Earthquake Early Warning Research Articles

Assessing the potential implementation of earthquake early warning for schools in the Patras region, Greece

Earthquake early warning (EEW) is currently deemed a credible approach to seismic resilience enhancement in modern societies, especially if part of a more holistic earthquake mitigation strategy involving other risk reduction tools such as structural upgrading/retrofit. Yet, there remains a strong need to 1) assess the feasibility of EEW in various seismotectonic contexts, considering specific target applications/end users; and 2) develop next-generation decision-support systems relying on interpretable probabilistic impact-based estimates toward more risk-informed decision-making on EEW installation/alert triggering. These challenges are addressed in this paper, which showcases a series of recent significant EEW contributions by the authors. First, we present the results of a state-of-the-art feasibility study for EEW in schools performed across the Patras region of Greece, attempting to spatially combine traditional seismologically-driven EEW decision criteria (i.e., warning time) with proxy risk-oriented measures for earthquake impact (i.e., building fragility and the number of exposed school students). These results show that, under certain conditions, EEW could be effective for the schools in the considered case-study region. We then demonstrate an advanced end-user-centred approach for improved risk-informed decision-making on triggering EEW alerts. The proposed methodology integrates earthquake-engineering-related seismic performance assessment procedures and metrics with multi-criteria decision-making (MCDM) within an end-to-end probabilistic framework. The performance-based earthquake engineering component of such a framework facilitates the computation of various damage/loss estimates (e.g., repair cost, downtime, and casualties) by combining target-structure-specific models of seismic response, fragility, and vulnerability with real-time ground-shaking estimates. Additionally, the incorporated MCDM methodology enables explicit consideration of end-user preferences (importance) towards the estimated consequences in the context of alert issuance. The developed approach is demonstrated using an archetype school building for the case-study region, for which we specifically investigate the optimal decision (i.e., “trigger” or “don't trigger” an EEW alert) across a range of ground-motion intensity measures. We find that the best action for a given level of ground shaking can vary as a function of stakeholder preferences.

A framework to quantify the effectiveness of earthquake early warning in mitigating seismic risk

Earthquake early warning systems (EEWSs) aim to rapidly detect earthquakes and provide timely alerts, so that users can take protective actions prior to the onset of strong ground shaking. The promise and limitations of EEWSs have both been widely debated. On one hand, an operational EEWS could mitigate earthquake damage by triggering potentially cost- and life-saving actions. These range from automated system responses such as slowing down trains to the actions of individuals that receive the alerts and take protective measures. On the other hand, the effectiveness of an EEWS is conditional on the ability to issue warnings that are sufficiently accurate and timely to facilitate an appropriate action. The refinement of earthquake early warning (EEW) algorithms and the installation of denser and faster seismic networks have improved performance; however, the benefit in risk reduction that an EEWS could achieve remains unquantified. In this study, we leverage upon regional event-based probabilistic seismic risk assessment to devise a quantitative and fully customizable framework for evaluating the effectiveness of EEW in mitigating seismic risk. We demonstrate this framework using Switzerland as a testbed, for which we compute and contrast human loss exceedance curves with and without EEW.

Incorporation of Real-Time Earthquake Magnitudes Estimated via Peak Ground Displacement Scaling in the ShakeAlert Earthquake Early Warning System

ABSTRACTThe United States earthquake early warning (EEW) system, ShakeAlert®, currently employs two algorithms based on seismic data alone to characterize the earthquake source, reporting the weighted average of their magnitude estimates. Nonsaturating magnitude estimates derived in real time from Global Navigation Satellite System (GNSS) data using peak ground displacement (PGD) scaling relationships offer complementary information with the potential to improve EEW reliability for large earthquakes. We have adapted a method that estimates magnitude from PGD (Crowell et al., 2016) for possible production use by ShakeAlert. To evaluate the potential contribution of the modified algorithm, we installed it on the ShakeAlert development system for real-time operation and for retrospective analyses using a suite of GNSS data that we compiled. Because of the colored noise structure of typical real-time GNSS positions, observed PGD values drift over time periods relevant to EEW. To mitigate this effect, we implemented logic within the modified algorithm to control when it issues initial and updated PGD-derived magnitude estimates (MPGD), and to quantify MPGD uncertainty for use in combining it with estimates from other ShakeAlert algorithms running in parallel. Our analysis suggests that, with these strategies, spuriously large MPGD will seldom be incorporated in ShakeAlert’s magnitude estimate. Retrospective analysis of data from moderate-to-great earthquakes demonstrates that the modified algorithm can contribute to better magnitude estimates for Mw>7.0 events. GNSS station distribution throughout the ShakeAlert region limits how soon the modified algorithm can begin estimating magnitude in some locations. Furthermore, both the station density and the GNSS noise levels limit the minimum magnitude for which the modified algorithm is likely to contribute to the weighted average. This might be addressed by alternative GNSS processing strategies that reduce noise.

