1:45 PM - 2:00 PM
[SSS10-01] The Extended PLUM Method to Expand the Reach of the Ground Motion Estimation:
a Case Study of the 2023 Türkiye-Syria Earthquake
Keywords:the 2023 Türkiye-Syria earthquake, Early Earthquake Warning, the PLUM method, Delaunay Triangulation
The Japanese Early Earthquake Warning system uses two methods. One of these is the PLUM (Propagation of Local Undamped Motion) method, which estimates seismic intensity without estimating epicenter or seismic magnitude, only using neighborhood intensity observations. Therefore, this method especially works for large earthquakes with large fault areas. In the current method, observation stations within 30 km of each target point are used for estimation, which requires a denser observation network than 30 km intervals. Many other countries do not facilitate such high-density observation. Therefore, we design a new scheme of the PLUM method that accommodates sparser observation networks.
In this study, we focused on the 2023 Türkiye-Syria Earthquake (Mw 7.8), which was caused by a long fault; therefore, the PLUM method is potentially profitable for such a case. This earthquake occurred at 01:17:34 UTC on 6 February 2023, the epicenter was located at 37.226 °N, 37.014°E and the depth was 10.0 km. We used available 249 data of seismometers in the time range from 01:17:05 to 01:26:20. For this earthquake, firstly, we experimentally applied the existing scheme of the PLUM method, and secondly, we installed an improved scheme.
In the existing scheme of the PLUM method, by using seismic intensities recorded at observation stations in radius R = 30 km around the target point and ground characteristics of the region, we calculate intensities of ground motion propagated from each observation station to the target point, and then take the maximum value as the predicted seismic intensity. However, in this case, 74 stations (27 %) have no neighbor station in a radius of 30 km, and 87 (32 %) have only one, which means lack of coverage. Therefore, we initially simulate with R varying from 10 km to 90 km with a step of 5 km. Besides, as for seismic intensity, we applied to these data a calculation method of JMA Real-time Seismic Intensity, same as the Japanese earthquake early warning system.
Subsequently, we devised an extended scheme of the PLUM method in which we flexibly select the reference area without fixing R: the scheme utilizing the Delaunay Triangulation. Firstly, we enumerate all observation stations in radius R1 around the target point. Secondly, in order of closeness, we enumerate other observation stations that are in radius R2 (> 2R1) around the target point and share a Delaunay Edge with the target point — until we count N or more stations and the Max Gap Angle < θ. Finally, by using seismic intensities recorded at these stations, we predict seismic intensities likewise.
The extended selection method, which we propose, has the feature that some usable neighbor stations around each target station are ensured. Moreover, in comparison with the existing scheme, we obtained a longer median leading time and less error between observed and predicted seismic intensity. Also, we found a trade-off between prediction error and leading time.
The extended selection method, allowing the distance from the target station to change, can optionally set the number of usable neighbor stations. By utilizing this characteristic, we can choose to emphasize prediction error or leading time depending on the purpose. Overall, the extended selection method (utilizing Delaunay Triangulation) brought better results than the existing selection method (fixing distance).
In this study, we focused on the 2023 Türkiye-Syria Earthquake (Mw 7.8), which was caused by a long fault; therefore, the PLUM method is potentially profitable for such a case. This earthquake occurred at 01:17:34 UTC on 6 February 2023, the epicenter was located at 37.226 °N, 37.014°E and the depth was 10.0 km. We used available 249 data of seismometers in the time range from 01:17:05 to 01:26:20. For this earthquake, firstly, we experimentally applied the existing scheme of the PLUM method, and secondly, we installed an improved scheme.
In the existing scheme of the PLUM method, by using seismic intensities recorded at observation stations in radius R = 30 km around the target point and ground characteristics of the region, we calculate intensities of ground motion propagated from each observation station to the target point, and then take the maximum value as the predicted seismic intensity. However, in this case, 74 stations (27 %) have no neighbor station in a radius of 30 km, and 87 (32 %) have only one, which means lack of coverage. Therefore, we initially simulate with R varying from 10 km to 90 km with a step of 5 km. Besides, as for seismic intensity, we applied to these data a calculation method of JMA Real-time Seismic Intensity, same as the Japanese earthquake early warning system.
Subsequently, we devised an extended scheme of the PLUM method in which we flexibly select the reference area without fixing R: the scheme utilizing the Delaunay Triangulation. Firstly, we enumerate all observation stations in radius R1 around the target point. Secondly, in order of closeness, we enumerate other observation stations that are in radius R2 (> 2R1) around the target point and share a Delaunay Edge with the target point — until we count N or more stations and the Max Gap Angle < θ. Finally, by using seismic intensities recorded at these stations, we predict seismic intensities likewise.
The extended selection method, which we propose, has the feature that some usable neighbor stations around each target station are ensured. Moreover, in comparison with the existing scheme, we obtained a longer median leading time and less error between observed and predicted seismic intensity. Also, we found a trade-off between prediction error and leading time.
The extended selection method, allowing the distance from the target station to change, can optionally set the number of usable neighbor stations. By utilizing this characteristic, we can choose to emphasize prediction error or leading time depending on the purpose. Overall, the extended selection method (utilizing Delaunay Triangulation) brought better results than the existing selection method (fixing distance).