A global geospatial liquefaction model (GGLM‐2017) was previously developed (Zhu et al., 2017) using logistic regression (LR) and is currently used by the U.S. Geological Survey as the preferred liquefaction model to map liquefaction probability immediately after the occurrence of earthquake events. This research proposes an ensemble modeling approach to improve the performance of the GGLM‐2017 for geospatial liquefaction modeling of the 2023 Türkiye earthquakes using an updated inventory of liquefaction occurrence locations in Europe (the OpenLIQ database, which includes prior events occurring in Türkiye) and a new inventory from the 2023 Türkiye earthquakes (gathered from multiple sources). Using the same geospatial proxies for soil saturation, soil density, and earthquake loading, and the same non‐liquefaction sampling strategy used to develop GGLM‐2017, the proposed ensemble method is validated on the data of the 2023 Türkiye earthquakes by integrating four models, including global (GGLM‐2017), continental (LR model trained on eight European events), regional (LR model trained on three historical events in Türkiye), and event‐specific (LR model trained on partially available data from the 2023 Türkiye earthquakes) models. The inventory from the 2023 Türkiye earthquakes is split into two batches, in which the first batch (163 liquefaction occurrences) resulted from the preliminary reconnaissance and is used for training the event‐specific model, and the second batch (284 liquefaction occurrences) resulted from a more complete reconnaissance (which was made available later) and is used for validating all models. The rationale for using the first batch for training the event‐specific model is to exploit the information as they become available to optimize the performance of global model in liquefaction prediction. The final ensemble probability assignment is done by averaging the probabilities derived by the four individual models, and a 50% threshold is used for classification accuracy evaluations. Comparative analysis of the ensemble model’s performance with the GGLM‐2017 showed improved predictive accuracy, resulting in higher liquefaction detection for the specific event under study (the 2023 Türkiye earthquakes). The ensemble model also provides an estimate of model uncertainty.

At 3:08 on 6 September 2018 (UTC +9), massive landslides were triggered by an earthquake of Mw 6.6 that occurred in Hokkaido, Japan. In this paper, a coseismic landslide inventory that covers 388 km² of the earthquake-impacted area and includes 5828 coseismic landslides with a total landslide area of 23.66 km² was compiled by using visual interpretations of various high-resolution satellite images. To analyze the spatial distribution and characteristics of coseismic landslides, five factors were considered: the peak ground acceleration (PGA), elevation, slope gradient, slope aspect, and lithology. Results show more than 87% of the landslides occurred at 100 to 200 m elevations. Slopes in the range of 10~20°are the most susceptible to failure. The landslide density of the places with peak ground acceleration (PGA) greater than 0.16 g is obviously larger than those with PGA less than 0.02 g. Compared with the number and scale of coseismic landslides caused by other strong earthquakes and the mobility of the coseismic landslides caused by the Haiyan and Wenchuan earthquakes, it was found that the distribution of coseismic landslides was extremely dense and that the mobility of the Hokkaido earthquake was greater than that of the Wenchuan earthquake and weaker than that of the Haiyuan earthquake, and is described by the following relationship: L = 18.454 ∗ H^0.612. Comparative analysis of coseismic landslides with similar magnitude has important guiding significance for disaster prevention and reduction and reconstruction planning of landslides in affected areas.

The dynamic penetration test (DPT) and the Menard pressuremeter test (PMT) have been widely used in geotechnical survey of deep soils for megadam foundations in western China. The DPT measures are not well utilized due to the lack of correction factors and of empirical relationships for deep soils. This study investigates the relationships between the corrected DPT blow counts (), pressuremeter modulus (E_PMT), limit pressure (p_L), deformation modulus (E₀), and bearing capacity (f_ak) derived from the PMT results. Meanwhile, a nonlinear regression model is developed to predict the DPT correction factor (a) based on the raw DPT blow counts (N₁₂₀) and the rod length (L) by integrating the available correction factors for shallow gravelly soils suggested by the code provisions and the deep soil data in this work. It is concluded that the proposed DPT correction factors match well with the code suggestions and the new compiled dataset, and the corrected DPT blow counts can be used to reasonably predict the engineering properties of deep sand and clay soils. Although the proposed correlations need to be tested among different soil types and regions, the results shed the light on in situ geotechnical tests and data utilization for deep and thick overburden.