Automatic Determination of Back Azimuth Based on a Single Mini Array for an Earthquake Early Warning System

ABSTRACT Earthquake early warning (EEW) systems systematically monitor ground motion and provide alerts prior to damaging shaking. However, these systems present certain challenges, which include avoiding alerts caused by false triggers or delayed alerts for earthquakes from outside the seismic network. Here, we show that an array processing approach, which is capable of identifying the propagation direction and type of seismic phase, can overcome these challenges. In this study, a new automatic array processing approach was implemented using a single mini array to determine the back azimuth and slowness of the phases for local events. Seismograms of local events with ML 2.0–4.6 that occurred in the Korean Peninsula were analyzed using three array processing methods: beam packing, frequency–wavenumber, and plane-wave fitting. In addition, a combined P-wave picking procedure was introduced, and its performance was evaluated. We developed the criteria associated with slowness range and consistency of back azimuths to determine a stable back azimuth from the three array processing methods, estimating the back azimuth of the seismic phase with more accuracy than any single method. It is expected that seismic mini arrays and the developed automatic array processing approach can be used for network EEW systems to discriminate alarms caused by false triggers. In addition, the developed approach can be used for rapid onsite and front-detection warnings using the array inside or on the outskirts of a network when combined with other estimations, such as an epicentral distance or another back azimuth.

A statistical methodology for classifying earthquake detections and for earthquake parameter estimation in smartphone-based earthquake early warning systems

Smartphone-based earthquake early warning systems (EEWSs) are emerging as a complementary solution to classic EEWSs based on expensive scientific-grade instruments. Smartphone-based systems, however, are characterized by a highly dynamic network geometry and by noisy measurements. Thus, there is a need to control the probability of false alarms and the probability of missed detection. This study proposes a statistical methodology to address this challenge and to jointly estimate in near real-time earthquake parameters like epicenter and depth. The methodology is based on a parametric statistical model, on hypothesis testing and on Monte Carlo simulation. The methodology is tested using data obtained from the Earthquake Network (EQN), a citizen science initiative that implements a global smartphone-based EEWS. It is discovered that, when the probability to miss an earthquake is fixed at 1%, the probability of false alarm is 0.8%, proving that EQN is a robust smartphone-based EEW system.

Seismoacoustic Coupled Signals from the 11 July 2020 Ms 5.1 Tangshan, China, Earthquake

ABSTRACT The Ms 5.1 Tangshan earthquake that occurred on 11 July 2020 was recorded by the infrasound array DQS at a source-to-receiver distance of 196 km and an azimuth of 278°. Relatively high-apparent velocity, celerity of epicenter infrasound, and propagation modeling indicate a stratospheric arrival. The backprojection of the epicentral infrasound detection delineates a northeast direction-extended infrasound radiation region with a long axis of about 57 km and a short axis of about 28 km, which is in good agreement with the fault rupture direction. The robustness of the infrasound radiation source location is also tested. The consistency between the spatial distribution of the radiation source and the epicenter confirms that the extended radiating zone around the epicenter excited infrasound signals lasting 2.5 min at a distance of 196 km. A comparison between the backprojected sound pressure level and peak surface pressure derived from measured seismic ground motions shows some disagreement, which may involve propagation path and/or topography around the epicenter. Therefore, these factors should be taken into account when locating the sources of infrasound and estimating the distribution of ground motion. This study shows the ability of infrasound arrays to detect, locate, and research moderate earthquakes and help reconstruct the seismoacoustic coupling in the source region and fault rupture. Moreover, the complexity of the infrasound radiation source and seismoacoustic coupling mechanism is recognized. It is expected that infrasonic observations of moderate earthquakes will increase with the number of infrasound arrays deployed worldwide. Our study is also helpful to the scientific design and optimal layout of infrasound monitoring systems, which will allow for the detection of earthquakes in addition to explosive sources.

