Experimental study of the mechanical properties of expansive soil with added nanomaterials

The main purpose of this paper is to describe ways to improve the microstructure of expansive soil by adding nanomaterials. Mechanical tests were done to explore the changes in shear strength and compression index of expansive soil that was modified by adding different amounts of two kinds of nanomaterials (nano-alumina and nano-silica). The test results show that adding 1.2% nano-alumina and about 2% nano-silica to expansive soil provides the optimal compression index. The test results show that adding 1.2% nano-alumina and about 1.5% nano-silica to expansive soil provides the optimal unconfined compression stress. Scanning electron microscopy of the microstructure of expansive soil modified by nanomaterials provided a deeper understanding of the effects of nanomaterials on improving expansive soil.     

Stability study on the northern batter of MBC Open Pit using Plaxis 3D

Cracks appeared on the northern batter at Maddingley Brown Coal Open Pit Mine, Victoria, Australia, on 8 November 2013 and a 2-day rainfall event happened 5 days later. This study models the stability of the northern batter considering the effect of the rainfall event and an emergency buttress using finite element method (FEM) encoded in Plaxis 3D. It is found that the batter tended to lead to block sliding after overburden removal. The observed vertical crack would be a combined action of the overburden removal and groundwater flow. The simulated location of cracks agrees well with the actual location, and the simulated heave of the coal seam is in good agreement with the experience in Victoria brown coal open pit mining. The rainfall accelerated the development of the cracks. With the construction of the emergency buttress, the batter became stable that is in good agreement with the monitored data.     

Exploratory Factor Analysis of Wireline Logs Using a Float-Encoded Genetic Algorithm

In the paper, a novel inversion approach is used for the solution of the problem of factor analysis. The float-encoded genetic algorithm as a global optimization method is implemented to extract factor variables using open-hole logging data. The suggested statistical workflow is used to give a reliable estimate for not only the factors but also the related petrophysical properties in hydrocarbon formations. In the first step, the factor loadings and scores are estimated by Jöreskog’s fast approximate method, which are gradually improved by the genetic algorithm. The forward problem is solved to calculate wireline logs directly from the factor scores. In each generation, the observed and calculated well logs are compared to update the factor population. During the genetic algorithm run, the average fitness of factor populations is maximized to give the best fit between the observed and theoretical data. By using the empirical relation between the first factor and formation shaliness, the shale volume is estimated along the borehole. Permeability as a derived quantity also correlates with the first factor, which allows its determination from an independent source. The estimation results agree well with those of independent deterministic modeling and core measurements. Case studies from Hungary and the USA demonstrate the feasibility of the global optimization based factor analysis, which provides a useful tool for improved reservoir characterization.     

Anisotropic Continuous-in-Scale Universal Multifractal Cascades: Simulation,Analysis and Correction Methods

Continuous-in-scale multifractal cascades has long been an attractive choice for mathematically modeling turbulent and turbulent-like geophysical fields. These fields are usually anisotropic as they are subject to both stratification and rotation, thereby questioning the isotropy assumption often made to model them. The self-affine and generalized scale invariance approaches to scaling are used here to introduce anisotropy in such models. These anisotropic simulations have (1) unresolved large-scale features and (2) statistics that deviate from the desired power-law scaling mainly in the small scales. The former issue is solved via nesting, whereas the latter is attempted to be overcome using singularity correction methods. While earlier studies have proposed isotropic correction methods, here they have been generalized to correct anisotropic simulations. These singularity corrections seem to improve the small-scale statistical properties of mildly anisotropic simulations; nesting, on the other hand, appears to enhance statistics over almost all scales even for strongly anisotropic simulations. Both the correction and nesting techniques lead to a reduction in computational time and memory usage suggesting that nested singularity-corrected cascades offer a better framework for quantitatively modeling the atmosphere, ocean, solid earth, and associated fields.     

A Preliminary Evaluation on the Prospects of Hydrocarbon Potential in the Carbonaceous Shales of Spiti and Chikkim Formations,Tethys Himalaya,India

In the present investigation, an attempt has been made to explore the possibility of hydrocarbon prospects in the carbonaceous shale deposits of Spiti and Chikkim formations exposed in the Spiti valley of the Tethys Himalaya. Twenty samples, collected from successive levels of these litho-units, have been subjected to maceral analysis, Rock-Eval Pyrolysis and six samples to Fourier Transform Infra-red Spectroscopy (FTIR) and nuclear magnetic resonance (NMR) analyses. The study reveals the presence of mainly kerogen-III type of organic matter but some of the shale samples have shown a good amount of total organic carbon (TOC) to the tune of 3.19% which is sufficient to produce hydrocarbon. The results indicate the presence of methane occurring as free and fixed hydrocarbon in the shale samples. Few levels are especially rich in hydrocarbon. They have shown encouraging results with potential for generating liquid as well as lighter hydrocarbon. The data is also supported by the FTIR and NMR studies.     

