Earthquake Early Warning Algorithm Research Articles

Developing and evaluating a risk-informed decision support system for earthquake early warning at a railway bridge

Earthquake early warning (EEW) systems provide timely information on the arrival of strong seismic waves at a site. Such information can help mitigate the consequences of earthquakes on the operation of infrastructure assets. EEW-informed mitigation actions should stem from risk-based decision-making protocols, and EEW benefits should be evaluated for the range of possible rupture scenarios that affect an asset of interest. This paper addresses these challenges specifically for railway bridges by: (1) developing a risk-informed EEW decision support system (DSS) for these assets; and (2) quantifying the effectiveness of the proposed EEW-DSS in mitigating seismic risks for railway bridges across relevant rupture scenarios. The proposed EEW-DSS combines information on site-specific seismic hazard, time-dependent EEW algorithm outputs, probabilistic seismic demand modeling, damage/derailment fragilities, and seismic loss models. These modeling components compose a multi-criteria decision-making framework. Value of information theory is then proposed to estimate the loss-mitigation benefits of the EEW-DSS, accounting for varied stakeholder risk priorities as well as dynamic lead-time/accuracy tradeoffs related to EEW performance. A railway bridge with multiple piers is adopted for the case study, which demonstrates the importance of risk-based, uncertainty-informed decision-making to the overall effectiveness of EEW in potentially reducing seismic losses.

Performance and Next-Generation Development of the Finite-Fault Rupture Detector (FinDer) within the United States West Coast ShakeAlert Warning System

ABSTRACTRapid fault rupture information is important to estimate seismic ground motions and damage in large earthquakes, and is, therefore, of great value for earthquake early warning (EEW) and rapid response. The Finite-Fault Rupture Detector (FinDer) algorithm computes earthquake line-source models by comparing spatial distributions of high-frequency seismic amplitudes with precomputed template maps. FinDer is one of two seismic EEW algorithms currently adopted by the United States West Coast ShakeAlert EEW system. Between March 2018 and October 2022, FinDer detected 1048 earthquakes (2.3 ≤ M ≤ 7.1) inside the FinDer-reporting region in California, Oregon, and Washington with a median detection time of 8.5 s (75th and 95th percentile: 11.5 s, 38.9 s) after event origin and median errors (first report) of 6.7 km (75th and 95th percentile: 10.5 km, 25.5 km) in location, −0.45 s (mean ± st. dev.: 0.1 ± 5.9 s) in origin time, and 0.33 units (mean ± st. dev.: 0.33 ± 0.31 m.u.) in magnitude. Ground motions estimated using FinDer source parameters are in excellent agreement with observed peak ground accelerations, and residuals are, on average, 30% smaller than if predicted from catalog source parameters. This suggests that FinDer’s simple source parameter terms are accounting for more complex high-frequency source characteristics. This article summarizes the performance of FinDer in ShakeAlert and describes the recent improvements to the algorithm addressing issues encountered during real-time operation. This includes the handling of latent seismic data, robust event detection in regions with sparse instrumentation, enabling faster magnitude convergence in large earthquakes, use of fault- and scenario-specific earthquakes (e.g., along the Cascadia subduction zone or San Andreas fault), as well as increased robustness of FinDer in complex earthquake sequences. We demonstrate the performance of the new FinDer version 3 algorithm using waveform playbacks of selected events along the U.S. West Coast, Japan, and China, including both historic and synthetic earthquakes.

Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review

Earthquake early warning system (EEWS) plays an important role in detecting ground shaking during an earthquake and alerting the public and authorities to take appropriate safety measures, reducing possible damages to lives and property. However, the cost of high-end ground motion sensors makes most earthquake-prone countries unable to afford an EEWS. Low-cost Microelectromechanical systems (MEMS)-based ground motion sensors are becoming a promising solution for constructing an affordable yet reliable and robust EEWS. This paper contributes to advancing Earthquake early warning (EEW) research by conducting a literature review investigating different methods and approaches to building a low-cost EEWS using MEMS-based sensors in different territories. The review of 59 articles found that low-cost MEMS-based EEWSs can become a feasible solution for generating reliable and accurate EEW, especially for developing countries and can serve as a support system for high-end EEWS in terms of increasing the density of the sensors. Also, this paper proposes a classification for EEWSs based on the warning type and the EEW algorithm adopted. Further, with the support of the proposed EEWS classification, it summarises the different approaches researchers attempted in developing an EEWS. Following that, this paper discusses the challenges and complexities in implementing and maintaining a low-cost MEMS-based EEWS and proposes future research areas to improve the performance of EEWSs mainly in 1) exploring node-level processing, 2) introducing multi-sensor support capability, and 3) adopting ground motion-based EEW algorithms for generating EEW.

