10:45 AM - 12:15 PM
[SSS12-P08] Contribution to early detection of seismic waves and tsunamis by N-net
Keywords:N-net, Nankai trough, Early detection
The Philippine Sea plate is subducting from the Suruga and Nankai troughs beneath southwest Japan. Great earthquakes occurred repeatedly on the plate interface between the Philippine Sea plate and the overriding plate. In the near future, Nankai trough earthquake will occur and be possible to become nationwide disaster. Early detection of seismic waves and tsunamis by instrument observation is important in order to reduce earthquake and tsunami disaster and one of the advantages of the earthquake and tsunami observation on the seafloor contributes to early detection of them compared to inland observation.
The sea area from the off the Kochi Prefecture to Hyuga-nada, which is correspond to the western half of the focal region of the anticipated Nankai Trough earthquake, is a blank area of the seafloor earthquake and tsunami observation networks. In 2019, the National Research Institute for Earth Science and Disaster Resilience started a project to develop the Nankai Trough Seafloor Observation Network for Earthquakes and Tsunamis (N-net) in this blank area. The N-net has two systems, a coastal system and an offshore system, to ensure redundancy, with 18 observation nodes in each system (e.g., Aoi et al., 2020). In this study, we investigated how fast the direct detection of seismic waves and tsunamis can be achieved when N-net data can be acquired.
The travel time calculation of seismic waves was performed using the one-dimensional seismic wave velocity structure (Ukawa et al., 1984). The hypocenters were assumed at a depth of 15 km and every 0.1 degree of latitude and longitude. The minimum travel time to reach a station was searched to each hypocenter. As a result, we found the detection of seismic wave was expected to be earlier approximately 20 seconds at the maximum considering the N-net data.
The tsunami travel time was calculated using ETOP1 for the seafloor topography. The tsunami wave source was assumed to be a point source, and was placed at every 0.1 degrees of latitude and longitude. The minimum travel time to reach a certain station was searched to each source as well as the case of the seismic wave. As a result, we found the direct detection of tsunami was expected to be earlier approximately 20 minutes at the maximum considering the N-net data.
The sea area from the off the Kochi Prefecture to Hyuga-nada, which is correspond to the western half of the focal region of the anticipated Nankai Trough earthquake, is a blank area of the seafloor earthquake and tsunami observation networks. In 2019, the National Research Institute for Earth Science and Disaster Resilience started a project to develop the Nankai Trough Seafloor Observation Network for Earthquakes and Tsunamis (N-net) in this blank area. The N-net has two systems, a coastal system and an offshore system, to ensure redundancy, with 18 observation nodes in each system (e.g., Aoi et al., 2020). In this study, we investigated how fast the direct detection of seismic waves and tsunamis can be achieved when N-net data can be acquired.
The travel time calculation of seismic waves was performed using the one-dimensional seismic wave velocity structure (Ukawa et al., 1984). The hypocenters were assumed at a depth of 15 km and every 0.1 degree of latitude and longitude. The minimum travel time to reach a station was searched to each hypocenter. As a result, we found the detection of seismic wave was expected to be earlier approximately 20 seconds at the maximum considering the N-net data.
The tsunami travel time was calculated using ETOP1 for the seafloor topography. The tsunami wave source was assumed to be a point source, and was placed at every 0.1 degrees of latitude and longitude. The minimum travel time to reach a certain station was searched to each source as well as the case of the seismic wave. As a result, we found the direct detection of tsunami was expected to be earlier approximately 20 minutes at the maximum considering the N-net data.