Environment
G.U Sikakwe
Abstract
In this work, the concentrations of the potentially toxic elements in stream sediments in SE Nigeria were assessed for pollution monitoring in mining, quarrying, and farming areas. The levels of iron, molybdenum, vanadium, copper, lead, zinc, nickel, cobalt, manganese, chromium, barium, and beryllium ...
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In this work, the concentrations of the potentially toxic elements in stream sediments in SE Nigeria were assessed for pollution monitoring in mining, quarrying, and farming areas. The levels of iron, molybdenum, vanadium, copper, lead, zinc, nickel, cobalt, manganese, chromium, barium, and beryllium were determined. The concentrations of the elements were in the order of Fe > Ba > Mn > Cr > Zn > Pb > Cu > Co > Ni > As > Mo. There were significant positive correlations at P < 0.01 between Mo and Cu (r = 0.734), Mo and Pb (r = 0.811), and Cu and Pb (r = 0.836). The others were between Cu and V (r = 0.748), Pb and V (r = 0.793), Fe and V (r = 0.905), Fe and Be (r = 0.703), V and Be (r = 0.830), Cu and Pb (r = 0.778), and Fe and V (r = 0.905). The geoaccumulation index values were classified as polluted (0-1) to moderately polluted (1-2). The enrichment factors fell into moderate, significant, and very high enrichment. Pb, Co, and Ba attained significant enrichment factors. The potential ecological risk showed a strong risk level "C" in only three locations. Arsenic was a strong factor carrying risk. The potential ecological risk (EiR) trend was EiR (AS) > EiR (Pb)> EiR (Cu) > EiR (Co) > EiR (Cr) > EiR (V) > EiR (Ni) > EiR (Zn). Ba, Pb, and As should be monitored further to determine their source and recommend possible remedial measures. The result of this work could be used to improve water management efficiency and serve as a benchmark of vulnerability assessment of the studied area. This could also be useful for future impact assessment and adequate planning of mining and farming areas. In addition, the result obtained could assist the scientists to make appropriate environmental management strategies to reduce the influence of metal contamination triggered from the mining sites and farming areas both in Nigeria and globally.
Environment
H. Nikoogoftar; A. Hezarkhani
Abstract
In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of the Artificial Neural Networks (ANNs) technique in ore grade estimation. Different training algorithms and numbers of hidden neurons are applied to estimate Cu grade of borehole data in the hypogene ...
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In this paper, we aim to achieve two specific objectives. The first one is to examine the applicability of the Artificial Neural Networks (ANNs) technique in ore grade estimation. Different training algorithms and numbers of hidden neurons are applied to estimate Cu grade of borehole data in the hypogene zone of porphyry copper-gold deposit, Masjed-Daghi, East Azerbaijan Province (Iran). The efficacy of ANNs in function-learning and estimation is compared with ordinary kriging (OK). As the kriging algorithms smooth the data, their applicability in the pre-processing of data for fractal analysis is not conducive. ANNs can be introduced as an alternative for this kind of problem. Secondly, we aim to delineate the potassic and phyllic alteration regions in the hypogene zone of Cu-Au porphyry deposit based on the estimation obtained by the ANNs and OK methods, and utilize the Concentration-Volume (C-V) fractal model. In this regard, at first, C-V log-log is generated based on the ANN results. The plots are then used to determine the Cu threshold values for the alteration zones. To investigate the correlation between the geological model and C-V fractal results, the log ratio matrix is applied. The results obtained show that Cu values less than 0.38% from ANNs have more overlapped voxels with phyllic alteration zone by an overall accuracy of 0.72. Spatial correlation between the potassic alteration zones resulting from 3D geological modeling and high concentration zones in C-V fractal model show that Cu values greater than 0.38% have more voxels overlapped with the potassic alteration zone by an overall accuracy of 0.76. Generally, the results obtained show that a combination of the ANNs and C-V fractal methods can be a suitable and robust tool for quantitative modeling of alteration zones instead of the qualitative methods.
