Comparing the Performance of Regional Earthquake Early Warning Algorithms in Europe

Zuccolo, Elisa; Cremen, Gemma; Galasso, Carmine

doi:10.3389/feart.2021.686272

Cited by 12 publications

(7 citation statements)

References 54 publications

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“…Studies have shown that implementing a PRESTo in Italy could theoretically deliver alert messages within approximately 5 to 10 s, with maximum lead times for implementing security measures at a distant target of about 25 s [16]. This system, therefore, represents the current state-of-the-art in regional EEW technology in the country and is one of the most used software solutions for EEW in Europe [18]. Besides PRESTo, the other most widely used regional EEW software in Europe is VS(SC) (Virtual Seismologist in SeisComP) [21].…”

Section: Related Workmentioning

confidence: 99%

“…VS(SC) incorporates phase picking using the Short-Time Average over Long-Time Average (STA/LTA) algorithm [22], and location and magnitude estimation algorithms integrated in the Seiscomp software module [23]. It has been shown that PRESTo is ultimately the best choice for regional EEW compared to the VS(SC) module [18], as it provides better short-term predictions, a key requirement for EEW. Furthermore, PRESTo has undergone extensive implementation and testing in multiple regions and scenarios, including Southern Italy, Turkey, Romania, Southern Iberia, Austria, and Slovenia [18].…”

Section: Related Workmentioning

confidence: 99%

“…Besides testing the accuracy of the localization algorithm, experiments in terms of latency are also carried out to test the suitability of the developed solution for EEW. The performance of the on-board triggering, localization algorithm, and the overall IoT application are compared with the PRobabilistic and Evolutionary early warning SysTem (PRESTo) software [13,15], which represents the Italian and European reference standard for regional EEW [16][17][18], with dedicated software modules for picking, localization, and magnitude estimation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning

Pierleoni,

Concetti,

Belli

et al. 2023

Sensors

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An effective earthquake early warning system requires rapid and reliable earthquake source detection. Despite the numerous proposed epicenter localization solutions in recent years, their utilization within the Internet of Things (IoT) framework and integration with IoT-oriented cloud platforms remain underexplored. This paper proposes a complete IoT architecture for earthquake detection, localization, and event notification. The architecture, which has been designed, deployed, and tested on a standard cloud platform, introduces an innovative approach by implementing P-wave “picking” directly on IoT devices, deviating from traditional regional earthquake early warning (EEW) approaches. Pick association, source localization, event declaration, and user notification functionalities are also deployed on the cloud. The cloud integration simplifies the integration of other services in the architecture, such as data storage and device management. Moreover, a localization algorithm based on the hyperbola method is proposed, but here, the time difference of arrival multilateration is applied that is often used in wireless sensor network applications. The results show that the proposed end-to-end architecture is able to provide a quick estimate of the earthquake epicenter location with acceptable errors for an EEW system scenario. Rigorous testing against the standard of reference in Italy for regional EEW showed an overall 3.39 s gain in the system localization speed, thus offering a tangible metric of the efficiency and potential proposed system as an EEW solution.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning

Pierleoni,

Concetti,

Belli

et al. 2023

Sensors

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“…Point source assessments verify EEW algorithms for magnitude and hypocenter accuracy (e.g., Cua et al, 2009;Chen et al, 2015;Massin et al, 2021). Alert assessments are performed to prevent errors during operations or simulations (e.g., Chen et al, 2019;Chung et al, 2019;Zuccolo et al, 2021). However, management aspects require a comprehensive review of the overall EEW.…”

Section: Introductionmentioning

confidence: 99%

Assessing network-based earthquake early warning systems in low-seismicity areas

Ahn,

Cho,

Hwang

et al. 2023

Front. Earth Sci.

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Earthquake early warning (EEW) technology, designed to alert the public of earthquake risks after initial P-wave detection but before the onset of strong tremors, has developed rapidly. Methodologies from various fields are combined in EEW systems to estimate earthquake locations, magnitudes, and expected intensities based on the initial P-wave data. These systems operate automatically because prompt responses are required. However, as no common evaluation framework for EEW system verification exists, potentially divergent evaluations from reviewers or countries could ensue. Moreover, evaluating EEW systems is more complicated when the target area does not experience frequent earthquakes. We aimed to establish a guidance review process for low-seismicity areas to ensure reliable and stable integrated EEW system operation. We incorporated management aspects through actual system operator surveillance and designed an EEW assessment process based on feedback from our surveys. Using this approach, we created a comprehensive and well-informed evaluation process that considers the diverse perspectives of experts involved in EEWs. Our proposed assessment method allows for a uniform and consistent evaluation process, regardless of changes in the methods or technologies used by EEW systems. The method aims to guide EEW system assessments in low-seismicity areas.

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“…Utilizing the time difference between primary and secondary seismic waves, they send warnings once an earthquake is detected. This allows immediate actions like unlocking building exits, managing power grids, and adjusting operations in critical facilities like hospitals and nuclear power plants [2].…”

Section: Introductionmentioning

confidence: 99%

Advanced Seismic Magnitude Classification Through Convolutional and Reinforcement Learning Techniques

Lin,

2023

IJACSA

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Earthquake Early Warning (EEW) systems are crucial in reducing the dangers associated with earthquakes. This paper delves into the realm of EEWs, focusing on rapidly determining earthquake magnitudes (EMs). Traditional methods for swift magnitude categorization often grapple with challenges such as data disparity and cumbersome processes. Our research introduces an innovative EEW model, employing a 7-second seismic waveform record from three different components provided by the China Earthquake Network Center (CENC). This empirical, quantitative study pioneers a method combining dilated convolutional techniques with a novel mutual learningbased artificial bee colony (ML-ABC) algorithm and reinforcement learning (RL) for EM classification. The proposed model utilizes an ensemble of convolutional neural networks (CNNs) to simultaneously extract feature vectors from input images, which are then amalgamated for classification. To address the imbalances in the dataset, we implement an RLbased algorithm, conceptualizing the training process as a series of decisions with individual samples representing distinct states. Within this framework, the network operates as an agent, receiving rewards or penalties based on its precision in distinguishing between the minority and majority classes.A key innovation in our approach is the initial weight pre-training using the ML-ABC method. This technique dynamically optimizes the "food source" for candidates, integrating mutual learning elements related to the initial weights. Extensive experiments were carried out on the selected dataset to ascertain the most effective parameter values, including the reward function. The findings demonstrate the superiority of our proposed model over other evaluated methods, highlighting its potential as a robust tool for EM classification in seismology. This research provides valuable insights for both seismologists and developers of EEW systems, offering a novel, efficient approach to earthquake magnitude determination.

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Comparing the Performance of Regional Earthquake Early Warning Algorithms in Europe

Cited by 12 publications

References 54 publications

A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning

A Cloud-IoT Architecture for Latency-Aware Localization in Earthquake Early Warning

Assessing network-based earthquake early warning systems in low-seismicity areas

Advanced Seismic Magnitude Classification Through Convolutional and Reinforcement Learning Techniques

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