5:15 PM - 6:45 PM
[MTT37-P02] Estimation of slip distribution of the 2024 Noto Peninsula earthquake based on dense GNSS observation network
Keywords:The 2024 Noto Peninsula earthquake, GNSS, Slip distribution estimation, Bayesian inversion, Hamiltonian Monte Carlo
The dense GNSS observation network, developed and utilized in various fields of earth sciences in recent years, can acquire more detailed crustal deformation fields in the epicenter region during earthquakes. The data is expected to be utilized for fault modeling with complex geometry and detailed slip distribution estimation on that coseimsic fault.
This presentation estimates the coseismic slip distribution based on the dense GNSS observation network for the 2024 Noto Peninsula earthquake. The region has been experiencing active seismic swarms since the end of 2020, and earthquakes of Mw 5.2 and 6.2 occurred in the previous year and last year, respectively, making it possible to use a dense observation near the epicenter, including temporal observation. In addition to the surface displacement data obtained by the Geospatial Information Authority of Japan and the temporal GNSS stations by Kyoto University and Kanazawa University, displacement data acquired near the epicenter by the GNSS stations installed by SoftBank Corp. were used for the analysis. Two rectangular faults were placed in the northern Noto Peninsula. The sea area northeast of the Noto Peninsula corresponds to the aftershock distribution and the known fault segments, and their slip distribution was estimated by Bayesian inverse analysis.
The results of the Bayesian inverse analysis indicate a 2-3 peak slip distribution dominated by reverse fault slip with a small amount of right lateral slip (Mw~7.5). On the southwestern side of the fault, which covers the entire northern Noto Peninsula, a prominent slip zone was estimated just below the north of coastal areas of Suzu City and Wajima City. This corresponds to the significant uplift in both areas suggested by SAR analysis and field surveys. On the other hand, a small reverse fault slip of about 2 m was also estimated on the northeast side of the fault. However, it should be noted that the sensitivity of the land-based observation network for this fault is lower than that for the southwest fault, and the posterior distribution has a large confidence interval (the 60% confidence interval > 5 m).
In addition, to evaluate the effect of using SoftBank stations on the model estimation, we also performed the estimation using only GEONET and temporary observation networks. As a result, we obtained a model with reduced slip in the western part of the southwestern fault, although the global characteristics were consistent. This may be attributed to the small density of the observation network in this area. In addition, the strength of the spatial smoothing of slip simultaneously estimated increased, and the confidence interval of the posterior distribution of slip increased along the entire fault. These results suggest that a dense and uniform GNSS observation network plays an essential role in accurately understanding the characteristics of coseismic slips.
Acknowledgments: The SoftBank's GNSS observation data used in this study was provided by SoftBank Corp. and ALES Corp. through the framework of the "Consortium to utilize the SoftBank original reference sites for Earth and Space Science.”
This presentation estimates the coseismic slip distribution based on the dense GNSS observation network for the 2024 Noto Peninsula earthquake. The region has been experiencing active seismic swarms since the end of 2020, and earthquakes of Mw 5.2 and 6.2 occurred in the previous year and last year, respectively, making it possible to use a dense observation near the epicenter, including temporal observation. In addition to the surface displacement data obtained by the Geospatial Information Authority of Japan and the temporal GNSS stations by Kyoto University and Kanazawa University, displacement data acquired near the epicenter by the GNSS stations installed by SoftBank Corp. were used for the analysis. Two rectangular faults were placed in the northern Noto Peninsula. The sea area northeast of the Noto Peninsula corresponds to the aftershock distribution and the known fault segments, and their slip distribution was estimated by Bayesian inverse analysis.
The results of the Bayesian inverse analysis indicate a 2-3 peak slip distribution dominated by reverse fault slip with a small amount of right lateral slip (Mw~7.5). On the southwestern side of the fault, which covers the entire northern Noto Peninsula, a prominent slip zone was estimated just below the north of coastal areas of Suzu City and Wajima City. This corresponds to the significant uplift in both areas suggested by SAR analysis and field surveys. On the other hand, a small reverse fault slip of about 2 m was also estimated on the northeast side of the fault. However, it should be noted that the sensitivity of the land-based observation network for this fault is lower than that for the southwest fault, and the posterior distribution has a large confidence interval (the 60% confidence interval > 5 m).
In addition, to evaluate the effect of using SoftBank stations on the model estimation, we also performed the estimation using only GEONET and temporary observation networks. As a result, we obtained a model with reduced slip in the western part of the southwestern fault, although the global characteristics were consistent. This may be attributed to the small density of the observation network in this area. In addition, the strength of the spatial smoothing of slip simultaneously estimated increased, and the confidence interval of the posterior distribution of slip increased along the entire fault. These results suggest that a dense and uniform GNSS observation network plays an essential role in accurately understanding the characteristics of coseismic slips.
Acknowledgments: The SoftBank's GNSS observation data used in this study was provided by SoftBank Corp. and ALES Corp. through the framework of the "Consortium to utilize the SoftBank original reference sites for Earth and Space Science.”