Applications of Nonergodic Site Response Models to ShakeAlert Case Studies in the Los Angeles Area

ABSTRACT In this study, we explore whether the Parker and Baltay (2022) site response models for the Los Angeles (LA) basin region can improve ground-motion forecasts in the U.S. Geological Survey ShakeAlert earthquake early warning system (hereafter ShakeAlert). We implement the peak ground acceleration and peak ground velocity site response models of Parker and Baltay (2022) in ShakeAlert via the earthquake information to ground-motion (hereafter eqinfo2GM) module, which predicts ground motions from the estimated earthquake parameters of magnitude, rupture length, and location. The nonergodic site response models for the greater LA area were developed using ground motions from 414 M 3–7.3 earthquakes in southern California. We test nonergodic ground-motion forecasts for five earthquakes in the LA area: the 1994 M 6.7 Northridge earthquake, the 2008 M 5.4 Chino Hills earthquake, the 2019 M 7.1 Ridgecrest earthquake, the 2020 M 4.5 South El Monte earthquake, and a synthetic M 7.8 earthquake on the southern San Andreas fault from the ShakeOut scenario, which was the basis of a statewide emergency response exercise. From the test results, we find that with the nonergodic site response applied, ShakeAlert not only alerts larger areas but can also result in longer warning times in LA region. In addition, the modified Mercalli intensity (MMI) ground-motion predictions generated by the ShakeAlert eqinfo2GM module are improved in accuracy when compared with the corresponding ShakeMap ground-truth MMI when the nonergodic site response model is applied.

<b>Improvement in Rapidness of Earthquake Early Warning Using Ocean Bottom Seismic Data</b>

This study proposes an earthquake early warning (EEW) method for ocean bottom seismic (OBS) data systems of railways. Because the conventional Shinkansen systems adopts only the S-wave alarm method for the OBS data, we demonstrate that the P-wave alarm method can estimate epicentral distance and magnitude using the OBS data but cannot accurately determine back azimuth. Considering this, we propose a concrete method for how to judge whether a warning is issued applying the OBS data. The proposed method enables us to improve the rapidness of EEW and the safety of railways during earthquakes.

Estimation of Willingness-to-pay for Enhanced Earthquake Early Warning System

This study estimated the willingness to pay (WTP) to enhance an earthquake early warning system (EEWS). Korea's need to prepare for earthquake damage, such as large-scale earthquakes accompanied by significant damage that affected Gyeongju in 2016 and Pohang in 2017, has come. The faster an earthquake warning is delivered, the more advantageous it is to secure an evacuation time; thus, an EEWS is a vital preparedness policy to minimize earthquake damage. As a result of estimating the WTP for the enhanced EEWS using the contingent valuation method (CVM) in this paper, whether the respondent has experienced an earthquake and considered the EEWS an effective earthquake preparedness countermeasure statistically affects their WTP. It was a primary variable with a significant impact, and the total WTP for enhanced domestic EEWS was estimated at about KRW 1.2 trillion. The scale of benefits of enhanced EEWS evaluated in this study can provide important information in establishing policies related to earthquake disaster-related damage prevention.

Improving Out of Network Earthquake Locations Using Prior Seismicity for Use in Earthquake Early Warning

ABSTRACTTimely alerts sent through earthquake early warning (EEW) programs allow those alerted to take protective actions to mitigate their risk from potentially damaging shaking. Over the past few years, ShakeAlert, the EEW program focused on the west coast of the contiguous United States, has grown, alerting communities within California, Oregon, and Washington about earthquakes where damaging shaking is expected. ShakeAlert uses a set of algorithms including the point-source algorithm, earthquake point-source integrated code (EPIC), to determine the location, magnitude, and origin time of potential earthquakes. Although EPIC produces low-latency and low error solutions for many events originating within the seismic network on land, numerous recent small earthquakes rupturing offshore of northern California have EPIC location solutions with high error (>50 km compared to USGS locations). Because most events are occurring offshore, there is a limited number of stations that can trigger and contribute information in a timely manner for use in EEW. To better constrain location solutions in this region, we propose to include information about contemporary past seismicity into EPIC’s grid-search algorithm through a Bayesian framework. This prior information layer downweights high error locations where EPIC’s proposed event location coincides with an area of low prior seismicity in preference for locations with a similar level of data fit that also have higher past seismicity. This addition to EPIC lowers the mean location error offshore northern California from 58 to 14 km.

