Traditional and modified Newmark displacement methods after the 2022 Ms 6.8 Luding earthquake (Eastern Tibetan Plateau)

peer reviewedThe Newmark displacement (ND) method, which reproduces the interactions between waves, solids, and fluids during an earthquake, has experienced numerous modifications. We compare the performances of a traditional and a modified version of the ND method through the analysis of co-seismic landslides triggered by the 2022 Ms 6.8 Luding earthquake (Sichuan, China). We implemented 23 ND scenarios with each equation, assuming different landslide depths, as well as various soil-rock geomechanical properties derived from previous studies in regions of similar lithology. These scenarios allowed verifying the presence or absence of such landslides and predict the likely occurrence locations. We evaluated the topographic and slope aspect amplification effects on both equations. The oldest equation has a better landslide predictive ability, as it considers both slope stability and earthquake intensity. Contrarily, the newer version of the ND method has a greater emphasis on slope stability compared to the earthquake intensity and hence tends to give high ND values only when the critical acceleration is weak. The topographic amplification does not improve the predictive capacity of these equations, most likely because few or no massive landslides were triggered from mountain peaks. This approach allows structural, focal mechanism, and site effects to be considered when designing ND models, which could help to explain and predict new landslide distribution patterns such as the abundance of landslides on the NE, E, S, and SE-facing slopes observed in the Luding case

Effect of Soil Geomechanical Properties and Geo-Environmental Factors on Landslide Predisposition at Mount Oku, Cameroon

In this work, we explored a novel approach to integrate both geo-environmental and soil geomechanical parameters in a landslide susceptibility model. A total of 179 shallow to deep landslides were identified using Google Earth images and field observations. Moreover, soil geomechanical properties of 11 representative soil samples were analyzed. The relationship between soil properties was evaluated using the Pearson correlation coe cient and geotechnical diagrams. Membership values were assigned to each soil property class, using the fuzzy membership method. The information value method allowed computing the weight value of geo-environmental factor classes. From the soil geomechanical membership values and the geo-environmental factor weights, three landslide predisposition models were produced, two separate models and one combined model. The results of the soil testing allowed classifying the soils in the study area as highly plastic clays, with high water content, swelling, and shrinkage potential. Some geo-environmental factor classes revealed their landslide prediction ability by displaying high weight values. While the model with only soil properties tended to underrate unstable and stable areas, the model combining soil properties and geo-environmental factors allowed a more precise identification of stability conditions. The geo-environmental factors model and the model combining geo-environmental factors and soil properties displayed predictive powers of 80 and 93%, respectively. It can be concluded that the spatial analysis of soil geomechanical properties can play a major role in the detection of landslide prone areas, which is of great interest for site selection and planning with respect to sustainable development at Mount Oku