Environment
H. Mahdiyanfar
Abstract
Detection of deep and hidden mineralization using the surface geochemical data is a challenging subject in the mineral exploration. In this work, a novel scenario based on the spectrum–area fractal analysis (SAFA) and the principal component analysis (PCA) has been applied to distinguish and delineate ...
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Detection of deep and hidden mineralization using the surface geochemical data is a challenging subject in the mineral exploration. In this work, a novel scenario based on the spectrum–area fractal analysis (SAFA) and the principal component analysis (PCA) has been applied to distinguish and delineate the blind and deep Mo anomaly in the Dalli Cu–Au porphyry mineralization area. The Dalli mineral deposit is located on the volcanic–plutonic belt of Sahand–Bazman in the central part of Iran. The geochemical data was transformed to the frequency domain using the Fourier transformation, and SAFA was applied for classification of geochemical frequencies and detection of geochemical populations. The very low-frequency signals in the fractal method were separated using the low-pass filter function and were interpreted using PCA. This scenario demonstrates that the Mo element has an important role in the mineralization phase in the very low-frequency signals that are related to the deep mineralization; it is an important innovation in this work. Then the Mo geochemical anomaly has been mapped using the inverse Fourier transformation. This research work shows that the high-power spectrum values in SAFA are related to the background elements and the deep mineralization. Two exploratory boreholes drilled inside and outside the deep Mo anomaly area properly confirm the results of the proposed approach.
Environment
B. Shokouh Saljoughi; A. Hezarkhani
Abstract
The Shahr-e-Babak district, as the studied area, is known for its large Cu resources. It is located in the southern side of the Central Iranian volcano–sedimentary complex in SE Iran. Shahr-e-Babak is currently facing a shortage of resources, and therefore, mineral exploration in the deeper and ...
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The Shahr-e-Babak district, as the studied area, is known for its large Cu resources. It is located in the southern side of the Central Iranian volcano–sedimentary complex in SE Iran. Shahr-e-Babak is currently facing a shortage of resources, and therefore, mineral exploration in the deeper and peripheral spaces has become a high priority in this area. This work aims to identify the geochemical anomalies associated with the Cu mineralization using the Spectrum–Area (S–A) multi-fractal and Wavelet Neural Network (WNN) methods. At first, the Factor Analysis (FA) is applied to integrate the multi-geochemical variables of a regional stream sediment dataset related to major mineralization elements in the studied area. Then the S–A model is applied to decompose the mixed geochemical patterns obtained from FA and compare with the results obtained from the WNN method. The S–A model, based on the distinct anisotropic scaling properties, reveals the local anomalies due to the consideration of the spatial characteristics of the geochemical variables. Most of the research works show that the capability (i.e. classification, pattern matching, optimization, and prediction) of an ANN considering its successful application is suitable for inheriting uncertainties and imperfections that are found in mining engineering problems. In this paper, an alternative method is presented for mineral prospecting based on the integration of wavelet theory and ANN or wavelet network. The results obtained for the WNN method are in a good agreement with the known deposits, indicating that the WNN method with Morlet transfer function consists of a highly complex ability to learn and track unknown/undefined complicated systems. The hybrid method of FA, S–A, and WNN employed in this work is useful to identify anomalies associated with the Cu mineralization for further exploration of mineral resources.
Environment
S. Abbaszade; F. Mohammad Torab; A. Alikhani; H. Molayemat
Abstract
In geochemical exploration, there are various techniques such as univariate and multivariate statistical methods available for recognition of anomalous areas. Univariate techniques are usually utilized to estimate the threshold value, which is the smallest quantity among the values representing the anomalous ...
