scholarly journals Retrospective Study of FinDer Algorithm During the 2019, Ridgecrest Earthquake Sequence

2021 ◽  
Author(s):  
Wei Huang
Keyword(s):  
Ground Motion ◽  
Early Warning ◽  
Warning System ◽  
Damage Threshold ◽  
Earthquake Sequence ◽  
Ground Shaking ◽  
Earthquake Early Warning System ◽  
Finite Fault ◽  
Alert Threshold ◽  
Warning Time

Abstract Real-time characterization of evolving rupture is crucial for mitigating against seismic hazards exposed to potentially devastating earthquake events in EEWs (Earthquake Early Warning system). Currently, FinDer (Finite Fault Rupture Detector) algorithm explicitly utilizes observed ground motion pattern to solve for the evolving rupture to generate alerts for early warning purpose, which is currently contributing to ShakeAlert EEW system in West Coast of United States, within the area covered by the Advanced National Seismic System (ANSS) network. Here we implement FinDer offline to explore its feasibility assuming ideal field telemetry on a database of real earthquakes with magnitude M ≥5.0 occurring in Ridgecrest, Southern California in 2019. We specially focus on evaluating the performance of FinDer through end-user-orientated analysis in terms of warning time and accuracy of ground shaking prediction. Overall, FinDer classifies alerts with a rate of success over 74% across a broad range of alert criteria, substantial fraction of sites can be successfully alerted including the most difficult cases with high ground motion intensities regardless of invariable few seconds of warning time. FinDer can be configured to generate more useful alerts with higher cost savings by applying lower alert threshold during the Ridgecrest earthquake sequence. Furthermore, although large fractions of sites would have been timely alerted, it is significantly challenging for predicting accurately the moderate or worse intensities (Modified Mercalli Intensity > 5.5) in advance even if applying lower alert threshold and higher damage threshold. Nonetheless, FinDer performs well in an evolutionary manner to guarantee reliable alerts by resorting to a consistent description of point source or occurring rupture.

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.

Repeatable Source, Path, and Site Effects from the 2019 M 7.1 Ridgecrest Earthquake Sequence

2020 ◽  
Vol 110 (4) ◽  
pp. 1530-1548 ◽  
Author(s):  
Grace A. Parker ◽  
Annemarie S. Baltay ◽  
John Rekoske ◽  
Eric M. Thompson
Keyword(s):  
Los Angeles ◽  
Ground Motion ◽  
Site Response ◽  
Ground Motions ◽  
Warning System ◽  
Site Amplification ◽  
Earthquake Sequence ◽  
Ground Acceleration ◽  
Small Magnitude ◽  
Earthquake Early Warning System

ABSTRACT We use a large instrumental dataset from the 2019 Ridgecrest earthquake sequence (Rekoske et al., 2019, 2020) to examine repeatable source-, path-, and site-specific ground motions. A mixed-effects analysis is used to partition total residuals relative to the Boore et al. (2014; hereafter, BSSA14) ground-motion model. We calculate the Arias intensity stress drop for the earthquakes and find strong correlation with our event terms, indicating that they are consistent with source processes. We look for physically meaningful trends in the partitioned residuals and test the ability of BSSA14 to capture the behavior we observe in the data. We find that BSSA14 is a good match to the median observations for M>4. However, we find bias for individual events, especially those with small magnitude and hypocentral depth≥7  km, for which peak ground acceleration is underpredicted by a factor of 2.5. Although the site amplification term captures the median site response when all sites are considered together, it does not capture variations at individual stations across a range of site conditions. We find strong basin amplification in the Los Angeles, Ventura, and San Gabriel basins. We find weak amplification in the San Bernardino basin, which is contrary to simulation-based findings showing a channeling effect from an event with a north–south azimuth. This and an additional set of ground motions from earthquakes southwest of Los Angeles suggest that there is an azimuth-dependent southern California basin response related to the orientation of regional structures when ground motion from waves traveling south–north are compared with those in the east–west direction. These findings exhibit the power of large, spatially dense ground-motion datasets and make clear that nonergodic models are a way to reduce bias and uncertainty in ground-motion estimation for applications like the U.S. Geological Survey National Seismic Hazard Model and the ShakeAlert earthquake early warning System.

scholarly journals Design and implementation of a mobile device APP for network-based EEW systems: application to PRESTo EEWS in Southern Italy

2019 ◽  
Author(s):  
Simona Colombelli ◽  
Francesco Carotenuto ◽  
Luca Elia ◽  
Aldo Zollo
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Southern Italy ◽  
Warning System ◽  
Mobile App ◽  
Wide Audience ◽  
Ground Shaking ◽  
Earthquake Early Warning System ◽  
Operational Modes ◽  
The Ideal

Abstract. A fundamental feature of any Earthquake Early Warning System is the ability of rapidly broadcast earthquake information to 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) 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 EWS, which is currently running on the accelerometric stations of the Irpinia Seismic Network (ISNet) in Southern Italy. In the absence of alerts, the EWApp displays the standard bulletin of seismic events occurred within the network. In the event of a relevant earthquake, instead, the app has a dedicated module to predict the expected ground shaking intensity and the available lead-time at the user position and to provide customized messages to inform the user about the proper reaction during the alert. We first present the architecture of both network-based system and EWApp, and then and describe its essential operational modes. The app is designed in a way that is easily exportable to any other network-based early warning system.

