Probabilistic tsunami hazard assessment based on the Gutenberg–Richter law in eastern Shikoku, Nankai subduction zone, Japan

Abstract

Earthquake and tsunami predictions comprise huge uncertainties, thus necessitating probabilistic assessments for the design of defense facilities and urban planning. In recent years, computer development has advanced probabilistic tsunami hazard assessments (PTHAs), where hazard curves show the exceedance probability of the maximum tsunami height. However, owing to the lack of historical and geological tsunami records, this method is generally insufficient for validating the estimated hazard curves. The eastern coast of Shikoku in the Nankai subduction zone, Japan, is suitable for validation because tsunami records from historical Nankai Trough earthquakes are available. This study evaluated PTHAs by comparing the tsunami hazard curves and exceedance frequencies of historical Nankai Trough tsunamis. We considered 3480 earthquake scenarios representing the rupture patterns of past Nankai earthquakes and calculated all tsunamis. The probability of earthquake occurrence was based on the Gutenberg–Richter law. We considered uncertainty in tsunami calculations with astronomical tide variations. The estimated tsunami hazard curves are consistent with the exceedance frequencies obtained from historical tsunamis. In addition, sensitivity tests indicate the significance of the earthquake slip heterogeneity and tsunami defense facilities in PTHAs. We also extended the PTHAs to tsunami inundation maps in high resolution and proposed an effective new method for reducing the tsunami computation load. Graphical Abstract

Tsunami Height Estimation by Gaussian Process Regression Using Maximum Pressure Change and Arrival Time at Seafloor Sensors off the Kii Peninsula, Japan

“…In this study, we proposed a method to predict coastal tsunami heights based on Gaussian process regression using DONET1 and DONET2 (Kaneda et al, 2015a) seafloor pressures and arrival times as explanatory variables. We verified the effectiveness of the proposed method by using a 3480-scenario simulation dataset (Baba et al, 2022). Compared to the conventional method (Igarashi et al, 2016) in which only seafloor pressure is used as an explanatory variable, the proposed method using both seafloor pressure and arrival time improved the prediction accuracy for all coastal cities; the RMSE average for the 19 cities in the DONET region was nearly halved, from RMSE = 0.13 m to RMSE = 0.07 m. The estimation results based on explanatory variables with added Gaussian noise in ranges up to 100 hPa and 40 s also indicated improved prediction accuracy when arrival times were considered.…”

“…For learning and validation of the Gaussian process regression, we constructed a precomputed tsunami database for the Nankai Trough subduction zone using a method similar to that described by Baba et al (2022), but differing with regard to the nested gridding system used for bathymetry/topography. Baba et al (2022) focused on tsunami characteristics in eastern Shikoku and used a high-resolution (10-m) grid for the region, whereas because our focus in this study was the entire coastal region of southwestern Japan facing the Nankai trough, we used a 50-m grid interval. Otherwise, the tsunami calculation method used here is identical to that used by Baba et al (2022).…”

“…Baba et al (2022) focused on tsunami characteristics in eastern Shikoku and used a high-resolution (10-m) grid for the region, whereas because our focus in this study was the entire coastal region of southwestern Japan facing the Nankai trough, we used a 50-m grid interval. Otherwise, the tsunami calculation method used here is identical to that used by Baba et al (2022). We assumed a total of 3480 interplate earthquakes (Fujiwara et al, 2020;Dohi et al, 2022) in the Nankai trough area with magnitudes ranging from Mw 7.6 to 9.1.…”