An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas

Landslide susceptibility zonation (LSZ) is necessary for disaster management and planning development activities in mountainous regions. A number of methods, viz. landslide distribution, qualitative, statistical and distribution-free analyses have been used for the LSZ studies and they are again briefly reviewed here. In this work, two methods, the Information Value (InfoVal) and the Landslide Nominal Susceptibility Factor (LNSF) methods that are based on bivariate statistical analysis have been applied for LSZ mapping in a part of the Himalayas. Relevant thematic maps representing various factors (e.g., slope, aspect, relative relief, lithology, buffer zones along thrusts, faults and lineaments, drainage density and landcover) that are related to landslide activity, have been generated using remote sensing and GIS techniques. The LSZ derived from the LNSF method, has been compared with that produced from the InfoVal method and the result shows a more realistic LSZ map from the LNSF method which appears to conform to the heterogeneity of the terrain.     

Spectral models for estimation of chlorophyll content, growth and yield of wheat crop

Field experiments were conducted during 1998–99 and 1999–2000 at research farm of the Department of Agricultural Meteorology, CCS Haryana Agricultural University, Hisar. Five wheat cultivars: WH 542, PBW 343, UP 2338, Raj 3765 and Sonak were sown on 25^th November, 10^th and 25^th December with four nitrogen levels viz., no nitrogen. 50, 100 and 150% of recommended dose. Leaf area index, dry matter at anthesis, final dry biomass and grain yield were recorded in all the treatments. Chlorophyll and wax contents of wheat leaves were estimated at different growth stages. Multiband spectral reflectance was measured using hand-held radiometer. Spectral indices such as simple ratio, normalized difference, transformed vegetation index, perpendicular vegetation index and greenness index were computed using the multiband spectral data. Values of all the spectral indices were maximum in 25 November sown crop with maximum dose of nitrogen (180 kg N ha^-1). PBW 343 showed higher values of all the spectral indices in comparison with other cultivars. The spectral indices recorded during maximum leaf area index stage were correlated with crop parameters. Using stepwise regression, empirical models for chlorophyll, leaf area index, dry biomass and yield prediction were developed. The ’R²’ values of these models ranged between 0.87 and 0.95.     

Evaluation and Assessment of Blast-Induced Ground Vibrations in an Underground Gold Mine: A Case Study

Natural Resources Research - Ground vibrations induced during rock fragmentation by blasting remain a potential source of hazard for the stability of nearby structures. In this paper, to forecast...     

Field Survey of Tsunami Effects in Sri Lanka due to the Sumatra-Andaman Earthquake of December 26, 2004

The December 26, 2004 Sumatra-Andaman earthquake that registered a moment magnitude (M_w) of 9.1 was one of the largest earthquakes in the world since 1900. The devastating tsunami that resulted from this earthquake caused more casualties than any previously reported tsunami. The number of fatalities and missing persons in the most seriously affected countries were Indonesia - 167,736, Sri Lanka - 35,322, India - 18,045 and Thailand - 8,212. This paper describes two field visits to assess tsunami effects in Sri Lanka by a combined team of Japanese and Sri Lankan researchers. The first field visit from December 30, 2004 – January 04, 2005 covered the western and southern coasts of Sri Lanka including the cities of Moratuwa, Beruwala, Bentota, Pereliya, Hikkaduwa, Galle, Talpe, Matara, Tangalla and Hambantota. The objectives of the first field visit were to investigate the damage caused by the tsunami and to obtain eyewitness information about wave arrival times. The second field visit from March 10–18, 2005 covered the eastern and southern coasts of Sri Lanka and included Trincomalee, Batticaloa, Arugam Bay, Yala National Park and Kirinda. The objectives of the second visit were mainly to obtain eyewitness information about wave arrival times and inundation data, and to take relevant measurements using GPS instruments.     

Petrology of the Indian eucrite Piplia Kalan

Abstract— Piplia Kalan is an equilibrated eucrite consisting of 60–80 vol% lithic clasts in a subordinate brecciated matrix. Ophitic/subophitic clasts fall into two groups: finer‐grained lithology A and coarser‐grained lithology B. Very fine‐grained clasts with equigranular textures (lithology C) also occur and originally were hypocrystalline in texture. The variety of materials represented in Piplia Kalan suggests cooling histories ranging from quenching to slower crystallization. Despite textural differences, clasts and matrix have similar mineral and bulk compositions. Thus, Piplia Kalan is probably best classified as a genomict breccia that could represent fragments of a single lava flow or shallow intrusive body, including fine‐grained or glassy outer margin and more slowly cooled coarser‐grained interior. Bulk composition suggests that the meteorite is most closely related to the main group eucrites, but it probably was affected by minor amounts of fractional crystallization. Piplia Kalan displays evidence of an early shock event, including brecciated matrix and areas of lithic clasts that contain very fine‐grained, granular pyroxene between deformed feldspar laths. The meteorite also displays evidence of at least one episode of extensive thermal metamorphism: hypocrystalline materials are recrystallized to hornfelsic textures and minerals throughout the meteorite contain abundant inclusions that are relatively large in size. Veins of brown glass transect both clasts and matrix and indicate a second, postmetamorphism shock event.     

