Determining the reference frame for kinematic analysis in S-tectonites using AMS

Salem-Attur shear zone in the Southern Granulite Terrane demarcates the tectonic boundary between Archaean granulites of Dharwar craton and the Palaeoproterozoic granulites of Salem area. The shear zone marks a low angle thrust which has been steepened at places due to late stage folding. Static recrystallisation during late stage folding has removed the strain marker of mylonites to large extent. However, in a few places S-C angle and porphyroclasts are preserved and have been used to compute the strain. The strain shows apparent flattening by simple shear deformation with 35 % volume loss. A minimum displacement along the thrust has been computed to be 2.7 km. The strain k values increases with r suggesting the strain approaching towards prolate field with increase in strain intensity. The above study suggests the Salem-Attur shear zone is a thrust with low to moderate deformation and volume loss.     

Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN)

The stability problem of natural slopes, filled slopes, and cut slopes are commonly encountered in Civil Engineering Projects. Predicting the slope stability is an everyday task for geotechnical engineers. In this paper, a study has been done to predict the factor of safety (FOS) of the slopes using multiple linear regression (MLR) and artificial neural network (ANN). A total of 200 cases with different geometric and shear strength parameters were analyzed by using the well-known slope stability methods like Fellenius method, Bishop’s method, Janbu method, and Morgenstern and Price method. The FOS values obtained by these slope stability methods were used to develop the prediction models using MLR and ANN. Further, a few case studies have been done along the Jorabat-Shillong Expressway (NH-40) in India, using the finite element method (FEM). The output values of FEM were compared with the developed prediction models to find the best prediction model and the results were discussed.     

Pre-onset land surface processes and ��internal�� interannual variabilities of the Indian summer monsoon

It is proposed that, land?Catmosphere interaction around the time of monsoon onset could modulate the first episode of climatological intraseasonal oscillation (CISO) and may generate significant ??internal?? interannual variation in the Indian summer monsoon rainfall. The regional climate model RegCM3 is used over Indian monsoon domain for 27?years of control simulation. In order to prove the hypothesis, another two sets of experiment are performed using two different boundary conditions (El Ni?o year and non-ENSO year). In each of these experiments, a single year of boundary conditions are used repeatedly year after year to generate ??internal?? interannual monsoon variability. Simulation of monsoon climate in the control model run is found to be in reasonably good agreement with observation. However, large rainfall bias is seen over Arabian Sea and Bay of Bengal. The interannual monsoon rainfall variability are of the same order in two experiments, which suggest that the external influences may not be important on the generation of ??internal?? monsoon rainfall variability. It is shown that, a dry (wet) pre-onset land-surface condition increases (decreases) rainfall in June which in turn leads to an anomalous increase (decrease) in seasonal (JJAS) rainfall. The phase and amplitude of CISO are modulated during May?CJune and beyond that the modulation of CISO is quite negligible. Though the pre-onset rainfall is unpredictable, significant modulation of the post-onset monsoon rainfall by it can be exploited to improve predictive skill within the monsoon season.