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In geochemical exploration, there are various techniques such as univariate and multivariate statistical methods available for recognition of anomalous areas. Univariate techniques are usually utilized to estimate the threshold value, which is the smallest quantity among the values representing the anomalous areas. In this work, a combination of the Sequential Gaussian Simulation (SGS) and Gap Statistics (GS) methods was utilized as a new technique to estimate the threshold and to visualize the anomalous regions in the Hararan area, which is located in SE Iran, and consists of copper mineralization that seems to be connected to a porphyry Cu-Mo system. Furthermore, the most important advantage of this method is the reliable assessment of the anomalous areas. In other words, the anomalous areas were discriminated in terms of their probability values. The regions with high probability values were reliable and appropriate to locate the drilling points for a detailed exploration. It not only decreases the risk, cost, and time of exploration but also increases the drilling point reliability and precision of reserve estimation after drilling. In this research work, the results of analysis of 607 lithogeochemical samples for the element Cu were used. The SGS method was performed on the transformed data and 50 realizations were obtained. In the next step, the back-transformed realizations were utilized to obtain an E-type map, which was the average of 50 realizations. Moreover, the results of the GS method showed that the Cu threshold value was 228 ppm in the area. Therefore, using the E-type map, areas with values greater than 228 ppm were introduced as the anomalous areas. Finally, the probability map of the exceeding threshold values was acquired, and the anomalous districts located in the southern part of the studied area were considered as more reliable regions for future detailed exploration and drilling.
Environment
B. Shokouh Saljoughi; A. Hezarkhani; E. Farahbakhsh
Abstract
The most significant aspect of a geochemical exploration program is to define and separate the anomalous values from the background. In the past decades, geochemical anomalies have been identified by means of various methods. Most of the conventional statistical methods aiming at defining the geochemical ...
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The most significant aspect of a geochemical exploration program is to define and separate the anomalous values from the background. In the past decades, geochemical anomalies have been identified by means of various methods. Most of the conventional statistical methods aiming at defining the geochemical concentration thresholds for separating anomalies from the background have limited the efficiency in the areas with complex geological settings. In this work, three methods including the Concentration-Area (C-A) and Spectrum-Area (S-A) fractal models, and the U-statistic method are applied to identify the geochemical anomalies in Avanj porphyry system due to a complex geological and tectonic setting. The results obtained show that the S-A and U-statistic methods present more acceptable outputs than the C-A method. The C-A model acts well to identify the geochemical anomalies within a region including a simple geochemical background; however, the model has limitations within a region including a complex geological setting, where each sub-area is characterized by different geochemical fields. The U-statistic method, by considering the location of sampling points, their spatial relation, and radius of influence for each point in the estimation of anomaly location, overcomes the limitations of the C-A model. The S-A model is a powerful tool to decompose mixed geochemical patterns into a geochemical anomaly map and a varied geochemical background map. The output of this method shows the analysis of geochemical data in the frequency domain, which can provide new exploratory information that may not be revealed in the spatial domain. Eventually, it can be pointed out that the accuracy of the S-A fractal model for determining the thresholds is higher than the other two methods mentioned.
Environment
N. Zandy Ilghani; F. Ghadimi; M. Ghomi
Abstract
The Haft-Savaran Pb-Zn mineralization zone with the lower Jurassic age is located in the southern basin of Arak and Malayer-Isfahan metallogenic belt of Iran. Based upon the geological map of the Haft-Savaran area, the sandstone and shale of lower Jurassic are the main rocks of Pb-Zn deposit. In this ...
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The Haft-Savaran Pb-Zn mineralization zone with the lower Jurassic age is located in the southern basin of Arak and Malayer-Isfahan metallogenic belt of Iran. Based upon the geological map of the Haft-Savaran area, the sandstone and shale of lower Jurassic are the main rocks of Pb-Zn deposit. In this area, 170samples were taken from 33 boreholes, and44 elements were measured by the ICP-MS method. Adaptation of the alteration index and Pb–Zn mineralization was investigated in this work. The model was created based on the Sericitic, Spitz-Darling, Alkali, Hashimoto, and Silicification Indices in all boreholes. This work showed that the Sericite, Hashimoto, Spitz-Darling, and Silicification indices increased around mineralization, and the alkali index decreased around it. Development of the alteration indices indicates that direction of the ore-bearing solution is NE-SW, and that this trend is consistent with the faults in the area. Based upon the 3D models and other data interpretations, Pb–Zn and elements such as Fe, Mn, Cr, and Ni have deposited within the alteration zones.
