We proposed the procedure for evaluating the parameters of characterized fault model for predicting long-period ground motions containing permanent displacement in the near-fault region of the 2016 Kumamoto earthquake. In this paper, we simulate the strong ground motions in the near-fault region of the 2023 Turkey-Syria Earthquakes by the characterized fault model based on the proposed procedure. In order to reproduce permanent displacement, it was necessary to set the fault geometry in such a way that the positional relationship between the active fault and the observation points was correct. In addition, because a portion of the fault surface had to be a super shear rupture, we investigated the effect of rupture velocity on the strong-motion prediction results.

In this paper, we measure the effects of "fixing machinery and equipment" and "reinforcing buildings against earthquakes" on the production capacity of firms immediately after the 2016 Kumamoto earthquake and the 2022 Fukushima offshore earthquake occurred. To measure the effect of countermeasures, we compiled a dataset for both groups by adjusting covariates for the treated group and the untreated group by matching using balancing scores and Mahalanobis distance. The difference in the occurrence probability of the production capacity state of these groups, obtained from the functional fragility curves, was used as the effect of the measures. The results showed that "fixing machinery and equipment" was highly effective in the "manufacturing" and "services (in-store or in-office)" sectors, reducing the occurrence probability of loss of production capacity state by approximately 4.9 –12.9 % for an earthquake equivalent to JMA intensity 6 or higher. In the case of "building seismic reinforcement," the effect of the countermeasures tended to increase as the seismic ground motion increased. The results of this study are expected to be applied to economic damage estimation, seismic risk management, and risk finance, reflecting the effects of countermeasures.

Earthquakes are among the most immediate and deadly natural disasters faced by humans. Accurate prediction of the extent of earthquake damage and assessment of potential risks can save numerous lives. In this study, we developed a hybrid model combining classification and regression models, capable of predicting seismic intensity distributions based on the following earthquake parameters: location, depth, and magnitude. As these models are completely data-driven, they can predict seismic intensity distributions without geographic information. The dataset comprises seismic intensity data from earthquakes that occurred in the vicinity of Japan between 1997 and 2020. It includes 1,857 instances of seismic intensity data for earthquakes with a magnitude of 5.0 or greater, sourced from the Japan Meteorological Agency. Regression and classification models were trained, then combined to take advantage of each other and create a hybrid model. The proposed model outperformed commonly used ground-motion prediction equations (GMPEs) in terms of the correlation coefficient, F1 score, and MCC. Furthermore, the proposed model can predict abnormal seismic intensity distributions, a task that conventional GMPEs often struggle to achieve.

We first summarize the relationship between the JMA magnitude M_JMA published by the Japan Meteorological Agency and the seismic moment M₀, or the moment magnitude M_W, published by the National Research Institute for Earth Science and Disaster Resilience for earthquakes that occurred in Japan between 1988 and 2024. We report that M_JMA and M_W are equal for plate boundary earthquakes and intra-plate earthquakes, while there is a magnitude-independent noticeable difference only for inland crustal earthquakes. Next, we show how the proposed conversion equation differs from the empirical formulae that have been widely used to obtain M₀ from M_JMA and M₀ or M_JMA from the source length L. Then, by combining the proposed conversion equations with the existing scaling laws for the source area S, we extend the scaling laws for the source length L to be valid over a wide magnitude or M₀ range and show that it is well consistent with recent observations.

The 2024 Noto Peninsula Earthquake on January 1, 2024, caused extensive ground damage, including landslides and liquefaction. In Uchinada Town, Ishikawa Prefecture, which is located on the sand dune, liquefaction caused large ground displacements with sand boils, cracks, subsidence and uplift. Roads, lifelines, buildings and houses were severely damaged. The authors measured the ground displacements by 3D survey using aerial photograph and discussed the effect of surface gradient on the displacements.