Heavy Mineral Placer Sand Deposits of Kontiagarh Area,Ganjam District,Orissa, India

The Kontiagarh placer deposit in the Ganjam district, Orissa, India extends in northeast direction having a width of 700–1000 m. A total of 187 samples were collected meterwise from 55 bore holes in a grid pattern from beach, frontal, intermediate and back dunes covering an area of approximately 1 km². Light minerals decrease in size from the beach to the back dunes, whereas the size distribution of heavy minerals in the beach and dunes is more or less uniform. The average heavy mineral content in the beach and dunes vary from 9.38% to 24.20%. The heavy minerals are ilmenite, garnet, sillimanite, rutile, monazite, and zircon with trace amounts of magnetite, hornblende, diopside, sphene, tourmaline, and epidote. Heavy minerals are mostly less than 350 µm in size, with a peak distribution in the range between 180 and 125 µm. Ilmenite shows exsolution intergrowth with hematite. Mineral chemistry of ilmenite, hematite, leucoxene, magnetite, monazite and sillimanite are examined by EPMA. Leucoxene is lower in Fe and higher in Ti, Al, Cr and V than ilmenite. The litho‐units of the Precambrian Eastern Ghats Mobile Belt, comprising primarily khondalite, charnockite, calc‐silicate granulite and gneiss, are the source of heavy minerals for this deposit. The bulk sample has 7.30% ilmenite, 5.24% sillimanite, 9.16% garnet, 0.18% rutile, 0.14% monazite, 0.06% zircon and 0.52% other heavy minerals. The deposit has good potential for economic exploitation of ilmenite, rutile, sillimanite, monazite, zircon and garnet.     

Grain size, geochemistry, magnetic susceptibility: Proxies in identifying sources and factors controlling distribution of metals in a tropical estuary, India

The aim of this study is to understand the various sources and factors controlling the abundance and distribution of the metals through the analyses of selected metals (iron, manganese, chromium, copper, zinc and cobalt), sand, silt, clay, organic carbon and magnetic susceptibility of the surface sediments of Zuari estuary in three different seasons. Total suspended matter (TSM) concentration and salinity of near surface and bottom waters were also measured. The study undertaken for three seasons, based on 18 selected stations all along the estuary, indicated that the concentration of most of the metals were comparatively higher during pre-monsoon than in monsoon and post-monsoon seasons. Further, along the length of the estuary, high concentrations were observed at lower middle and lower estuary and also at few stations at the upstream end. Salinity, distribution of TSM, size of the sediment, organic matter, geomorphological setup, fresh water input from land and release of industrial waste within the estuary played a role in distribution and concentration of metals. Magnetic measurements indicated the dominance of haematite like minerals and helped in understanding the source and depositional processes.     

Improving peak flow estimates in artificial neural network river flow models

In this paper, the concern of accuracy in peak estimation by the artificial neural network (ANN) river flow models is discussed and a suitable statistical procedure to get better estimates from these models is presented. The possible cause for underestimation of peak flow values has been attributed to the local variations in the function being mapped due to varying skewness in the data series, and theoretical considerations of the network functioning confirm this. It is envisaged that an appropriate data transformation will reduce the local variations in the function being mapped, and thus any ANN model built on the transformed series should perform better. This heuristic is illustrated and confirmed by many case studies and the results suggest that the model performance is significantly improved by data transformation. The model built on transformed data outperforms the model built on raw data in terms of various statistical performance indices. The peak estimates are improved significantly by data transformation. Copyright © 2003 John Wiley & Sons, Ltd.     

The numerical evaluation of theH-functions of radiative transfer in active amplifying media

The present paper contains extensive tables of the values of theH-functionH(z,<0) and of the moments ofQ(x) (in terms of which the moments ofH(z, ) can be determined) appropriate for transfer of radiation in active amplifying media in which<0. These values have been computed correct to the 7th decimal place for values of in the range (–10^–12)-(–10³⁰) and for values of z[0,1] with the aid of a 48-point gaussian quadrature formula.     

Groundwater prospective zones in basaltic terrain using remote sensing

Journal of the Indian Society of Remote Sensing - The study aims at investigating the relative role of landforms and lineaments as an integrated unit for the assessment of groundwater availability...     

Retrieval of sea surface velocities using sequential Ocean Colour Monitor (OCM) data

The Indian remote sensing satellite, IRS-P4 (Oceansat-I) launched on May 26th, 1999 carried two sensors on board, i.e., the Ocean Colour Monitor (OCM) and the Multi-frequency Scanning Microwave Radiometer (MSMR) dedicated for oceanographic research. Sequential data of IRS-P4 OCM has been analysed over parts of both east and west coast of India and a methodology to retrieve sea surface current velocities has been applied. The method is based on matching suspended sediment dispersion patterns, in sequential two time lapsed images. The pattern matching is performed on a pair of atmospherically corrected and geo-referenced sequential images by Maximum Cross-Correlation (MCC) technique. The MCC technique involves computing matrices of cross-correlation coefficients and identifying correlation peaks. The movement of the pattern can be calculated knowing the displacement of windows required to match patterns in successive images. The technique provides actual flow during a specified period by integrating both tidal and wind influences. The current velocities retrieved were compared with synchronous data collected along the east coast during the GSI cruise ST-133 of R.V. Samudra Kaustubh in January 2000. The current data were measured using the ocean current meter supplied by the Environmental Measurement and CONtrol (EMCON), Kochi available with the Geological Survey of India, Marine Wing. This current meter can measure direction and magnitude with an accuracy of ±5‡ and 2% respectively. The measurement accuracies with coefficient of determination (R ² ) of 0.99, for both magnitude (cm.s^-1) and direction (deg.) were achieved.     

Fuzzy computing based rainfall–runoff model for real time flood forecasting

This paper analyses the skills of fuzzy computing based rainfall–runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time‐steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one‐step‐ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one‐step‐ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times. Copyright © 2004 John Wiley & Sons, Ltd.