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.

CSESnet: A deep learning P-wave detection model based on UNet++ designed for China Seismic Experimental Site

Accurate detection of P-wave arrivals has important applications in real-time seismic data processing, such as earthquake monitoring and earthquake early warning. The Sichuan and Yunnan regions, where the China Seismic Experimental Site (CSES) is located, has frequent strong earthquakes and large amount small earthquakes, resulting in serious earthquake disasters. In this paper, we modify the UNet++ network structure and use 490,000 event waveform data and 78,000 noisy data from the CSES as the data set, and analyze the effects of the training set quality, labeled data and loss function on the model performance to obtain a new P-wave detection model-CSESnet. The recall, precision and F1 score of this model are 94.6%, 85.4% and 89.7%, respectively. The tests in Beijing Capital Circle (BCC) indicates the performance of the CSESnet decrease little and has good generalization. The test in Luxian M6.0 earthquake shows that CSESnet can also predict the P-wave arrival times of large earthquakes and process strong motion data very well. CSESnet provides a new detection model to improve the earthquake detection capability in CSES.

Earthquake Countermeasures in Scanning Tunneling Microscopy Experiments Using Earthquake Early Warning

Earthquake Countermeasures in Scanning Tunneling Microscopy Experiments Using Earthquake Early Warning

Magnitude estimation and ground motion prediction to harness fiber optic distributed acoustic sensing for earthquake early warning

Earthquake early warning (EEW) systems provide seconds to tens of seconds of warning time before potentially-damaging ground motions are felt. For optimal warning times, seismic sensors should be installed as close as possible to expected earthquake sources. However, while the most hazardous earthquakes on Earth occur underwater, most seismological stations are located on-land; precious seconds may go by before these earthquakes are detected. In this work, we harness available optical fiber infrastructure for EEW using the novel approach of distributed acoustic sensing (DAS). DAS strain measurements of earthquakes from different regions are converted to ground motions using a real-time slant-stack approach, magnitudes are estimated using a theoretical earthquake source model, and ground shaking intensities are predicted via ground motion prediction equations. The results demonstrate the potential of DAS-based EEW and the significant time-gains that can be achieved compared to the use of standard sensors, in particular for offshore earthquakes.

Fracture‐Mesh Faulting in the Swarm‐Like 2020 Maacama Sequence Revealed by High‐Precision Earthquake Detection, Location, and Focal Mechanisms

AbstractIn August of 2020, an earthquake sequence initiated within the Maacama fault zone in northern California, raising questions about its relationship with the larger‐scale fault. To investigate the faulting geometry and its implications for physical processes driving seismicity, we applied an integrated, multi‐faceted seismic analysis including waveform‐correlation‐based event detection, relative relocation, and high‐precision focal mechanisms. To determine mechanisms for a large population of very small earthquakes (predominantly M < 1), we combined correlation‐derived polarity analysis with a recently developed technique to determine S/P amplitude ratios from single seismic components. Finally, we applied an iterative stress inversion to distinguish between the likely fault and auxiliary planes. This analysis reveals that although the sequence initiated on a right‐lateral fault, plausibly a strand of the Maacama Fault, it also activated numerous en echelon left‐lateral conjugate faults. Together, these interlocking faults form a fracture mesh, consistent with structures associated elsewhere with fluid‐induced faulting and earthquake swarms.

Combining Structural Health Monitoring and Earthquake Early Warning Techniques for an Enhanced Monitoring System

Advancements in Civil Engineering & Technology Combining Structural Health Monitoring and Earthquake Early Warning Techniques for an Enhanced Monitoring System Shieh-Kung Huang* Assistant Professor, Department of Civil Engineering, National Chung-Hsing University, Taiwan *Corresponding author:Shieh-Kung Huang, Assistant Professor, Department of Civil Engineering, National Chung-Hsing University, Taiwan Submission: December 16, 2022;Published: January 05, 2023 DOI: 10.31031/ACET.2023.05.000613 ISSN: 2639-0574 Volume5 Issue3