The Potential of Using Fiber Optic Distributed Acoustic Sensing (DAS) in Earthquake Early Warning Applications

ABSTRACT As the seismological community embraces fiber optic distributed acoustic sensing (DAS), DAS arrays are becoming a logical, scalable option to obtain strain and ground-motion data for which the installation of seismometers is not easy or cheap, such as in dense offshore arrays. The potential of strain data in earthquake early warning (EEW) applications has been recently demonstrated using records from borehole strainmeters (BSMs). However, current BSM networks are sparse, installing more BSMs is expensive and often impractical, and BSMs have the same limitations in offshore environments as other traditional seismic instruments. Here, we aim to provide a road map about how DAS data could be used in existing EEW applications, using the ShakeAlert EEW System for the West Coast of the United States as an example. We review the data requirements for EEW systems, examine ways in which strain-derived ground-motion data can be incorporated into such systems without significant modifications, and determine what is still needed for full utilization of DAS data in these applications. Importantly, EEW algorithms require ground-motion amplitude information for rapid earthquake source characterization; thus, accurate strain amplitude observations, not only phase information, are necessary for deriving these ground-motion metrics from DAS data. To obtain high-quality ground-motion observations, EEW-compatible DAS arrays need to be multicomponent, well coupled, and low noise. We suggest ways to achieve such data requirements using existing DAS technology and discuss areas in which further research is needed to optimize DAS array performance for EEW.

“Saving Precious Seconds”—A Novel Approach to Implementing a Low-Cost Earthquake Early Warning System with Node-Level Detection and Alert Generation

This paper presents findings from ongoing research that explores the ability to use Micro-Electromechanical Systems (MEMS)-based technologies and various digital communication protocols for earthquake early warning (EEW). The paper proposes a step-by-step guide to developing a unique EEW network architecture driven by a Software-Defined Wide Area Network (SD-WAN)-based hole-punching technology consisting of MEMS-based, low-cost accelerometers hosted by the general public. In contrast with most centralised cloud-based approaches, a node-level decentralised data-processing is used to generate warnings with the support of a modified Propagation of Local Undamped Motion (PLUM)-based EEW algorithm. With several hypothetical earthquake scenarios, experiments were conducted to evaluate the system latencies of the proposed decentralised EEW architecture and its performance was compared with traditional centralised EEW architecture. The results from sixty simulations show that the SD-WAN-based hole-punching architecture supported by the Transmission Control Protocol (TCP) creates the optimum alerting conditions. Furthermore, the results provide clear evidence to show that the decentralised EEW system architecture can outperform the centralised EEW architecture and can save valuable seconds when generating EEW, leading to a longer warning time for the end-user. This paper contributes to the EEW literature by proposing a novel EEW network architecture.

A deep neural network framework for real‐time on‐site estimation of acceleration response spectra of seismic ground motions

AbstractVarious earthquake early warning (EEW) methodologies have been proposed globally for speedily estimating information (i.e., location, magnitude, ground‐shaking intensities, and/or potential consequences) about ongoing seismic events for real‐time/near real‐time earthquake risk management. Conventional EEW algorithms have often been based on the inferred physics of a fault rupture combined with simplified empirical models to estimate the source parameters and intensity measures of interest. Given the recent boost in computational resources, data‐driven methods/models are now widely accepted as effective alternatives for EEW. This study introduces a highly accurate deep‐learning‐based computational framework named ROSERS (i.e., Real‐time On‐Site Estimation of Response Spectra) to estimate the acceleration response spectrum (Sa(T)) of the expected on‐site ground‐motion waveforms using early non‐damage‐causing early p‐waves and site characteristics. The framework is trained using a carefully selected extensive database of recorded ground motions. Due to the well‐known correlation of Sa(T) with structures’ seismic response and resulting damage/losses, rapid and accurate knowledge of expected on‐site Sa(T) values is highly beneficial to various end‐users to make well‐informed real‐time and near‐real‐time decisions. The framework is thoroughly assessed and investigated through multiple statistical tests under three historical earthquake events. These analyses demonstrate that the overall framework leads to excellent prediction power and, on average, has an accuracy above 85% for hazard‐consistent early‐warning trigger classification.

