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

ABSTRACT We 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.

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

Bulletin of the Seismological Society of America ◽

10.1785/0120200022 ◽

2020 ◽

Vol 110 (4) ◽

pp. 1872-1886 ◽

Cited By ~ 1

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.

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

Bulletin of the Seismological Society of America ◽

10.1785/0120180326 ◽

2019 ◽

Vol 109 (4) ◽

pp. 1524-1541 ◽

Cited By ~ 6

Author(s):

Elizabeth S. Cochran ◽

Julian Bunn ◽

Sarah E. Minson ◽

Annemarie S. Baltay ◽

Deborah L. Kilb ◽

...

Keyword(s):

Los Angeles ◽

Ground Motion ◽

Early Warning ◽

Southern California ◽

Ground Motions ◽

Tohoku Earthquake ◽

Prediction Equation ◽

Earthquake Early Warning ◽

Japan Meteorological Agency ◽

Origin Time

Abstract We 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.

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Earthquake Early Warning System in Israel—Towards an Operational Stage

Frontiers in Earth Science ◽

10.3389/feart.2021.684421 ◽

2021 ◽

Vol 9 ◽

Author(s):

Ran N. Nof ◽

Itzhak Lior ◽

Ittai Kurzon

Keyword(s):

Ground Motion ◽

Early Warning ◽

Seismic Network ◽

Earthquake Early Warning ◽

Performance Estimation ◽

Motion Prediction ◽

Ground Motion Prediction Equations ◽

Prediction Equations ◽

Ground Motion Prediction ◽

Origin Time

The Geological Survey of Israel has upgraded and expanded the national Israeli Seismic Network (ISN), with more than 110 stations country-wide, as part of the implementation of a governmental decision to build a national Earthquake Early Warning (EEW) system named TRUAA. This upgraded seismic network exhibits a high station density and fast telemetry. The stations are distributed mainly along the main fault systems, the Dead Sea Transform, and the Carmel-Zfira Fault, which may potentially produce Mw 7.5 earthquakes. The system has recently entered a limited operational phase, allowing for initial performance estimation. Real-time performance during eight months of operation (41 earthquakes) matches expectations. Alert delays (interval between origin-time and Earthquake Early Warning alert time) are reduced to as low as 3 s, and source parameter errorstatistics are within expected values found in previous works using historical data playbacks. An evolutionary alert policy is implemented based on a magnitude threshold of Mw 4.2 and peak ground accelerations exceeding 2 cm/s2. A comparison between different ground motion prediction equations (GMPE) is presented for earthquakes from Israel and California using median ground motion prediction equations values. This analysis shows that a theoretical GMPE produced the best agreement with observed ground motions, with less bias and lower uncertainties. The performance of this GMPE was found to improve when an earthquake specific stress drop is implemented.

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

Annual Review of Earth and Planetary Sciences ◽

10.1146/annurev-earth-053018-060457 ◽

2019 ◽

Vol 47 (1) ◽

pp. 361-388 ◽

Cited By ~ 35

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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Status of Earthquake Early Warning in Switzerland

Frontiers in Earth Science ◽

10.3389/feart.2021.707654 ◽

2021 ◽

Vol 9 ◽

Author(s):

Frédérick Massin ◽

John Clinton ◽

Maren Böse

Keyword(s):

Real Time ◽

Open Source Software ◽

Early Warning ◽

Seismic Network ◽

Earthquake Early Warning ◽

Fault Rupture ◽

Time Data ◽

Origin Time ◽

P Waves ◽

Finite Fault

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.

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

10.21203/rs.3.rs-1009668/v1 ◽

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.

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Source Process-Related Delays in Earthquake Early Warning for Example Cases in Greece

Bulletin of the Seismological Society of America ◽

10.1785/0120200385 ◽

2021 ◽

Author(s):

Nikolaos Vavlas ◽

Anastasia A. Kiratzi ◽

Zafeiria Roumelioti

Keyword(s):

Early Warning ◽

Monitoring Network ◽

Earthquake Early Warning ◽

Source Process ◽

Spatial Density ◽

Intermediate Depth ◽

National Monitoring ◽

Gulf Of Corinth ◽

Crete Island ◽

Finite Fault

ABSTRACT We explore a hypothetical zero-latency earthquake early warning (EEW) system in Greece, aiming to provide alerts before warning thresholds of the intensity of ground motion are exceeded. Within the seismotectonic context of Greece, both shallow- and intermediate-depth earthquakes (along the Hellenic subduction zone) are plausible and, thus, examined. Using regionally applicable attenuation relations, we combine and adjust the methodologies of Minson et al. (2018) and Hoshiba (2020) to examine what are the minimum magnitudes required to invoke the warning thresholds at the user site. With simple modeling, we examine how fast an alert can be issued and what is the available warning time when taking into account delays due to finite-fault rupture propagation, alongside other delays. These computations are merged with delays introduced due to the present-day configuration of the Greek national monitoring network (varying spatial density of permanent monitoring stations). This approach serves as a tool to assess the feasibility of an EEW system at specific sites and to redesign the national permanent monitoring network to serve such a system more effectively (we provide results for four sites.). Warning times for on-land crustal earthquakes are found to be shorter, whereas for intermediate-depth earthquakes in Greece an EEW system is feasible (provides warning times of several tens of seconds at large cities, e.g., on Crete Island) even with the current configuration of the national monitoring network, which is quite sparse in the southern part of the country. The current network configuration also provides sufficient early warning (e.g., of the order of 10 s for a warning threshold of 0.05g) at the center of Athens from earthquakes of the eastern Gulf of Corinth—a zone posing elevated hazard in the broader area of the Greek capital. Several additional assumptions and factors affecting the operability of an EEW system in Greece (i.e., source process complexity and uncertainty in attenuation laws) are also discussed.

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The limits of earthquake early warning: Timeliness of ground motion estimates

Science Advances ◽

10.1126/sciadv.aaq0504 ◽

2018 ◽

Vol 4 (3) ◽

pp. eaaq0504 ◽

Cited By ~ 44

Author(s):

Sarah E. Minson ◽

Men-Andrin Meier ◽

Annemarie S. Baltay ◽

Thomas C. Hanks ◽

Elizabeth S. Cochran

Keyword(s):

Ground Motion ◽

Early Warning ◽

Earthquake Early Warning

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

Bulletin of the Seismological Society of America ◽

10.1785/0120190306 ◽

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.

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

10.5194/egusphere-egu2020-5107 ◽

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

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.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.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.

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