Employing Machine Learning and IoT for Earthquake Early Warning System in Smart Cities

An earthquake early warning system (EEWS) should be included in smart cities to preserve human lives by providing a reliable and efficient disaster management system. This system can alter how different entities communicate with one another using an Internet of Things (IoT) network where observed data are handled based on machine learning (ML) technology. On one hand, IoT is employed in observing the different measures of EEWS entities. On the other hand, ML can be exploited to analyze these measures to reach the best action to be taken for disaster management and risk mitigation in smart cities. This paper provides a survey on the different aspects required for that EEWS. First, the IoT system is generally discussed to provide the role it can play for EEWS. Second, ML models are classified into linear and non-linear ones. Third, the evaluation metrics of ML models are addressed by focusing on seismology. Fourth, this paper exhibits a taxonomy that includes the emerging ML and IoT efforts for EEWS. Fifth, it proposes a generic EEWS architecture based on IoT and ML. Finally, the paper addresses the application of ML for earthquake parameters’ observations leading to an efficient EEWS.

Unveiling the potential: preliminary result on an AI-based onsite earthquake early warning system deployment in Denpasar City

Due to Indonesia’s geographical location within the Pacific Ring of Fire, it is susceptible to earthquake occurrences. Therefore, the implementation of an Earthquake Early Warning (EEW) System is crucial to mitigate the impact of earthquakes. This study focused on deploying an on-site EEW system on Bali Island, an Indonesian region known for its elevated seismic activity. The study evaluated the system’s performance over five months in 2023. Throughout this observation period, the AI-based on-site EEW system successfully detected the earthquake’s P-wave before the occurrence of stronger seismic waves, such as the peak ground acceleration (PGA). It provided a warning time of up to 75 seconds and accurately predicted the intensity of the impending earthquake. These outcomes meet the fundamental requirements for an effective EEW system. The results obtained from the deployed AI-based on-site EEW system demonstrate the potential for wider implementation of such systems across Indonesia.

Introduction to Rusho’s Transform Lakshmann and Smith Model: A Machine Learning Approach to Earthquake Detection

Machine learning now become one of the demandable field in engineering and life sciences , IT is now the cutting edge of technological development , The threat of climate change makes it crucial to improve our understanding of the climate system . This paper has been inspired by the book : Machine Learning and Data Mining Approaches to climate science . From this book I am inspired to find a new branch of machine learning for the earthquake detection and intro climate +physics or Climato -physics Modelling . Using Artificial intelligence is one of the remarkable steps to find the optimal solution of Earthquake detection . Approximately 20,000 people are killed every year by earthquakes. There are over 500 active faults in California and most of its residents live within 30 miles of an active fault. So if we can predict the earth-quake before it occurred , we can make active step and save millions of life . It is similar to weather detection and thunderstorms detection , same algorithm will be implemented in thunderstorm and weather detection , but we will use both as well as machine learning model and artificial intelligence model ,Rats, weasels, snakes, and centipedes reportedly left their homes and headed for safety several days before a destructive earthquake. Anecdotal evidence abounds of animals, fish, birds, reptiles, and insects exhibiting strange behavior anywhere from weeks to seconds before an earthquake. So we will first understand why and how earthquake happen and biological mechanism of understanding fish , birds , reptiles and insects hearing . Then we will use the datasets of global earthquake pattern . And finally we will put all in one and relate the algorithm with storm-detection.

Development of Seismic Intensity Maps Using Low-Cost Micro-Electro-Mechanical Systems Seismic Network

The Korea Meteorological Administration (KMA), SK Telecom, and Kyungpook National University collaborated to develop the low-cost IoT seismometer (LCIS) that embeds a micro-electro-mechanical systems accelerometer and LTE communication sensor. Currently, approximately 7,000 stations (the average density is 0.07/km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) with LCIS devices are operating in real-time on the Korean Peninsula, which is about 25 times higher density than the national seismic network with high performance seismometers operated by KMA. The majority of LCISs are installed inside buildings so that adjustment is applied to convert to the free-surface equivalent vibration. Researchers derived seismic intensity maps using only LCIS network data, which showed very similar distributions to the maps from the high-performance network. This validates the usefulness of the LCIS network for Modified Mercalli intensity (MMI) map generation and earthquake early warning system.