Alert Optimization of the PLUM Earthquake Early Warning Algorithm for the Western United States

ABSTRACT We determine an optimal alerting configuration for the propagation of local undamped motion (PLUM) earthquake early warning (EEW) algorithm for use by the U.S. ShakeAlert system covering California, Oregon, and Washington. All EEW systems should balance the primary goal of providing timely alerts for impactful or potentially damaging shaking while limiting alerts for shaking that is too low to be of concern (precautionary alerts). The PLUM EEW algorithm forward predicts observed ground motions to nearby sites within a defined radius without accounting for attenuation, avoiding the earthquake source parameter estimation step of most EEW algorithms. PLUM was originally developed in Japan where the alert regions and ground motions for which alerts are issued differ from those implemented by ShakeAlert. We compare predicted ground motions from PLUM to ShakeMap-reported ground motions for a set of 22 U.S. West Coast earthquakes of magnitude 4.4–7.2 and evaluate available warning times. We examine a range of prediction radii (20–100 km), thresholds used to issue an alert (alert threshold), and levels of impactful or potentially damaging shaking (target threshold). We find optimal performance when the alert threshold is close to the target threshold, although higher target ground motions benefit from somewhat lower alert thresholds to ensure timely alerts. We also find that performance, measured as the cost reduction that a user can achieve, depends on the user’s tolerance for precautionary alerts. Users with a low target threshold and high tolerance for precautionary alerts achieve optimal performance when larger prediction radii (60–100 km) are used. In contrast, users with high target thresholds and low tolerance for precautionary alerts achieve better performance for smaller prediction radii (30–60 km). Therefore, setting the PLUM prediction radius to 60 km balances the needs of many users and provides warning times of up to ∼20 s.

The Impact of 3D Finite-Fault Information on Ground-Motion Forecasting for Earthquake Early Warning

ABSTRACTWe identify aspects of finite-source parameterization that strongly affect the accuracy of estimated ground motion for earthquake early warning (EEW). EEW systems aim to alert users to impending shaking before it reaches them. The U.S. West Coast EEW system, ShakeAlert, currently uses two algorithms based on seismic data to characterize the earthquake’s location, magnitude, and origin time, treating it as a point or line source. From this information, ShakeAlert calculates shaking intensity and alerts locations where shaking estimates exceed a threshold. Several geodetic EEW algorithms under development would provide 3D finite-fault information. We investigate conditions under which this information produces sufficiently better intensity estimates to potentially improve alerting. Using scenario crustal and subduction interface sources, we (1) identify the most influential source geometry parameters for an EEW algorithm’s shaking forecast, and (2) assess the intensity alert thresholds and magnitude ranges for which more detailed source characterization affects alert accuracy. We find that alert regions determined using 3D-source representations of correct magnitude and faulting mechanism are generally more accurate than those obtained using line sources. If a line-source representation is used and magnitude is calculated from the estimated length, then incorrect length estimates significantly degrade alert region accuracy. In detail, the value of 3D-source characterization depends on the user’s chosen alert threshold, tectonic regime, and faulting style. For the suite of source models we tested, the error in shaking intensity introduced by incorrect geometry could reach levels comparable to the intrinsic uncertainty in ground-motion calculations (e.g., 0.5–1.3 modified Mercalli intensity [MMI] units for MMI 4.5) but, especially for crustal sources, was often less. For subduction interface sources, 3D representations substantially improved alert area accuracy compared to line sources, and incorrect geometry parameters were more likely to cause error in calculated shaking intensity that exceeded uncertainties.