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.

Design and implementation of a mobile device APP for network-based EEW systems: application to PRESTo EEWS in Southern Italy

2020 ◽  
Author(s):  
Simona Colombelli ◽  
Francesco Carotenuto ◽  
Luca Elia ◽  
Aldo Zollo
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Southern Italy ◽  
Warning System ◽  
Mobile App ◽  
Wide Audience ◽  
Ground Shaking ◽  
Earthquake Early Warning System ◽  
Operational Modes ◽  
The Ideal

<p><span>A fundamental feature of any Earthquake Early Warning System is the ability of rapidly broadcast earthquake information to 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.</span></p><p><span>Here we present a mobile App (named ISNet 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 EWS, which is currently running on the accelerometric stations of the Irpinia Seismic Network (ISNet) in Southern Italy. In the absence of alerts, the EWApp displays the standard bulletin of seismic events occurred within the network. In the event of a relevant earthquake, instead, the app has a dedicated module to predict the expected ground shaking intensity and the available lead-time at the user position and to provide customized messages to inform the user about the proper reaction during the alert.</span></p><p><span>We first present the architecture of both network-based system and EWApp, and then and describe its essential operational modes. The app is designed in a way that is easily exportable to any other network-based early warning system.</span></p>

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.

scholarly journals Preliminary Results of an Earthquake Early Warning System in Costa Rica

2021 ◽  
Vol 9 ◽  
Author(s):  
Juan Porras ◽  
Frédérick Massin ◽  
Mario Arroyo-Solórzano ◽  
Ivonne Arroyo ◽  
Lepolt Linkimer ◽  
...  
Keyword(s):  
Costa Rica ◽  
Early Warning ◽  
Early Warning System ◽  
Source Parameters ◽  
Warning System ◽  
Earthquake Early Warning ◽  
Capital City ◽  
San Jose ◽  
Earthquake Early Warning System ◽  
Finite Fault

We analyze the performance of a prototype earthquake early warning system deployed at the National Seismological Network of Costa Rica in collaboration with the Swiss Seismological Service by presenting the real-time performance during six earthquakes (Mw 5.1-6.4) that took place during 2018 and 2019. We observe that, despite only limited efforts to optimize the existing network of 158 stations, for EEW purposes, the network density allows fast determination of source parameters using both the Virtual Seismologist and the Finite Fault Rupture Detector algorithms. Shallow earthquakes on or near-shore are routinely identified within 11–20 s of their occurrence. The warning times for the capital city of San Jose are of 43 s for epicenters located at 220 km, like for the Mw 6.4 Armuelles earthquake. On the other hand, during the time analyzed, the EEW system did not provide positive warning times for earthquakes at distances less than 40 km from San Jose. Even though large (Mw > 7) distant historical earthquakes have not caused heavy damage in San Jose, there is potential for developing an EEW system for Costa Rica, especially for the purposes of rapid earthquake notifications, disaster response management, and seismic risk mitigation.

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.

Earthquake Early Warning: Advances, Scientific Challenges, and Societal Needs

2019 ◽  
Vol 47 (1) ◽  
pp. 361-388 ◽  
Author(s):  
Richard M. Allen ◽  
Diego Melgar
Keyword(s):  
Ground Motion ◽  
Early Warning ◽  
The United States ◽  
Earthquake Early Warning ◽  
Ground Shaking ◽  
Motion Models ◽  
Societal Needs ◽  
Finite Fault ◽  
User Groups ◽  
Ground Motion Models

Earthquake early warning (EEW) is the delivery of ground shaking alerts or warnings. It is distinguished from earthquake prediction in that the earthquake has nucleated to provide detectable ground motion when an EEW is issued. Here we review progress in the field in the last 10 years. We begin with EEW users, synthesizing what we now know about who uses EEW and what information they need and can digest. We summarize the approaches to EEW and gather information about currently existing EEW systems implemented in various countries while providing the context and stimulus for their creation and development. We survey important advances in methods, instrumentation, and algorithms that improve the quality and timeliness of EEW alerts. We also discuss the development of new, potentially transformative ideas and methodologies that could change how we provide alerts in the future. ▪ Earthquake early warning (EEW) is the rapid detection and characterization of earthquakes and delivery of an alert so that protective actions can be taken. ▪ EEW systems now provide public alerts in Mexico, Japan, South Korea, and Taiwan and alerts to select user groups in India, Turkey, Romania, and the United States. ▪ EEW methodologies fall into three categories, point source, finite fault, and ground motion models, and we review the advantages of each of these approaches. ▪ The wealth of information about EEW uses and user needs must be employed to focus future developments and improvements in EEW systems.

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