Optimizing a piezometric network in the estimation of the groundwater budget: a case study from a crystalline-rock watershed in southern India

An estimate of the groundwater budget at the catchment scale is extremely important for the sustainable management of available water resources. Water resources are generally subjected to over-exploitation for agricultural and domestic purposes in agrarian economies like India. The double water-table fluctuation method is a reliable method for calculating the water budget in semi-arid crystalline rock areas. Extensive measurements of water levels from a dense network before and after the monsoon rainfall were made in a 53 km(2)atershed in southern India and various components of the water balance were then calculated. Later, water level data underwent geostatistical analyses to determine the priority and/or redundancy of each measurement point using a cross-validation method. An optimal network evolved from these analyses. The network was then used in re-calculation of the water-balance components. It was established that such an optimized network provides far fewer measurement points without considerably changing the conclusions regarding groundwater budget. This exercise is helpful in reducing the time and expenditure involved in exhaustive piezometric surveys and also in determining the water budget for large watersheds (watersheds greater than 50 km(2)).

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

Seismic Stability Analysis of a Himalayan Rock Slope

The seismic slope stability analysis of the right abutment of a railway bridge proposed at about 350 m above the ground level, crossing a river and connecting two huge hillocks in the Himalayas, India, is presented in this paper. The rock slopes are composed of highly jointed rock mass and the joint spacing and orientation are varying at different locations. Seismic slope stability analysis of the slope under consideration is carried out using both pseudo-static approach and time response approach as the site is located in seismic zone V as per the earth quake zonation maps of India. Stability of the slope is studied numerically using program FLAC. The results obtained from the pseudo-static analysis are presented in the form of Factor of Safety (FOS) and the results obtained from the time response analysis of the slope are presented in terms of horizontal and vertical displacements along the slope. The results obtained from both the analyses confirmed the global stability of the slope as the FOS in case of pseudo-static analysis is above 1.0 and the displacements observed in case of time response analysis are within the permissible limits. This paper also presents the results obtained from the parametric analysis performed in the case of time response analysis in order to understand the effect of individual parameters on the overall stability of the slope.

A new cryptic rock-dwelling Hemidactylus (Squamata: Gekkonidae) from south India

A new species of gecko, Hemidactylus graniticolus sp. nov. is described from Karnataka state, south India. This large-sized (SVL to at least 110.6 mm), rupicolous gecko differs from congeners in having 16-18 longitudinal rows of fairly regularly arranged, subtrihedral, weakly keeled, striated tubercles at midbody; 9-11 and 12-13 subdigital lamellae on the first and fourth digits, respectively, of both manus and pes; tail with transverse series of four enlarged tubercles on each tail segment; 23-28 femoral pores on each side separated by 1-3 poreless scales; 12-14 supralabials and 9-11 infralabials. Molecular data support the distinctiveness of the new species and its affinities with large-bodied, tuberculate Hemidactylus spp. from India and Sri Lanka.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

The Behaviour of Two-Phase Flow of DNAPL and Water through a Fractured Rock under Confining Pressure

This study presents the characterization of DNAPL and water flow in a fracture under confining pressure. A comprehensive mathematical model and the conditions under which DNAPL will enter an initially water-saturated deforming rock fracture are discussed. A numerical model with which to predict the quantity of each phase in terms of their saturations in deforming rock joint is developed. The effect of varying confining stresses on the traverse time of DNAPL across a fractured aquitard is studied. The sensitivity analysis for physical and hydraulic properties like initial fracture apertures, fracture dips, equivalent fracture aperture and confining pressures are performed and discussed.

Influence of rock depth on seismic site classification for shallow bedrock regions

Seismic site classifications are used to represent site effects for estimating hazard parameters (response spectral ordinates) at the soil surface. Seismic site classifications have generally been carried out using average shear wave velocity and/or standard penetration test n-values of top 30-m soil layers, according to the recommendations of the National Earthquake Hazards Reduction Program (NEHRP) or the International Building Code (IBC). The site classification system in the NEHRP and the IBC is based on the studies carried out in the United States where soil layers extend up to several hundred meters before reaching any distinct soil-bedrock interface and may not be directly applicable to other regions, especially in regions having shallow geological deposits. This paper investigates the influence of rock depth on site classes based on the recommendations of the NEHRP and the IBC. For this study, soil sites having a wide range of average shear wave velocities (or standard penetration test n-values) have been collected from different parts of Australia, China, and India. Shear wave velocities of rock layers underneath soil layers have also been collected at depths from a few meters to 180 m. It is shown that a site classification system based on the top 30-m soil layers often represents stiffer site classes for soil sites having shallow rock depths (rock depths less than 25 m from the soil surface). A new site classification system based on average soil thickness up to engineering bedrock has been proposed herein, which is considered more representative for soil sites in shallow bedrock regions. It has been observed that response spectral ordinates, amplification factors, and site periods estimated using one-dimensional shear wave analysis considering the depth of engineering bedrock are different from those obtained considering top 30-m soil layers.

New rock salt-related oxides Li3M2RuO6 (M=Co, Ni): Synthesis, structure, magnetism and electrochemistry

We describe the synthesis, crystal structure, magnetic and electrochemical characterization of new rock salt-related oxides of formula, Li3M2RuO6 (M=Co, Ni). The M=Co oxide adopts the LiCoO2 (R-3m) structure, where sheets of LiO6 and (Co-2/Ru)O-6 octahedra are alternately stacked along the c-direction. The M=Ni oxide also adopts a similar layered structure related to Li2TiO3, where partial mixing of Li and Ni/Ru atoms lowers the symmetry to monoclinic (C2/c). Magnetic susceptibility measurements reveal that in Li3Co2RuO6, the oxidation states of transition metal ions are Co3+ (S=0), Co2+ (S=1/2) and Ru4+ (S=1), all of them in low-spin configuration and at 10 K, the material orders antiferromagnetically. Analogous Li3Ni2RuO6 presents a ferrimagnetic behavior with a Curie temperature of 100 K. The differences in the magnetic behavior have been explained in terms of differences in the crystal structure. Electrochemical studies correlate well with both magnetic properties and crystal structure. Li-transition metal intermixing may be at the origin of the more impeded oxidation of Li3Ni2RuO6 when compared to Li3CO2RuO6. Interestingly high first charge capacities (between ca. 160 and 180 mAh g(-1)) corresponding to ca. 2/3 of theoretical capacity are reached albeit, in both cases, capacity retention and cyclability are not satisfactory enough to consider these materials as alternatives to LiCoO2. (C) 2013 Elsevier Inc. All rights reserved.