Evaluation of Noah-LSM for soil hydrology parameters in the Indian summer monsoon conditions

The micrometeorological observations, collected over a station in Ranchi (23°45′N, 85°30′E) which is under the monsoon trough region of India, were used in the Noah-LSM (NCEP, OSU, Air Force and Office of Hydrology Land Surface Model) to investigate the model performance in wet (2009 and 2011) and dry (2010) conditions during the south-west summer monsoon season. With this analysis, it is seen that the Noah-LSM has simulated the diurnal cycle of heat fluxes (sensible and ground) reasonably. The simulated heat fluxes were compared with its direct measurements by sonic anemometer and soil heat flux plate. The net radiation and sensible heat flux are simulated well by the model, but the simulation of ground heat flux was found to be poor in both dry as well as wet conditions. The soil temperature simulations were also found to be poor in 0–5- and 5–10-cm layers compared to other deeper layers. The observations were also correlated with the Modern Era Retrospective-analysis for Research and Applications (MERRA) data. The correlation between the observations and ground heat flux was better in MERRA dataset than that of the Noah-LSM simulation.     

Overpressure zone under the Krishna–Godavari offshore basin: geophysical implications for natural hazard in deeper-water drilling

Accurate knowledge of pore pressure is fundamental to any safe and economic well construction. Here, we present results that are indicative of over pressure zones (OPZ) for five wells drilled under the Krishna–Godavari offshore basin (KGOB) at the Eastern Continental Margin of India (ECMI). OPZ in areas of crustal flexuring can act as potential geohazard while drilling. These wells locate at water depths of 515–1,265 m, where their penetrated-vertical-depth reaches up to 3,960 m in clastic sediments. pore pressure gradient (PPG) and fracture pressure gradient (FPG) are estimated from acoustic log for all five wells, while the Normal Compaction Trend (NCT) and pore pressure are calculated from Miller’s sonic equation. Top of OPZ is indicated by values that are higher than the NCT; departure from NCT is observed at depth intervals of 1,320–2,180 m, 1,700–3,960 m, 1,600–1,880 m, 1,420–2,609 m and 2,080–2,200 m for the respective Wells 1 through 5. The pressure data from Modular Dynamic Tester (MDT) agree well with the pore pressure values obtained from the logs. The Overburden Gradient (OBG), PPG and FPG values increase rather slowly with total depth in deeper-water of KGOB when compared to the wells located in shallow water depth. Consequently, the operating safety margin between PPG and FPG decreases as the water depth increases, and this clearly leads to an increase in the number of casing strings to reach the target depth. Certain basic conclusions on the potentiality of natural hazard for drilling operations are drawn on the basis of these results, but evidently, further studies are warranted to present a more composite picture of OPZ under KGOB.     

Prediction of thermal conductivity of rocks by soft computing

The transfer of energy between two adjacent parts of rock mainly depends on its thermal conductivity. Knowledge of the thermal conductivity of rocks is necessary for the calculation of heat flow or for the longtime modeling of geothermal resources. In recent years, considerable effort has been made to develop artificial intelligence techniques to determine these properties. Present study supports the application of artificial neural network (ANN) in the study of thermal conductivity along with other intrinsic properties of rock due to its increasing importance in many areas of rock engineering, agronomy, and geoenvironmental engineering field. In this paper, an attempt has been made to predict the thermal conductivity (TC) of rocks by incorporating uniaxial compressive strength, density, porosity, and P-wave velocity using artificial neural network (ANN) technique. A three-layer feed forward back propagation neural network with 4-7-1 architecture was trained and tested using 107 experimental data sets of various rocks. Twenty new data sets were used for the validation and comparison of the TC by ANN. Multivariate regression analysis (MVRA) has also been done with same data sets of ANN. ANN and MVRA results were compared based on coefficient of determination (CoD) and mean absolute error (MAE) between experimental and predicted values of TC. It was found that CoD between measured and predicted values of TC by ANN and MVRA were 0.984 and 0.914, respectively, whereas MAE was 0.0894 and 0.2085 for ANN and MVRA, respectively.