&lt;b&gt;Earthquake Early Warning Using Ocean Bottom Seismic Data for Railways&lt;/b&gt;

Korenaga, Masahiro; Yamamoto, Shunroku; Noda, Shunta; Aoi, Shin

doi:10.2219/rtriqr.60.2_134

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Furthermore, S-net and DONET data are applied to control Shinkansen trains for disaster prevention of social infrastructure for the private sectors. Based on signed agreements between NIED and three JR companies, NIED provides several indices of acceleration and real-time seismic intensity, and significantly increases lead time to stop Shinkansen trains using an alarm method based on a threshold value being exceeded developed by Korenaga et al (2019a). The calculations are done in situ at the landing stations of each cable system for S-net and at the NIED DMC for DONET, and then directly transmitted to the JR companies (Korenaga et al 2019b).…”

Section: Seafloor Observations Utilization By Local Governments and Pmentioning

confidence: 99%

“…Based on signed agreements between NIED and three JR companies, NIED provides several indices of acceleration and real-time seismic intensity, and significantly increases lead time to stop Shinkansen trains using an alarm method based on a threshold value being exceeded developed by Korenaga et al (2019a). The calculations are done in situ at the landing stations of each cable system for S-net and at the NIED DMC for DONET, and then directly transmitted to the JR companies (Korenaga et al 2019b). Seafloor observatories are installed away from railways, so it is necessary to consider the attenuation that occurs with distance and the site amplification that is related to seafloor stations located on soft sedimentary layers.…”

Section: Seafloor Observations Utilization By Local Governments and Pmentioning

confidence: 99%

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MOWLAS: NIED observation network for earthquake, tsunami and volcano

Aoi

Asano

Kunugi

et al. 2020

Earth Planets Space

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197

118

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National Research Institute for Earth Science and Disaster Resilience (NIED) integrated the land observation networks established since the 1995 Kobe earthquake with the seafloor observation networks established since the 2011 Tohoku earthquake and tsunami as MOWLAS (Monitoring of Waves on Land and Seafloor) in November 2017. The purpose of MOWLAS is to provide comprehensive, accurate, and rapid observation and monitoring of earthquake, tsunami, and volcano events throughout Japan and its offshore areas. MOWLAS data are widely utilized for long-term earthquake forecasting, the monitoring of current seismic activity, seismic and tsunami hazard assessments, earthquake early warning, tsunami warning, and earthquake engineering, as well as earthquake science. Ocean bottom observations provide an extension of observations to areas where no people are living and have the advantage of increasing lead time of earthquake early warning and tsunami warning. The application of recent technology advancements to real-time observations as well as the processing of MOWLAS data has contributed to the direct disaster mitigation of ongoing earthquakes. These observations are fundamental for both science and disaster resilience, and thus it is necessary to continue ceaseless operation and maintenance.

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Section: Seafloor Observations Utilization By Local Governments and Pmentioning

confidence: 99%

Section: Seafloor Observations Utilization By Local Governments and Pmentioning

confidence: 99%

MOWLAS: NIED observation network for earthquake, tsunami and volcano

Aoi

Asano

Kunugi

et al. 2020

Earth Planets Space

Self Cite

197

118

View full text Add to dashboard Cite

show abstract

“…The system also employs EEW alerts published from the Japan Meteorological Agency (JMA) to enhance its redundancy [2]. Recently, to increase the rapidness of warnings for earthquakes that occur in offshore areas, the railway companies started to receive the ocean bottom seismic (OBS) data observed by the Seafloor Observation Network for Earthquakes and Tsunamis along the Japan Trench (S-net) and the Dense Ocean-floor Network System for Earthquakes and Tsunamis (DONET) from the National Research Institute for Earth Science and Disaster Resilience (NIED) [3,4]. Figure 1 shows the distribution of S-net and DONET seismic stations.…”

Section: Introductionmentioning

confidence: 99%

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

Noda

Iwata

2023

QR of RTRI

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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.

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Deep Learning Estimating of Epicentral Distance for Earthquake Early Warning Systems

Noda

2024

Bulletin of the Seismological Society of America

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To enhance the performance of earthquake early warning (EEW) systems that aim to issue alerts as quickly as possible, it is crucial to improve the accuracy of the epicentral distance Δ estimated via the single-station method. Although the conventional method estimates Δ from the slope of the initial P-wave envelope, this study applies deep learning techniques that can extract a variety of information from the waveform data. By analyzing ∼20,000 records observed at Kyoshin Network stations in Japan, the convolutional neural network (CNN) method achieved higher accuracy than the conventional method. Increasing the data length or the number of iterations of convolution, activation, and pooling layers in the typical CNN model did not significantly improve the accuracy of Δ estimation. An automatic structure search (AutoSS) technique, in which model structure and hyperparameters are randomly varied, was employed to identify models that yield higher accuracy. A typical CNN model was used as the initial structure. The models obtained through this technique showed improved accuracy with increased data length or computational cost. The models that delivered the highest accuracy among those generated using the AutoSS technique outperformed the typical CNN model in terms of accuracy, although their computational costs were comparable. The AutoSS technique offers a significant advantage in that it allows the selection of a model that matches the computational capabilities of the hardware used for its implementation, thereby ensuring optimal computational efficiency. This exhibits that deep learning technologies can be used to improve the performance of EEW systems.

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

Cited by 4 publications

References 7 publications

MOWLAS: NIED observation network for earthquake, tsunami and volcano

MOWLAS: NIED observation network for earthquake, tsunami and volcano

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

Deep Learning Estimating of Epicentral Distance for Earthquake Early Warning Systems

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