Early Warning for Great Earthquakes From Characterization of Crustal Deformation Patterns With Deep Learning

AbstractAlthough infrequent, large (Mw7.5+) earthquakes can be extremely damaging and occur on subduction and intraplate faults worldwide. Earthquake early warning (EEW) systems aim to provide advanced warning before strong shaking and tsunami onsets. These systems estimate earthquake magnitude using the early metrics of waveforms, relying on empirical scaling relationships of abundant past events. However, both the rarity and complexity of great events make it challenging to characterize them, and EEW algorithms often underpredict magnitude and the resulting hazards. Here, we propose a model, M‐LARGE, that leverages deep learning to characterize crustal deformation patterns of large earthquakes for a specific region in real‐time. We demonstrate the algorithm in the Chilean Subduction Zone by training it with more than six million different simulated rupture scenarios recorded on the Chilean GNSS network. M‐LARGE performs reliable magnitude estimation on the testing data set with an accuracy of 99%. Furthermore, the model successfully predicts the magnitude of five real Chilean earthquakes that occurred in the last 11 years. These events were damaging, large enough to be recorded by the modern high rate global navigation satellite system instrument, and provide valuable ground truth. M‐LARGE tracks the evolution of the source process and can make faster and more accurate magnitude estimation, significantly outperforming other similar EEW algorithms. This is the first demonstration of our approach. Future work toward generalization is outstanding and will include the addition of more training and testing data, interfacing with existing EEW methods, and applying the method to different tectonic settings to explore performance in these regions.

Status of Earthquake Early Warning in Switzerland

The Swiss Seismological Service (SED) at ETH has been developing methods and open-source software for Earthquake Early Warning (EEW) for more than a decade and has been using SeisComP for earthquake monitoring since 2012. The SED has built a comprehensive set of SeisComP modules that can provide EEW solutions in a quick and transparent manner by any seismic service operating SeisComP. To date, implementations of the Virtual Seismologist (VS) and Finite-Fault Rupture Detector (FinDer) EEW algorithms are available. VS provides rapid EEW magnitudes building on existing SeisComP detection and location modules for point-source origins. FinDer matches growing patterns of observed high-frequency seismic acceleration amplitudes with modeled templates to identify rupture extent, and hence can infer on-going finite-fault rupture in real-time. Together these methods can provide EEW for all event dimensions from moderate to great, if a high quality, EEW-ready, seismic network is available. In this paper, we benchmark the performance of this SeisComP-based EEW system using recent seismicity in Switzerland. Both algorithms are observed to be similarly fast and can often produce first EEW alerts within 4–6 s of origin time. In real time performance, the median delay for the first VS alert is 8.7 s after origin time (56 earthquakes since 2014, from M2.7 to M4.6), and 7 s for FinDer (10 earthquakes since 2017, from M2.7 to M4.3). The median value for the travel time of the P waves from event origin to the fourth station accounts for 3.5 s of delay; with an additional 1.4 s for real-time data sample delays. We demonstrate that operating two independent algorithms provides redundancy and tolerance to failures of a single algorithm. This is documented with the case of a moderate M3.9 event that occured seconds after a quarry blast, where picks from both events produced a 4 s delay in the pick-based VS, while FinDer performed as expected. Operating on the Swiss Seismic Network, that is being continuously optimised for EEW, the SED-ETHZ SeisComP EEW system is achieving performance that is comparable to operational EEW systems around the world.

Comparing the Performance of Regional Earthquake Early Warning Algorithms in Europe

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 SeisComPscanlocmodule 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.

The PLUM Earthquake Early Warning Algorithm: A Retrospective Case Study of West Coast, USA, Data

AbstractThe PLUM (Propagation of Local Undamped Motion) earthquake early warning (EEW) algorithm differs from typical source‐based EEW algorithms as it predicts shaking directly from observed shaking without first deriving earthquake source information (e.g., magnitude and epicenter). Here, we determine optimal PLUM event detection thresholds for U.S. West Coast earthquakes using two data sets: 558 M3.5+ earthquakes (California, Oregon, Washington; 2012–2017) and the ShakeAlert test suite of historic and problematic signals (1999–2015). PLUM computes Modified Mercalli Intensity (IMMI) using velocity and acceleration data, leveraging co‐located sensors to avoid problematic signals. An event detection is issued when the observed IMMI exceeds a given threshold(s). We find a two‐station detection method using IMMI trigger thresholds of 4.0 and 3.0 for the first and second stations, respectively, is optimal for detecting M4.5+ earthquakes. PLUM detected 79 events in the 2012–2017 data set, reporting (not including telemetry or alert dissemination) detection times on par, and sometimes faster than current EEW methods (mean 8 s; median 6 s). As expected, detection times were slower for the older 1999–2015 earthquakes (N = 21; mean 11 s; median 6 s) when station coverage was sparser. Of the 31 PLUM detected M5+ events (10 2012–2017; 21 1999–2015), theoretically 20 (∼65%) could provide timely warnings. PLUM issued no false detections and avoided issuing detections for all calibration/anomalous signals, regional and teleseismic events. We conclude PLUM can successfully identify IMMI 4+ shaking from local earthquakes and could complement and enhance EEW in the U.S.

