30. Accuracy enhancement of Earthquake Early Warning for railways using optical sensing technology
We proposed the use of optical sensing technology (Distributed Acoustic Sensing: DAS) to improve the immediacy of Earthquake Early Warning (EEW) and the accuracy of hypocenter determination.
We established an earthquake monitoring network by applying DAS to existing optical fiber cables on Shinkansen structures.
This allowed us to confirm that strain waveforms in the optical fiber cables, reflecting the type of structure and ground shaking susceptibility, can be obtained with high density (every five meters) during earthquakes (Figure 1).
Additionally, we proposed a new method to reconstruct earthquake waveforms at each location, which previously had large errors with optical measurement technologies.
This method uses the ratio of site characteristics between locations and phase information obtained from accurate waveforms in the vicinity. We calculated the maximum strain at the crown of the structure of the Magnitude 6.6 earthquake. Thus, we confirmed that DAS allows for obtaining high-density and accurate seismic waveforms along railway lines. Furthermore, to enhance the accuracy of EEW for railways,
we have developed a method for determining the hypocenter using real-time data from multiple DAS observation points. This method enables determining the epicenter with high accuracy (epicenter location error of less than a few kilometers) and speed (within 1 second after detecting the primary wave), compared to existing methods using data from a single observation point (which have an epicentral distance error that can be between half and double the actual distance).
This improvement allows for a reduction in false alarms and the issuance of warnings within appropriate ranges, thereby contributing to enhanced safety and stability of railway transportation (Figure 2).
Other Contents
- 27. Simulation of seismic behavior of a train set
- 28. Estimation method for factors contributing to decision errors by train forward surveillance AI
- 29. Estimation method for wear mode of current collection materials with the consideration of sliding history
- 30. Accuracy enhancement of Earthquake Early Warning for railways using optical sensing technology
- 27. Simulation of seismic behavior of a train set
- 28. Estimation method for factors contributing to decision errors by train forward surveillance AI
- 29. Estimation method for wear mode of current collection materials with the consideration of sliding history
- 30. Accuracy enhancement of Earthquake Early Warning for railways using optical sensing technology