The Relationship between Fe Mineralization and Magnetic Basement Faults using Multifractal Modeling in the Esfordi and Behabad Areas (BMD), Central Iran

Multifractal modeling is a mathematical method for the separation of a high potential mineralized background from a non-mineralized background. The Concentration-Distance to Fault structures (C-DF) fractal model and the distribution of the known iron (Fe) deposits/mines seen in the Esfordi and Behabad 1:100,000 sheets from the Bafq region of central Iran are used to distinguish Fe mineralization based on their distance to magnetic basement structures and surface faults, separately, using airborne geophysical data and field surveys. Application of the C-DF fractal model for the classification of Fe mineralizations in the Esfordi and Behabad areas reveals that the main ones show a correlation with their distance from magnetic basement structures. Accordingly, the distances of Fe mineralizations with grades of Fe higher than 55% )43% < Fe ≤ 60%) are located at a distance of less than 1 km, whereas for surfacial faults with grades of 43% ≤ Fe ≤ 60%, the distances are 3162< DF ≤ 4365 m from the faults. Thus, there is a positive relationship between Fe mineralization and magnetic basement structures. Also, the proximity evidence of Precambrian high-grade Fe mineralization related to magnetic basement structures indicates syn-rifting tectonic events. Finally, this C-DF fractal model can be used for exploration of magmatic and hydrothermal ore deposits.     

Morphotectonic and lithostratigraphic analysis of intermontane Karewa Basin of Kashmir Himalayas, India

Morpho-tectonic study plays an important role in deciphering the effects of tectonic activity in the geomorphic evolution of the drainage basins.Romushi watershed forms one of the major watersheds of the intermontane Karewa Basin of Kashmir Valley.The Karewa sediments are characterized by glacio-fluvio-lacustrine deposits capped by the aeolian loess.The geomorphic,morphometric and lithostratigraphic studies of these cap deposits have been carried out to elucidate the effect of tectonics on the geomorphic evolution of Romushi Watershed.Geomorphic mapping was carried out using GPS measurements,DEM at 30m resolution,Topographic Position Index(TPI) model,topographic maps,LANDSAT TM Imagery and field data.Morphometric and morphotectonic analyses in GIS environment were used to calculate various geomorphic indices(Mountain Front Sinuosity Index,Bifurcation Ratio,Asymmetry Factor,River Profile,etc).These indices reveal that the tectonic uplift observed in the region due to Himalayan orogeny coupled with mass movement and aeolian deposition have dominated the landscape evolution of intermontane Karewa Basin of Kashmir throughout the Late Quaternary Period.Additional data from lithostratigraphic measurements were analyzed to understand the geomorphic evolution of intermontane Karewa Basin.The data revealed that the basin has experienced differential uplift and erosion rates from time to time in the geological past.This was corroborated by the results from the morphometric and morphotectonic analysis.     

Characterization of spatial patterns in river water quality using chemometric pattern recognition techniques

This study employed three chemometric data mining techniques (factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA)) to identify the latent structure of a water quality (WQ) dataset pertaining to Kinta River (Malaysia) and to classify eight WQ monitoring stations along the river into groups of similar WQ characteristics. FA identified the WQ parameters responsible for variations in Kinta River's WQ and accentuated the roles of weathering and surface runoff in determining the river's WQ. CA grouped the monitoring locations into a cluster of low levels of water pollution (the two uppermost monitoring stations) and another of relatively high levels of river pollution (the mid-, and down-stream stations). DA confirmed these clusters and produced a discriminant function which can predict the cluster membership of new and/or unknown samples. These chemometric techniques highlight the potential for reasonably reducing the number of WQVs and monitoring stations for long-term monitoring purposes.     

Integrated petrographic,mineralogical, and geochemical study of the upper Kaimur Group of rocks,Son Valley,India: Implications for provenance,source area weathering and tectonic setting

