast, we find >94% of the people who experience MMI IV+ shaking can be included in the MMI IV alert region when two standard deviations of ground-motion uncertainty are included in the alert distance computation. The optimal alerting strategy depends on the false alert tolerance of the community due to the trade-off between minimizing missed and false alerts. This is especially the case for situations like the Mw 6.4 earthquake when alerting 95% of the 5 million people who experience MMI IV+ also results in alerting 14 million people who experience shaking below this level and do not need to take protective action.

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.

Ground Motion Estimation Using Front Site Wave Form Data Based on RVM for Earthquake Early Warning

2015 ◽  
Vol 10 (4) ◽  
pp. 667-677
Author(s):  
Yincheng Yang ◽  
◽  
Masato Motosaka ◽  
Keyword(s):  
Ground Motion ◽  
Early Warning ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Peak Ground Velocity ◽  
Wave Form ◽  
Source Information ◽  
Ground Acceleration ◽  
P Waves ◽  
Earthquake Early Warning System

The use of the earthquake early warning system (EEWS), one of the most useful emergency response tools, requires that the accuracy of real-time ground motion prediction (GMP) be enhanced. This requires that waveform information at observation points along earthquake wave propagation paths (hereafter, front-site waveform information) be used effectively. To enhance the combined reliability of different systems, such as on-site and local/regional warning, we present a GMP method using front-site waveform information by applying a relevant vector machine (RVM). We present methodology and application examples for a case study estimating peak ground acceleration (PGA) and peak ground velocity (PGV) for earthquakes in the Miyagi-Ken Oki subduction zone. With no knowledge of source information, front site waveforms have been used to predict ground motion at target sites. Five input variables – earthquake PGA, PGD, pulse rise time, average period and theVpmax/Amaxratio – have been used for the first 4 to 6 seconds of P-waves in training a regression model. We found that RVM is a useful tool for the prediction of peak ground motion.

scholarly journals Earthquake Early Warning ShakeAlert 2.0: Public Rollout

2020 ◽  
Vol 91 (3) ◽  
pp. 1763-1775 ◽  
Author(s):  
Monica D. Kohler ◽  
Deborah E. Smith ◽  
Jennifer Andrews ◽  
Angela I. Chung ◽  
Renate Hartog ◽  
...  
Keyword(s):  
United States ◽  
Early Warning ◽  
Performance Metrics ◽  
Warning System ◽  
The United States ◽  
Earthquake Early Warning ◽  
Earthquake Risk ◽  
Waveform Data ◽  
Ground Shaking ◽  
Event Rates

Abstract The ShakeAlert earthquake early warning system is designed to automatically identify and characterize the initiation and rupture evolution of large earthquakes, estimate the intensity of ground shaking that will result, and deliver alerts to people and systems that may experience shaking, prior to the occurrence of shaking at their location. It is configured to issue alerts to locations within the West Coast of the United States. In 2018, ShakeAlert 2.0 went live in a regional public test in the first phase of a general public rollout. The ShakeAlert system is now providing alerts to more than 60 institutional partners in the three states of the western United States where most of the nation’s earthquake risk is concentrated: California, Oregon, and Washington. The ShakeAlert 2.0 product for public alerting is a message containing a polygon enclosing a region predicted to experience modified Mercalli intensity (MMI) threshold levels that depend on the delivery method. Wireless Emergency Alerts are delivered for M 5+ earthquakes with expected shaking of MMI≥IV. For cell phone apps, the thresholds are M 4.5+ and MMI≥III. A polygon format alert is the easiest description for selective rebroadcasting mechanisms (e.g., cell towers) and is a requirement for some mass notification systems such as the Federal Emergency Management Agency’s Integrated Public Alert and Warning System. ShakeAlert 2.0 was tested using historic waveform data consisting of 60 M 3.5+ and 25 M 5.0+ earthquakes, in addition to other anomalous waveforms such as calibration signals. For the historic event test, the average M 5+ false alert and missed event rates for ShakeAlert 2.0 are 8% and 16%. The M 3.5+ false alert and missed event rates are 10% and 36.7%. Real-time performance metrics are also presented to assess how the system behaves in regions that are well-instrumented, sparsely instrumented, and for offshore earthquakes.

Accuracy and Uncertainty Analysis of Selected Methodological Approaches to Earthquake Early Warning in Europe

2021 ◽  
Author(s):  
Gemma Cremen ◽  
Elisa Zuccolo ◽  
Carmine Galasso
Keyword(s):  
Early Warning ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Parameter Estimates ◽  
Parameter Uncertainties ◽  
Ground Shaking ◽  
Methodological Approaches ◽  
Error Metric ◽  
Conceptual Study

Abstract Earthquake early warning (EEW) is becoming an increasingly attractive real-time strategy for mitigating the threats posed by potentially devastating incoming seismic events. As efforts accelerate to develop practical EEW-based solutions for earthquake-prone countries in Europe, it is important to understand and quantify the level of performance that can be achieved by the underlying seismological algorithms. We conduct a conceptual study on EEW performance in Europe, which explicitly focuses on the accuracy and associated uncertainties of selected methodological approaches. Twenty-three events from four diverse European testbeds are used to compare the quality of EEW predictions produced by the Virtual Seismologist and PRobabilistic and Evolutionary early warning SysTem algorithms. We first examine the location and magnitude estimates of the algorithms, accounting for both bias and uncertainty in the resulting predictions. We then investigate the ground-shaking prediction capabilities of the source-parameter estimates, using an error metric that can explicitly capture the propagation of uncertainties in these estimates. Our work highlights the importance of accounting for EEW parameter uncertainties, which are often neglected in studies of EEW performance. Our findings can be used to inform current and future implementations of EEW systems in Europe. In addition, the evaluation metrics presented in this work can be used to determine EEW accuracy in any worldwide setting.

scholarly journals Design and implementation of a mobile device app for network-based earthquake early warning systems (EEWSs): application to the PRESTo EEWS in southern Italy

2020 ◽  
Vol 20 (4) ◽  
pp. 921-931
Author(s):  
Simona Colombelli ◽  
Francesco Carotenuto ◽  
Luca Elia ◽  
Aldo Zollo
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Southern Italy ◽  
Warning System ◽  
Early Warning Systems ◽  
Mobile App ◽  
Earthquake Early Warning ◽  
Wide Audience ◽  
Ground Shaking ◽  
Earthquake Early Warning System

Abstract. A fundamental feature of any earthquake early warning system is the ability of rapidly broadcast earthquake information to reach a wide audience of potential end users and stakeholders, in an intuitive, customizable way. Smartphones and other mobile devices are nowadays continuously connected to the Internet and represent the ideal tools for earthquake alerts dissemination to inform a large number of users about the potential damaging shaking of an impending earthquake. Here we present a mobile app (named ISNet EWApp or simply EWApp) for Android devices which can receive the alerts generated by a network-based Early Warning system. Specifically, the app receives the earthquake alerts generated by the PRESTo EEWS, which is currently running on the accelerometric stations of the Irpinia Seismic Network (ISNet) in southern Italy. In the absence of alerts, EWApp displays the standard bulletin of seismic events that have occurred within the network. In the event of a relevant earthquake, the app has a dedicated module to predict the expected ground-shaking intensity and the available lead time at the user's position and to provide customized messages to inform the user about the proper reaction to adopt during the alert. We first present the architecture of both the network-based system and EWApp and then describe its essential operational modes. The app is designed in a way that is easily exportable to any other network-based early warning system.

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