Correlations of SMR-Qslope Data in Stability Classification of Discontinuous Rock Slope: A Modified Relationship Considering the Iranian Data

Geotechnical and Geological Engineering - The preliminary responses on stability assessment are very effective in discontinuous rock slope stabilisations, which geo-mechanical empirical...     

A micro-investigation of unsaturated sand in mini-triaxial compression based on micro-CT image analysis

Acta Geotechnica - The mechanical behaviours of unsaturated soils are highly related to the water content and pore water and air distributions. Under the context of climate change, geo-disasters...     

GIS-based spatial prediction of debris flows using logistic regression and frequency ratio models for Zêzere River basin and its surrounding area,Northwest Covilhã, Portugal

Landslide susceptibility mapping (LSM) is important for catastrophe management in the mountainous regions. They focus on generating susceptibility maps beginning from landslide inventories and considering the main predisposing parameters. The aim of this study was to assess the susceptibility of the occurrence of debris flows in the Zêzere River basin and its surrounding area using logistic regression (LR) and frequency ratio (FR) models. To achieve this, a landslide inventory map was created using historical information, satellite imagery, and extensive field works. One hundred landslides were mapped, of which 75% were randomly selected as training data, while the remaining 25% were used for validating the models. The landslide influence factors considered for this study were lithology, elevation, slope gradient, slope aspect, plan curvature, profile curvature, normalized difference vegetation index (NDVI), distance to roads, topographic wetness index (TWI), and stream power index (SPI). The relationships between landslide occurrence and these factors were established, and the results were then evaluated and validated. Validation results show that both methods give acceptable results [the area under curve (AUC) of success rates is 83.71 and 76.38 for LR and FR, respectively]. Furthermore, the AUC results for prediction accuracy revealed that LR model has the highest predictive performance (AUC of predicted rate?=?80.26). Hence, it is concluded that the two models showed reasonably good accuracy in predicting the landslide susceptibility in the study area. These two models have the potential to aid planners in development and land-use planning and to offer tools for hazard mitigation measures.     

Did the circum-Rodinia subduction trigger the Neoproterozoic rifting along the Congo–Kalahari Craton margin?

International Journal of Earth Sciences - Early Neoproterozoic metaigneous rocks occur in the central part of the Kaoko–Dom Feliciano–Gariep orogenic system along the coasts of the...     

On the (anticipated) diversity of terrestrial planet atmospheres

On our way toward the characterization of smaller and more temperate planets, missions dedicated to the spectroscopic observation of exoplanets will teach us about the wide diversity of classes of planetary atmospheres, many of them probably having no equivalent in the Solar System. But what kind of atmospheres can we expect? To start answering this question, many theoretical studies have tried to understand and model the various processes controlling the formation and evolution of planetary atmospheres, with some success in the Solar System. Here, we shortly review these processes and we try to give an idea of the various type of atmospheres that these processes can create. As will be made clear, current atmosphere evolution models have many shortcomings yet, and need heavy calibrations. With that in mind, we will thus discuss how observations with a mission similar to EChO would help us unravel the link between a planet’s environment and its atmosphere.