The upper Kaimur Group (UKG) of the Vindhyan Supergroup in central India, primarily consists of three rock types-Dhandraul sandstone, Scarp sandstone and Bijaigarh shale. The present study aims to reconstruct the parent rock assemblages, their tectonic provenance, mineralogy, weathering intensity, hydraulic sorting and depositional tectonic setting. Samples from the UKG rocks representing the Dhandraul sandstone, Scarp sandstone and Bijaigarh shale were studied using a combination of petrographic, mineralogical, and geochemical techniques. Texturally, medium to coarse grained UKG sandstones are mature and moderate to well sorted. Deficiency of feldspars in these sandstones indicates that the rocks are extensively recycled from distant sources. Their average modal composition for Scarp (avg. Qt₉₉ F₀.₂L_0.8) and Dhandraul (avg. Qt₉₉ F_0.1L_0.8) sandstones, classifies them as quartz arenite to sub-litharenite types, which is consistent with geochemical study. Major element concentrations revealed that sandstones have high SiO₂, K₂O < Na₂O, and low Fe₂O₃, which are supported by the modal data. On the other hand, sandstone samples are enriched in most trace elements such as Ce, Sr, V, Sc and Zr and depleted in U and Th. The CIA values (43.17–76.48) of the UKG rocks indicate low to moderate weathering, either of the original source or during transport before deposition, which may have related to low-relief and humid climatic conditions in the source area. Further, petrographic and geochemical interpretations indicate that they are derived from craton interior to quartzose recycled sedimentary rocks and deposited in a passive continental margin. Therefore, granitic and low grade metamorphic rocks of Mahakoshal Group and Chotanagpur granite-gneiss, situated on the southern and south-eastern side of the Vindhyan basin are suggested as possible provenance for the UKG rocks.     

3D inversion of DC data using artificial neural networks

In this paper, we investigate the applicability of artificial neural networks in inverting three-dimensional DC resistivity imaging data. The model used to produce synthetic data for training the artificial neural network (ANN) system was a homogeneous medium of resistivity 100 Ωm with an embedded anomalous body of resistivity 1000 Ωm. The different sizes for anomalous body were selected and their location was changed to different positions within the homogeneous model mesh elements. The 3D data set was generated using a finite element forward modeling code through standard 3D modeling software. We investigated different learning paradigms in the training process of the neural network. Resilient propagation was more efficient than any other paradigm. We studied the effect of the data type used on neural network inversion and found that the use of location and the apparent resistivity of data points as the input and corresponding true resistivity as the output of networks produces satisfactory results. We also investigated the effect of the training data pool volume on the inversion properties. We created several synthetic data sets to study the interpolation and extrapolation properties of the ANN. The range of 100–1000 Ωm was divided into six resistivity values as the background resistivity and different resistivity values were also used for the anomalous body. Results from numerous neural network tests indicate that the neural network possesses sufficient interpolation and extrapolation abilities with the selected volume of training data. The trained network was also applied on a real field dataset, collected by a pole-pole array using a square grid (8 ×8) with a 2-m electrode spacing. The inversion results demonstrate that the trained network was able to invert three-dimensional electrical resistivity imaging data. The interpreted results of neural network also agree with the known information about the investigation area.     

The Effects of Hydrophilic Polymer and Soil Salinity on Corn Growth in Sandy and Loamy Soils

The interaction effects of different applied ratios of a hydrophilic polymer (Superab A200) (0, 0.2, 0.6% w/w) under various soil salinity levels (initial salinity, 4 and 8 ms/cm) were evaluated on available water content (AWC), biomass, and water use efficiency for corn grown in loamy sand and sandy clay loam soils. The results showed that the highest AWC was measured at the lowest soil salinity. The application of 0.6% w/w of the polymer at the lowest salinity level increased the AWC by 2.2 and 1.2 times greater than those of control in the loamy sand and sandy clay loam soils, respectively. The analysis of variance of data showed that the effect of salinity was significant on biomass and water use efficiency of corn in the loamy sand and sandy clay loam soils. The highest amounts of these traits were measured in soils with the lowest salinity level. Application of polymer at the rate of 0.6% in the loamy sand soil and at the rate of 0.2% in the sandy clay loam soil resulted in the highest aerial and root biomass and water use efficiency for corn. At these polymer rates the amounts of water use efficiency for corn were 2.6 and 1.7 times greater than those of control in the loamy sand and sandy clay loam soils, respectively. Thus, the use of hydrophilic polymer in soils especially in the sandy soils increases soil water holding capacity, yield, and water use efficiency of plant. On the other hand, decreases the negative effect of soil salinity on plant and helps for irrigation projects to succeed in arid and semi‐arid areas.     

Application of various methods for 2D inverse modeling of residual gravity anomalies

This paper presents a neural network approach to determine 2D inverse modeling of a buried structure from gravity anomaly profile. The results of the applied neural network method are compared with the results of two other methods, least-squares minimization and the simple method. Sphere, horizontal cylinder and vertical cylinder and their gravity effects are considered as the synthetic models and the synthetic data, respectively. The synthetic data are also corrupted with noise to evaluate the capability of the methods. Then the Dehloran bitumen map in Iran is chosen as a real data application. Anomaly value of the cross-section, which is taken from the gravity anomaly map of Dehloran bitumen, is very close to those obtained from these methods.