Hydroclimatological Perspective of the Kerala Flood of 2018

Flood is among the deadliest disasters in India, and the frequency of floods and extreme precipitation events is projected to increase under the warming climate. The frequency of floods in India varies geographically as some regions are more prone to floods than the others. The Kerala flood of 2018 caused enormous economic damage, affected millions of people, and resulted in the death of more than 400 people. Here we provide a hydroclimatological perspective on the Kerala flood of 2018. Using the observations and model simulations from the Variable Infiltration Capacity (VIC) model, we show that the 2018 extreme precipitation and runoff conditions that caused flooding were unprecedented in the record of the past 66 years (1951–2017). Our results show that mean monsoon precipitation has significantly declined while air temperature has significantly increased during 1951–2017 in Kerala. The drying and warming trends during the monsoon season resulted in a declined total runoff in large part of the state in the last 66 years. Apart from the mean hydroclimatic conditions, extreme precipitation, and extreme total runoff have also declined from 1951 to 2017. However, 1 and 2-day extreme precipitation and extreme runoff conditions in August 2018 exceeded substantially from the long-term 95^th percentiles recorded during 1951–2017. Since there is no increase in mean and extreme precipitation in Kerala over the last six decades, the extreme event during August 2018 is likely to be driven by anomalous atmospheric conditions due to climate variability rather anthropogenic climate warming. The severity of the Kerala flood of 2018 and the damage caused might be affected by several factors including land use/land cover change, antecedent hydrologic conditions, reservoir storage and operations, encroachment of flood plains, and other natural factors. The impacts of key drivers (anthropogenic and natural) on flood severity need to be established to improve our understanding of floods and associated damage.     

Singular spectrum analysis,harmonic regression and El-Nino effect on total ozone (1979–1993) over India and surrounding regions

The Total Ozone Mapping Spectrometer (TOMS) is a satellite instrument that records Total Column Ozone (TCO) concentration (in DB) of the atmosphere in the form of different overpass files. We have selected 23 sites over India (15), Pakistan (4), Bangladesh (1) and adjoining China (3) to investigate the TCO scenario over this region. About 114,000 historical records (1979M1–1992M12) from 23 overpass files were processed to generate 23 monthly mean time series (TS) of TCO. Statistical analysis followed by singular spectrum analysis (SSA), harmonic regression (HR) and spatial interpolation have been used to accomplish the investigation. Four dominant signals; quasi-biennial signal (QBI, \(\hbox {T} = 30.12\) months), quasi-annual signal (QAN, \(\hbox {T} = 19.69\) months), annual signal (ANN, \(\hbox {T} = 12\) months) and semi-annual signal (SAN, \(\hbox {T} = 6\) months) were discerned to explain the variability. Direct latitudinal effect on the TCO distribution was observed. The variance was limited between 80.53 and 90.13%; ANN contributes 65.93–93.22% followed by SAN 0.58–5.69%, QAN 0.33–5.48%, and QBI 0.06–5.94%. Peak values of the oscillations are estimated from phasor diagrams: QBI, March to May in the mid-latitude; QAN, April, and May; ANN, February to April; SAN, March to May. Incisive pictures of the average distribution and variability of four sinusoids were investigated from contour plots. Two ozone valley were discerned from Spatial interpolation; one over Deccan Plateau in low and other over Tibetan Plateau in high latitude. 179 outliers from \(23 \times 168\) observations have been identified after harmonic regression. The appearance of the outliers is highly consistent with extreme phases of multivariate ENSO Index and Dipole Mode Index.     

Pedotransfer functions to estimate soil water content at field capacity and permanent wilting point in hot Arid Western India

Characterization of soil water retention, e.g., water content at field capacity (FC) and permanent wilting point (PWP) over a landscape plays a key role in efficient utilization of available scarce water resources in dry land agriculture; however, direct measurement thereof for multiple locations in the field is not always feasible. Therefore, pedotransfer functions (PTFs) were developed to estimate soil water retention at FC and PWP for dryland soils of India. A soil database available for Arid Western India (N=370) was used to develop PTFs. The developed PTFs were tested in two independent datasets from arid regions of India (N=36) and an arid region of USA (N=1789). While testing these PTFs using independent data from India, root mean square error (RMSE) was found to be 2.65 and 1.08 for FC and PWP, respectively, whereas for most of the tested ‘established’ PTFs, the RMSE was >3.41 and >1.15, respectively. Performance of the developed PTFs from the independent dataset from USA was comparable with estimates derived from ‘established’ PTFs. For wide applicability of the developed PTFs, a user-friendly soil moisture calculator was developed. The PTFs developed in this study may be quite useful to farmers for scheduling irrigation water as per soil type.