Examination of chloritization of biotite as a tool for reconstructing the physicochemical parameters of mineralization and associated alteration in the Zafarghand porphyry copper system,Ardestan, Central Iran: mineral-chemistry and stable isotope analyses

Mineralogy and Petrology - The chloritization of biotite and stable isotopes of silicate have been studied for the Zafarghand porphyry copper deposit, Ardestan, Iran. The studied area, in the...     

3D Gravity Inversion using Tikhonov Regularization

Subsalt exploration for oil and gas is attractive in regions where 3D seismic depth-migration to recover the geometry of a salt base is difficult. Additional information to reduce the ambiguity in seismic images would be beneficial. Gravity data often serve these purposes in the petroleum industry. In this paper, the authors present an algorithm for a gravity inversion based on Tikhonov regularization and an automatically regularized solution process. They examined the 3D Euler deconvolution to extract the best anomaly source depth as a priori information to invert the gravity data and provided a synthetic example. Finally, they applied the gravity inversion to recently obtained gravity data from the Bandar Charak (Hormozgan, Iran) to identify its subsurface density structure. Their model showed the 3D shape of salt dome in this region.     

Assessing the Performance of Independent Component Analysis in Remote Sensing Data Processing

Independent component analysis (ICA) is a novel method to be considered as a powerful type of analysis in the process of source signal separation. Based on the capabilities of this particular analysis, there will be a hypothesis of applying ICA in the image processing of remote sensing data. This paper aims to introduce the ability of ICA in contrasting and highlighting some area with potential of mineralization. Considering and applying ICA transformation on the ETM⁺ image of southern Masule, Iran has resulted in finding some favorable points for further investigation. Moreover, sampling program on the indicated area has led to identify some huge, unexpected lithology and dikes. ICA analysis is a robust method even in remote sensing data processing with the high speed and capabilities in separating source signals from noise.     

Accumulation of heavy metals in soil and uptake by plant species with phytoremediation potential

Contamination of heavy metals represents one of the most pressing threats to water and soil resources, as well as human health. Phytoremediation can be potentially used to remediate metal contaminated sites. In this study, concentrations of copper, zinc, iron, and magnesium accumulated by native plant species were determined in field conditions of Hame Kasi iron and copper mine in the central part of Iran in Hamadan province. The results showed that metal accumulation by plants differed among species and tissue bodies. Species grown in substrata with elevated metals contained significantly higher metals in plants. Metals accumulated by plants were mostly distributed in root tissues, suggesting that an exclusion strategy for metal tolerance exists widely amongst them. The mentioned species could accumulate relatively higher metal concentrations far above the toxic concentration in the plant shoots. With high translocation factor, metal concentration ratio of plant shoots to roots indicates internal detoxification metal tolerance mechanism; thus, they have potential for phytoextraction. The factors affecting metal accumulation by plant species including metal concentrations, pH, electrical conductivity, and nutrient status in substrata were measured. Mostly, concentrations of zinc and copper in both aboveground and underground tissues of the plants were significantly, positively related to their total in substrata, while iron, zinc, and copper were negatively correlated to soil phosphorus.     

Upscaling of the permeability by multiscale wavelet transformations and simulation of multiphase flows in heterogeneous porous media

We describe a new approach for simulation of multiphase flows through heterogeneous porous media, such as oil reservoirs. The method, which is based on the wavelet transformation of the spatial distribution of the single-phase permeabilities, incorporates in the upscaled computational grid all the relevant data on the permeability, porosity, and other important properties of a porous medium at all the length scales. The upscaling method generates a nonuniform computational grid which preserves the resolved structure of the geological model in the near-well zones as well as in the high-permeability sectors and upscales the rest of the geological model. As such, the method is a multiscale one that preserves all the important information across all the relevant length scales. Using a robust front-detection method which eliminates the numerical dispersion by a high-order total variation diminishing method (suitable for the type of nonuniform upscaled grid that we generate), we obtain highly accurate results with a greatly reduced computational cost. The speed-up in the computations is up to over three orders of magnitude, depending on the degree of heterogeneity of the model. To demonstrate the accuracy and efficiency of our methods, five distinct models (including one with fractures) of heterogeneous porous media are considered, and two-phase flows in the models are studied, with and without the capillary pressure.     

