Performance Evaluation of a Dense MEMS-Based Seismic Sensor Array Deployed in the Sichuan-Yunnan Border Region for Earthquake Early Warning

With the last decades of development, earthquake early warning (EEW) has proven to be one of the potential means for disaster mitigation. Usually, the density of the EEW network determines the performance of the EEW system. For reducing the cost of sensors and building a dense EEW network, an upgraded low-cost Micro Electro Mechanical System (MEMS)-based sensor named GL-P2B was developed in this research. This device uses a new high-performance CPU board and is built on a custom-tailored Linux 3.6.9 operating system integrating with seismological processing. Approximately 170 GL-P2Bs were installed and tested in the Sichuan-Yunnan border region from January 2017 to December 2018. We evaluated its performance on noise-level, dynamic range (DR), useful resolution (NU), collocated recording comparison, and shake map generation. The results proved that GL-P2B can be classified as a type of Class-B sensor. The records obtained are consistent with the data obtained by the collocated traditional force-balanced accelerometers even for stations with an epicenter distance of more than 150 km, and most of the relative percentage difference of peak ground acceleration (PGA) values is smaller than 10%. In addition, with the current density of the GL-P2B seismic network, near-real-time refined shake maps without using values derived for virtual stations could be directly generated, which will significantly improve the capability for earthquake emergency response. Overall, this MEMS-based sensor can meet the requirements of dense EEW purpose and lower the total investment of the National System for Fast Seismic Intensity Report and Earthquake Early Warning project.

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Performance Evaluation of Low-Cost Seismic Sensors for Dense Earthquake Early Warning: 2018–2019 Field Testing in Southwest China

Sensors ◽

10.3390/s19091999 ◽

2019 ◽

Vol 19 (9) ◽

pp. 1999 ◽

Cited By ~ 3

Author(s):

Jihua Fu ◽

Zhitao Li ◽

Hao Meng ◽

Jianjun Wang ◽

Xinjian Shan

Keyword(s):

Early Warning ◽

Dynamic Range ◽

Low Cost ◽

Noise Analysis ◽

Field Testing ◽

Earthquake Early Warning ◽

Real Field ◽

Seismic Sensors ◽

Good Consistency ◽

Class C

Earthquake Early Warning (EEW) was proved to be a potential means of disaster reduction. Unfortunately, the performance of the EEW system is largely determined by the density of EEW network. How to reduce the cost of sensors has become an urgent problem for building a dense EEW. A low-cost seismic sensor integrated with a Class C MEMS accelerometer was proposed in this paper. Based on minimal structure design, the sensor’s reliability was enhanced, while the costs were cut down as well. To fully reveal the performance, ten of the seismic sensors were installed and tested in Sichuan Province, southwest of China from May 2018 to February 2019. The seismic records obtained by the MNSMSs were compared with those by the traditional strong motion seismographs. The records obtained by the MNSMSs have good consistency with the data obtained by the Etnas. The MNSMSs can obtain clear seismic phases that are enough to trigger earthquake detections for EEW. By noise analysis, different channels of the same sensor and different sensors have good consistency. The tested dynamic range (over 87 dB) and useful resolution (over 14.5 bits) are completely in conformity with the designed parameters. Through real field testing, small earthquakes (M 3.1–3.6) can be detected by all three components E-W, N-S, and U-D within 50 km. In all, the low-cost seismic sensor proposed as a high-performance Class C MEMS sensor can meet the needs of dense EEW in terms of noise, dynamic range, useful resolution, reliability, and detecting capabilities.

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Earthquake Early Warning Using Low-Cost MEMS Sensors

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323438 ◽

2020 ◽

Author(s):

Young-Woo Kwon ◽

Jae-Kwang Ahn ◽

Jimin Lee ◽

Chul-Ho Lee

Keyword(s):

Early Warning ◽

Low Cost ◽

Earthquake Early Warning ◽

Mems Sensors

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Feasibility study of earthquake early warning in Tehran, Iran

Journal of Seismology ◽

10.1007/s10950-021-10014-3 ◽

2021 ◽

Author(s):

S. Enferadi ◽

Z. H. Shomali ◽

A. Niksejel

Keyword(s):

