Determination of Earthquake Magnitude for Early Warning from the Time Dependence of
 P
 ‐Wave Amplitudes

Noda, Shunta; Ellsworth, William L.

doi:10.1785/0120170048

Cited by 8 publications

(18 citation statements)

References 23 publications

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“…3, contains 8965 recordings for seismic stations within 200 km of the epicenter of 195 Japanese earthquakes, of magnitudes between 4.5 and 9. Although damaging earthquakes can occur at longer distances [17], the relationship between the growth amplitude parameter and Δ does not exhibit significant correlation beyond 200 km [8]. The peak number of entries occur around 80 km and there are few entries in the range 0-40 km.…”

Section: Earthquake Datasetmentioning

confidence: 91%

Improved Epicentral Distance Estimations for Railway Earthquake Early Warning

et al. 2020

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Accurate, rapid estimation of epicentral distance, Δ, is essential for earthquake early warning systems. To improve Δ estimation, new methods to calculate the empirical relationship between the amplitude growth rate parameter, C, and Δ are investigated. Using orthogonal regression is most appropriate for application to EEW systems for Japanese high-speed trains. Evaluation using a K-NET dataset, of earthquake epicenters up to 200 km from the recording station, showed that the proposed method reduces maximum error from 546.2 km to 209.8 km. The percentage of correct estimations, defined as estimates within ±30% of the measured epicentral distance, is increased from 38.0% to 55.3%.

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Section: Earthquake Datasetmentioning

confidence: 91%

Improved Epicentral Distance Estimations for Railway Earthquake Early Warning

et al. 2020

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“…The conventional EEW system for railways employs Pand S-wave warning methods [1]. The P-wave warning estimates epicenter location [e.g., 5,6] and magnitude [e.g., 7,8] from the initial P-wave waveform and determines the area of potential damage using the M-Δ diagram [1,4]. Although the Pwave warning has the advantage of speed, its accuracy relies on having a clear picture of seismic and noise data characteristics.…”

Section: On-site Algorithmmentioning

confidence: 99%

Earthquake Early Warning Using Ocean Bottom Seismic Data for Railways

et al. 2019

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This article explains processes for improving the railway earthquake early warning system. These processes were introduced to use data transmitted in real time from recently developed ocean bottom seismic networks. Three mechanisms were designed to be able to exploit this data: 1) an algorithm for servers in OBS system base stations; 2) a procedure to allow transmissions between the servers and railway company receivers; and 3) a system built into the receiver to determine whether running trains need to be stopped or not. Confirmation was obtained that the proposed processes were able to reduce the risks to railways from earthquakes, because they are capable of extending the lead time before the arrival of strong seismic motion.

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“…As mentioned above, however, M estimates from displacement amplitude are more accurate than those from the contents of predominant periods which must have connection with T dp [4]. Considering this problem, Noda & Ellsworth [29] proposed a new method to upgrade the GMPE approach based on the scaling relation. In the method, the intercept of GMPE is determined using only the amplitude data that have passed T dp .…”

Section: Application Of the Scaling Relation To Earthquake Early Warningmentioning

confidence: 99%

“…In the method, the intercept of GMPE is determined using only the amplitude data that have passed T dp . As a result, the intercept is time-dependent even though it is conventionally constant (see the paper [29] for more detail). Noda & Ellsworth [29] demonstrated that the method was effective in improving the speed of the GMPE approach without the loss of accuracy of M. We conclude that this method will be also useful for the Shinkansen EEW system in order to improve the safety of the railways while earthquakes are still underway [1].…”

Section: Application Of the Scaling Relation To Earthquake Early Warningmentioning

confidence: 99%

Statistical Characterization of P-wave Growth for Earthquake Early Warning

Noda

2018

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This article introduces a statistical relation between earthquake magnitude (M) and a characteristic measured from initial P-wave displacement records, which demonstrates that the final M can be inferred before the peak amplitude arrival (i.e., earthquake rupture completion) in the range of M < 7. Because it is unknown why and how the relation is established, we propose a model for explaining its physical background. We also discuss the application of the relation to earthquake early warning, which can help improve train running safety because M can be determined faster than with the conventional method while an earthquake rupture is underway.

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Determination of Earthquake Magnitude for Early Warning from the Time Dependence of P ‐Wave Amplitudes

Cited by 8 publications

References 23 publications

<b>Improved Epicentral Distance Estimations for Railway Earthquake Early Warning</b>

<b>Improved Epicentral Distance Estimations for Railway Earthquake Early Warning</b>

<b>Earthquake Early Warning Using Ocean Bottom Seismic Data for Railways</b>

<b>Statistical Characterization of P-wave Growth for Earthquake Early Warning</b>

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