Commentary: The Role of Geodetic Algorithms for Earthquake Early Warning in Cascadia

AbstractThe ShakeAlert earthquake early warning (EEW) system issues public alerts in California and will soon extend to Oregon and Washington. The Cascadia subduction zone presents significant new challenges and opportunities for EEW. Initial publications suggested that EEW algorithms based on Global Navigation Satellite System (GNSS) data could provide improved warning for intraslab events and dramatically improved warning for offshore megathrust events, both of which contribute significantly to hazard in Cascadia. We find that some expectations in these publications were unrealistic, and we demonstrate that in general geodetic algorithms would not produce timely warnings for intraslab events nor warning times of two minutes or more for severe shaking from megathrust earthquakes. Nonetheless, lessons from recent earthquakes in Japan and California, for which alerts from seismic algorithms suffered from magnitude saturation and high data latencies, demonstrate the urgent need for rigorous testing of geodetic EEW as a potential complement to seismic EEW.

Latency of Waveform Data Delivery from the Southern California Seismic Network during the 2019 Ridgecrest Earthquake Sequence and Its Effect on ShakeAlert

Abstract The occurrence of the 4–6 July 2019 Mw 6.4 and Mw 7.1 Ridgecrest earthquake sequence provided the first full-scale test of the network and telemetry readiness of the Southern California Seismic Network (SCSN), to support the ShakeAlert earthquake early warning (EEW) system in California. ShakeAlert is a U.S. Geological Survey (USGS)-led collaboration to detect earthquakes and, when possible, to alert the public before the arrival of the strongest shaking. The SCSN performed well in its regional monitoring role for both the 4 July Mw 6.4 and the 6 July Mw 7.1 earthquakes. In the EEW role, it provided timely delivery of 5 s of P-wave data to ShakeAlert, which issued its first alert 6.9 s after origin time. Data delivery at peak data volumes for many stations exhibited some latency, and, as a consequence, some data arrived too late for analysis by one of the EEW algorithms. We find that the average link bandwidth for each station was sufficient, because all waveform data were delivered automatically to the archive, but link capacity for many stations was insufficient for peak demand. We describe the performance of the data telemetry for the sequence, including cellular, radio, hybrid, and backhaul systems. Cellular-based telemetry systems maintained low latency throughout strong shaking and after, but some stations, even at great distances, experienced subsequent brief increases in latency. Performance of radio links depended mostly on the signal strength of the link, with short-distance direct shots to high-bandwidth backhaul systems showing no latency impact, whereas stations on some long distance or marginal quality links suffered latencies of tens or hundreds of seconds. Improvements are being implemented to move telemetry links onto USGS and partner high-bandwidth microwave systems, and to reduce dependency on less robust long-distance radio shots.

How Often Can Earthquake Early Warning Systems Alert Sites With High‐Intensity Ground Motion?