The Cimmerian Orogeny in northern Iran: tectono-stratigraphic evidence from the foreland

From the Permian onwards, the Gondwana-derived Iran Plate drifted northward to collide with Eurasia in the Late Triassic, thereby closing the Palaeotethys. This Eo-Cimmerian Orogeny formed the Cimmeride fold-and-thrust belt. The Upper Triassic–Middle Jurassic Shemshak Group of northern Iran is commonly regarded as the Cimmerian foreland molasse. However, our tectono-stratigraphic analysis of the Shemshak Group resulted in a revised and precisely dated model for the Triassic–Jurassic geodynamic evolution of the Iran Plate: initial Cimmerian collision started in the Carnian with subsequent Late Triassic synorogenic peripheral foreland deposition (flysch, lower Shemshak Group). Subduction shifted south in the Norian (onset of Neotethys subduction below Iran) and slab break-off around the Triassic–Jurassic boundary caused rapid uplift of the Cimmerides followed by Liassic post-orogenic molasse (middle Shemshak Group). During the Toarcian–Aalenian (upper Shemshak Group), Neotethys back-arc rifting formed a deep-marine basin, which developed into the oceanic South Caspian Basin during the Late Bajocian–Late Jurassic.     

Flooding and its relationship with land cover change,population growth,and road density

Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve-receiver operating char-acteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC＞90％)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the per-iod between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72％between 2000 and 2017,while the PG rate increased by 51.68％over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.     

Spatial modelling of hazardous elements at waste dumps using geostatistical approach: a case study Sarcheshmeh copper mine,Iran

Mining activities and resulting wastes can be considered as one of the most important sources of hazardous elements in the environment. Knowledge of the spatial distribution of toxic elements in waste dump systems is necessary to assess environmental hazard and strategy. To achieve this goal, this paper investigates spatial distribution of toxic elements using statistical and geostatistical analysis. A total of 58 soil samples were collected, and the amount of As, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb and Zn was then determined at “Sarcheshmeh” copper mine waste dumps. In order to evaluate the presence of multivariate outliers, Mahalanobis distance technique (D ²) was applied and the multivariate outlier samples were removed. This resulted in an increase in correlation coefficient. To reduce dimension of data set, principal component analysis was applied and four principal components were determined which indicate 83.463% of the total variance of data set. Estimated PCs together with the toxic elements maps based on the ordinary kriging display aggregation of toxic elements in some parts, and validity of predictions was evaluated using the leave-one-out cross-validation method. The regression coefficients of estimated and observed values presented the reliability of the kriging estimates. Sequential Gaussian simulation method was applied for principal components due to similar results of estimated principal components and toxic elements. The results of simulation maps are almost identical to estimated outcomes.     

Optimization approach to retrieve soil surface parameters from single-acquisition single-configuration SAR data

This study suggests a novel approach to the retrieval of soil surface parameters using a single-acquisition single-configuration synthetic-aperture radar (SAR) system. Soil surface parameters such as soil moisture and surface roughness are key elements for many environmental studies, including Earth surface water cycles, energy exchange, agriculture, and geology. Remote sensing techniques, especially SAR data, are commonly used to retrieve such soil surface parameters over large areas. Several backscattering models have been proposed for soil surface parameters retrieval from SAR data. However, commonly, these backscattering models require multi configuration SAR data, including multi-polarization, multi-frequency, and multi-incidence angle. Here we propose a methodology that employs single-acquisition single-configuration SAR data for the retrieval of soil surface parameters. The originality is to use single-acquisition single-configuration SAR data to retrieve the soil surface parameters using an optimization approach by the genetic algorithm (GA); we have used the modified Dubois model (MDM) in HH polarization as the backscattering model. Three HH polarization and C band data sets from Quebec (Radarsat-1), Ontario (SIR-C), and Oklahoma (AIRSAR) were analyzed. The retrieved values of soil moisture and soil surface roughness were then compared to ground truth measurements with corresponding parameters. We employed diverse criteria, including the mean absolute error (MAE), the root mean square error (RMSE), the coefficient of performance (CP), and the correlation coefficient to investigate the performance of the proposed methodology. This analysis suggests the capability of the GA for the retrieval of soil surface parameters. Based on our findings, this method presents a viable alternative approach to the retrieval of soil surface parameters when only single-acquisition single-configuration SAR data is available.     

Streamflow drought time series forecasting

Drought is considered to be an extreme climatic event causing significant damage both in the natural environment and in human lives. Due to the important role of drought forecasting in water resources planning and management and the stochastic behavior of drought, a multiplicative seasonal autoregressive integrated moving average (SARIMA) model is applied to the monthly streamflow forecasting of the Zayandehrud River in western Isfahan province, Iran. After forecasting 12 leading month streamflow, four drought thresholds including streamflow mean, monthly streamflow mean, 2-, 5-, 10- and 20-year return period monthly drought and standardized streamflow index were chosen. Both observed and forecasted streamflow showed a drought period with different severity in the lead-time. This study also demonstrates the usefulness of SARIMA models in forecasting, water resources planning and management.