Early Warning ◽

Early Warning System ◽

Warning System ◽

Earthquake Early Warning ◽

Disaster Mitigation ◽

Lead Times ◽

Worst Case ◽

Earthquake Parameters ◽

Earthquake Early Warning System ◽

Earthquake Scenarios

AbstractIn this study, we examine the scientific feasibility of an Earthquake Early Warning System in Tehran, Iran, by the integration of the Tehran Disaster Mitigation and Management Organization (TDMMO) accelerometric network and the PRobabilistic and Evolutionary early warning SysTem (PRESTo). To evaluate the performance of the TDMMO-PRESTo system in providing the reliable estimations of earthquake parameters and the available lead-times for The Metropolis of Tehran, two different approaches were analyzed in this work. The first approach was assessed by applying the PRESTo algorithms on waveforms from 11 moderate instrumental earthquakes that occurred in the vicinity of Tehran during the period 2009–2020. Moreover, we conducted a simulation analysis using synthetic waveforms of 10 large historical earthquakes that occurred in the vicinity of Tehran. We demonstrated that the six worst-case earthquake scenarios can be considered for The Metropolis of Tehran, which are mostly related to the historical and instrumental events that occurred in the southern, eastern, and western parts of Tehran. Our results indicate that the TDMMO-PRESTo system could provide reliable and sufficient lead-times of about 1 to 15s and maximum lead-times of about 20s for civil protection purposes in The Metropolis of Tehran.

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The Extended Integrated Particle Filter Method (IPFx) as a High-Performance Earthquake Early Warning System

Bulletin of the Seismological Society of America ◽

10.1785/0120210008 ◽

2021 ◽

Author(s):

Masumi Yamada ◽

Koji Tamaribuchi ◽

Stephen Wu

Keyword(s):

Particle Filter ◽

Early Warning ◽

Seismic Intensity ◽

Earthquake Early Warning ◽

Japan Meteorological Agency ◽

Estimation Accuracy ◽

Filter Method ◽

Source Estimation ◽

Single Station ◽

Seismic Networks

ABSTRACT An earthquake early warning (EEW) system rapidly analyzes seismic data to report the occurrence of an earthquake before strong shaking is felt at a site. In Japan, the integrated particle filter (IPF) method, a new source-estimation algorithm, was recently incorporated into the EEW system to improve the source-estimation accuracy during active seismicity. The problem of the current IPF method is that it uses the trigger information computed at each station in a specific format as the input and is therefore applicable to only limited seismic networks. This study proposes the extended IPF (IPFx) method to deal with continuous waveforms and merge all Japanese real-time seismic networks into a single framework. The new source determination algorithm processes seismic waveforms in two stages. The first stage (single-station processing) extracts trigger and amplitude information from continuous waveforms. The second stage (network processing) accumulates information from multiple stations and estimates the location and magnitude of ongoing earthquakes based on Bayesian inference. In 10 months of continuous online experiments, the IPFx method showed good performance in detecting earthquakes with maximum seismic intensity ≥3 in the Japan Meteorological Agency (JMA) catalog. By merging multiple seismic networks into a single EEW system, the warning time of the current EEW system can be improved further. The IPFx method provides accurate shaking estimation even at the beginning of event detection and achieves seismic intensity error <0.25 s after detecting an event. This method correctly avoided two major false alarms on 5 January 2018 and 30 July 2020. The IPFx method offers the potential of expanding the JMA IPF method to global seismic networks.

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Robust, an Earthquake Early Warning System in the Lower Rhine Embayment, Germany

10.5194/egusphere-egu21-7997 ◽

2021 ◽

Author(s):

Bita Najdahmadi ◽

Marco Pilz ◽

Dino Bindi ◽

Hoby Njara Tendrisoa Razafindrakoto ◽

Adrien Oth ◽

...

Keyword(s):

Early Warning ◽

Early Warning System ◽

Warning System ◽

Earthquake Early Warning ◽

Optimization Approach ◽

Ground Acceleration ◽

Earthquake Early Warning System ◽

Scenario Earthquakes ◽

Lower Rhine Embayment ◽

Lower Rhine

<p>The Lower Rhine Embayment in western Germany is one of the most important areas of earthquake recurrence north of the Alps, facing a moderate level of seismic hazard in the European context but a significant level of risk due to a large number of important&#160;industrial infrastructures. In this context, the project ROBUST aims at designing a user-oriented hybrid earthquake early warning and rapid response system where regional seismic monitoring is combined with smart, on-site sensors, resulting in the implementation of decentralized early warning procedures.<br><br>One of the research areas of this project deals with finding an optimal regional seismic network arrangement. With the optimally compacted network, strong ground movements can be detected quickly and reliably. In this work simulated scenario earthquakes in the area are used with an optimization approach in order to densify the existing sparse network through the installation of additional decentralized measuring stations. Genetic algorithms are used to design efficient EEW networks, computing optimal station locations and trigger thresholds in recorded ground acceleration. By minimizing the cost function, a comparison of the best earthquake early warning system designs is performed and the potential usefulness of existing stations in the region is considered as&#160;will be presented in the meeting.</p>

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