AbstractAlthough numerous Earthquake Early Warning (EEW) algorithms have been developed to date, we lack a detailed understanding of how often and under what circumstances useful ground motion alerts can be provided to end users. In particular, it is unclear how often EEW systems can successfully alert sites with high ground motion intensities. These are the sites that arguably need EEW alerts the most, but they are also the most challenging ones to alert because they tend to be located close to the epicenter where the seismic waves arrive first. Here we analyze the alerting performance of the Propagation of Local Undamped Motion (PLUM), Earthquake Point‐Source Integrated Code (EPIC), and Finite‐Fault Rupture Detector (FinDer) algorithms by running them retrospectively on the seismic strong‐motion data of the 219 earthquakes in Japan since 1996 that exceeded Modified Mercalli Intensity (MMI) of 4.5 on at least 10 sites (Mw 4.5–9.1). Our analysis suggests that, irrespective of the algorithm, EEW end users should expect that EEW can often but not always provide useful alerts. Using a conservative warning time (tw) definition, we find that 40–60% of sites with strong to extreme shaking levels receive alerts with tw > 5 s. If high‐intensity shaking is caused by shallow crustal events, around 50% of sites with strong (MMI~6) and <20% of sites with severe and violent (MMI ≥ 8) shaking receive alerts with tw > 5 s. Our results provide detailed quantitative insight into the expected alerting performance for EEW algorithms under realistic conditions. We also discuss how operational systems can achieve longer warning times with more precautionary alerting strategies.

Event Detection Performance of the PLUM Earthquake Early Warning Algorithm in Southern California

AbstractWe test the Japanese ground‐motion‐based earthquake early warning (EEW) algorithm, propagation of local undamped motion (PLUM), in southern California with application to the U.S. ShakeAlert system. In late 2018, ShakeAlert began limited public alerting in Los Angeles to areas of expected modified Mercalli intensity (IMMI) 4.0+ for magnitude 5.0+ earthquakes. Most EEW systems, including ShakeAlert, use source‐based methods: they estimate the location, magnitude, and origin time of an earthquake from P waves and use a ground‐motion prediction equation to identify regions of expected strong shaking. The PLUM algorithm uses observed ground motions directly to define alert areas and was developed to address deficiencies in the Japan Meteorological Agency source‐based EEW system during the 2011 Mw 9.0 Tohoku earthquake sequence. We assess PLUM using (a) a dataset of 193 magnitude 3.5+ earthquakes that occurred in southern California between 2012 and 2017 and (b) the ShakeAlert testing and certification suite of 49 earthquakes and other seismic signals. The latter suite includes events that challenge the current ShakeAlert algorithms. We provide a first‐order performance assessment using event‐based metrics similar to those used by ShakeAlert. We find that PLUM can be configured to successfully issue alerts using IMMI trigger thresholds that are lower than those implemented in Japan. Using two stations, a trigger threshold of IMMI 4.0 for the first station and a threshold of IMMI 2.5 for the second station PLUM successfully detect 12 of 13 magnitude 5.0+ earthquakes and issue no false alerts. PLUM alert latencies were similar to and in some cases faster than source‐based algorithms, reducing area that receives no warning near the source that generally have the highest ground motions. PLUM is a simple, independent seismic method that may complement existing source‐based algorithms in EEW systems, including the ShakeAlert system, even when alerting to light (IMMI 4.0) or higher ground‐motion levels.

Lead Time for Cities of Northern India by Using Multiparameter EEW Algorithm

Earthquake early warning (EEW) is considered one of the important real-time earthquake damage mitigation measures. The presence of seismogenic sources generating high seismicity in Himalayas and the cities of concern lying at appropriate distances makes Northern India a perfect case to be monitored using EEW systems. In the present study, an attempt has been made to estimate the lead times for Northern Indian cities for issuing early warning by using the EEW system deployed by IIT Roorkee in Central Himalayas. The instrumentation deployed at 100 locations between Uttarkashi and Chamoli has been used to estimate the lead time at six cities. The estimated lead time includes the time to reach S-wave after subtraction of the sum of P-wave arrival time at the station, time taken by EEW algorithm, transmission and processing delay. The study reveals that for Dehradun, Hardwar, Roorkee, Muzaffarnagar, Meerut, and Delhi the minimum calculated lead time is 5, 11, 20, 35, and 68 sec while the maximum lead time is 37, 36, 47, 59, and 90 sec, respectively. Such larger estimated lead times to these densely populated cities show that EEW can successfully work as one of the important real-time earthquake disaster reduction measures in Northern India.

