Sparse Data Division Using Data Segmentation and Kohonen Network for Neural Network and Geostatistical Ore Grade Modeling in Nome Offshore Placer Deposit

In this paper, sparse data problem in neural network and geostatistical modeling for ore-grade estimation was addressed in the Nome offshore placer gold deposit. The problem of sparse data arises because of the random data division into training, validation, and test subsets during ore-grade modeling. In this regard, the possibility of generating statistically dissimilar data subsets by random data division was also explored through a simulation exercise. A combined approach of data segmentation and application of a Kohonen network then was used to solve the data division problem. Two neural networks and five kriging models were applied for grade modeling. The neural network was trained using an early stopping method. Performance evaluation of the models was carried out on the test data set. The study results indicated that all the models that were investigated in this study performed almost equally. It was also revealed that by using the secondary variable watertable depth the neural network and the kriging models slightly improved their prediction precision. Further, the overall R ² of the models was poor as a result of high nugget (noisy) component in ore-grade variation.     

On the propagation of disturbances in a composite viscoelastic layer in a magnetic field

Summary The propagation of disturbances in a composite viscoelastic layer when subjected to an impulsive force in the presence of a primary magnetic field, has been investigated in this note. The mechanical displacement has been derived from the electro-magnetic equations of Maxwell, the equation of motion and the stress-strain relations of the materials considered and has been computed for small values of time.     

Mobility of arsenic in West Bengal aquifers conducting low and high groundwater arsenic. Part II: Comparative geochemical profile and leaching study

Aquifer sediments from areas of low- and high-As groundwater were characterized mineralogically and geochemically at a field site in the Nadia district of West Bengal, India. Leaching experiments and selective extraction of the sediments were also carried out to understand the release mechanism of As in the sub-surface. The correlation between measured elements (major, minor and trace) from low- and high-As groundwater areas are only significant for As, Fe and Mn. The borehole lithology and percentage of silt and clay fraction demonstrates the dominance of finer sediments in the high-As aquifer. Multivariate analysis of the geochemical parameters showed the presence of four different mineral phases (heavy-mineral fraction, phyllosilicates/biotite/Fe-oxyhydroxides, carbonates and sulphides) in the sediments. Selective extraction of sediment reveals that amorphous Fe-oxyhydroxide acts as a potential sink for As in the sub-surface. The result is consistent with microbially mediated redox reactions, which are controlled in part by the presence of natural organic matter within the aquifer sediments. The occurrences of As-bearing redox traps, primarily formed of Fe- and Mn-oxides/hydroxides, are also important factors that control the release of As into groundwater at the study site.     

Climate Change and Tropical Forests in India

India has 64 Mha under forests, of which 72% are tropical moist deciduous, dry deciduous, and wet evergreen forest. Projected changes in temperature, rainfall, and soil moisture are considered at regional level for India under two scenarios, the first involving greenhouse gas forcing, and the second, sulphate aerosols. Under the former model, a general increase in temperature and rainfall in all regions is indicated. This could potentially result in increased productivity, and shift forest type boundaries along attitudinal and rainfall gradients, with species migrating from lower to higher elevations and the drier forest types being transformed to moister types. The aerosol model, however, indicates a more modest increase in temperature and a decrease in precipitation in central and northern India, which would considerably stress the forests in these regions.Although India seems to have stabilized the area under forest since 1980, anthropogenic stresses such as livestock pressure, biomass demand for fuelwood and timber, and the fragmented nature of forests will all affect forest response to changing climate. Thus, forest area is unlikely to expand even if climatically suitable, and will probably decrease in parts of northeast India due to extensive shifting cultivation and deforestation. A number of general adaptation measures to climate change are listed.     

Hydraulic significance of fracture correlated lineaments in precambrian rocks in Purulia district, West Bengal

Filter analysis of lineaments in Precambrian metamorphic rocks was used to delineate fracture-correlated lineaments and hydraulically significant fractures. The unfiltered analysis technique fails to show correlation between major lineaments and fractures. Domain-based and discrete filtering techniques successfully identify fracture-correlated lineaments within the brittle-ductile shear zone in conjunction with fractures characterized by high fracture frequencies (>10/m). The locales of hydraulically significant fractures can thus be assessed if the geological controls governing the spatial distribution of fracture frequencies are computed using structural domain approach. The concurrence of fracture-correlated lineaments and hydraulically significant fractures within the brittle-ductile shear zone is evident.     

GIS Based Marine Platinum Exploration, Goodnews Bay Region, Southwest Alaska

Goodnews Bay, southwest Alaska, is known for platinum (Pt) reserve that extends offshore in the Bering Sea. To assess the nearshore placer potential we first collated marine Pt concentrations available since 1960 in a geographic information system (GIS) database. Subsequently, in 2005, we collected 23 pipe dredge sediment samples and 26 vibracores from unexplored sites and analyzed them for Pt. This sampling was supplemented by magnetic (Sea Spy) and seismic (side scan, geoacoustic and datasonic bubble pulser) surveys. Integrating results of geospatial analysis of Pt concentrations with geophysical analysis using GIS techniques led to delineate four locations encouraging for further Pt exploration. Of these, two locations fall close to paleochannels and drowned ultramafic source, while the other two coincide with high energy environments in the Goodnews Bay and close to the Carter Bay.     

Goodnews Bay Platinum Resource Estimation Using Least Squares Support Vector Regression with Selection of Input Space Dimension and Hyperparameters

Resource estimation of a placer deposit is always a difficult and challenging job because of high variability in the deposit. The complexity of resource estimation increases when drill-hole data are sparse. Since sparsely sampled placer deposits produce high-nugget variograms, a traditional geostatistical technique like ordinary kriging sometimes fails to produce satisfactory results. In this article, a machine learning algorithm—the support vector machine (SVM)—is applied to the estimation of a platinum placer deposit. A combination of different neighborhood samples is selected for the input space of the SVM model. The trade-off parameter of the SVM and the bandwidth of the kernel function are selected by genetic algorithm learning, and the algorithm is tested on a testing data set. Results show that if eight neighborhood samples and their distances and angles from the estimated point are considered as the input space for the SVM model, the developed model performs better than other configurations. The proposed input space-configured SVM model is compared with ordinary kriging and the traditional SVM model (location as input) for resource estimation. Comparative results reveal that the proposed input space-configured SVM model outperforms the other two models.     

Viewing Angle Effect on the Remote Sensing Monitoring of Wheat and Rice Crops

Abstract

Bidirectional reflectance characteristics of a wheat and a rice canopy were studied using radiometric measurements over the two canopies with an Exotech Radiometer Model 100AX. For both rice and wheat, the measurements exhibited significant variations in reflectance amplitude for varying viewing zenith angles and their values decreased from backward to forward scattering direction. Reflectance obtained its maximum value in the backscatter direction for the viewing angle that corresponds approximately to the solar zenith angle at the time of measurements. Canopy background condition (irrigated or non‐irrigated) was an important determining factor of canopy reflectance particularly in the near‐infrared region of the solar spectrum. Furthermore, bidirectional response characteristics of a vegetation canopy were influenced by the amount of vegetation cover.