A Fast Earthquake Early Warning Algorithm Based on the First 3 s of the P‐Wave Coda

Research Article| July 10, 2018 A Fast Earthquake Early Warning Algorithm Based on the First 3 s of the P‐Wave Coda Armando Cuéllar; Armando Cuéllar aCentro de Instrumentación y Registro Sísmico (CIRES), Anaxágoras 814, Colonia Narvarte, Mexico City 03020, Mexico Search for other works by this author on: GSW Google Scholar Gerardo Suárez; Gerardo Suárez bInstituto de Geofísica, Universidad Nacional Autónoma de México, Ciudad Universitaria, Mexico City 04510, Mexico, gerardo@geofisica.unam.mx Search for other works by this author on: GSW Google Scholar J. M. Espinosa‐Aranda J. M. Espinosa‐Aranda aCentro de Instrumentación y Registro Sísmico (CIRES), Anaxágoras 814, Colonia Narvarte, Mexico City 03020, Mexico Search for other works by this author on: GSW Google Scholar Bulletin of the Seismological Society of America (2018) 108 (4): 2068–2079. https://doi.org/10.1785/0120180079 Article history first online: 10 Jul 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Armando Cuéllar, Gerardo Suárez, J. M. Espinosa‐Aranda; A Fast Earthquake Early Warning Algorithm Based on the First 3 s of the P‐Wave Coda. Bulletin of the Seismological Society of America 2018;; 108 (4): 2068–2079. doi: https://doi.org/10.1785/0120180079 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyBulletin of the Seismological Society of America Search Advanced Search Abstract A new seismic early warning algorithm is presented that estimates a magnitude threshold using the first 3 s of the P‐wave coda on the vertical component. The algorithm considerably reduces the processing time compared to previous algorithms used by the Mexican seismic early warning system (Seismic Alert System of Mexico [SASMEX]). It was designed to alert earthquakes within the subducted Cocos plate. The tP+3 algorithm was based on a training dataset of 76 accelerograms of 25 Mexican in‐slab earthquakes, with focal depths >40 km⁠. The algorithm uses two parameters based on the unfiltered vertical component of the P waves: the sum of the cumulative quadratic acceleration, av(T) and θP a parameter that represents the slope of the cumulative acceleration. The model is based on a learning machine that linearizes piecewise the empirical relation between these two parameters and magnitude Mw⁠. The resulting algorithm was tested on nine earthquakes that took place from 2014 to 2017, recorded in 37 strong‐motion records. In addition, the algorithm was evaluated in the context of the Mexican earthquake early warning, applying it to 24 in‐slab earthquakes occurring from 1995 to 2017 (⁠5.0<Mw<7.1⁠). The magnitude of 19 earthquakes was properly estimated; for four of them, it was overestimated and in one case the magnitude was underestimated. Three earthquakes Mw>6.5 that affected Mexico City were included in the dataset: the Mw 6.5 event on 11 December 2011 and the destructive in‐slab Tehuacán and Morelos earthquakes on 15 June 1999 (⁠Mw 7.0) and 19 September 2017 (⁠Mw 7.1). The retrospective application of the tP+3 algorithm shows that these three earthquakes are correctly identified as Mw>6 and would activate a seismic alert. The tP+3 algorithm would have given an advance warning of 34, 35, and 16 s respectively, before the arrival of strong motion in Mexico City. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Real‐Time Detection of Rupture Development: Earthquake Early Warning Using P Waves From Growing Ruptures

AbstractLarge earthquakes with long rupture durations emit P wave energy throughout the rupture period. Incorporating late‐onset P waves into earthquake early warning (EEW) algorithms could contribute to robust predictions of strong ground motion. Here I describe a technique to detect in real time P waves from growing ruptures to improve the timeliness of an EEW algorithm based on seismic wavefield estimation. The proposed P wave detector, which employs a simple polarization analysis, successfully detected P waves from strong motion generation areas of the 2011 Mw 9.0 Tohoku‐oki earthquake rupture. An analysis using 23 large (M ≥ 7) events from Japan confirmed that seismic intensity predictions based on the P wave detector significantly increased lead times without appreciably decreasing the prediction accuracy. P waves from growing ruptures, being one of the fastest carriers of information on ongoing rupture development, have the potential to improve the performance of EEW systems.

An Earthquake Early Warning Algorithm Based on the P‐Wave Energy Released in the tS–tP Interval

An Earthquake Early Warning Algorithm Based on the P‐Wave Energy Released in the tS–tP Interval