Exploration
Kamran Mostafaei; Mohammad Nabi Kianpour; Mahyar Yousefi; Meisam Saleki
Abstract
Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting ...
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Discrimination of geochemical anomalies from background is a challenge in that elemental dispersion patterns are affected by a variety of geological factors, which vary from one to another area. While statistical and fractal methods are commonly employed for anomaly detection, they struggle with selecting optimal thresholds. This study proposes the Grey Wolf Optimizer (GWO) algorithm as a novel approach for identifying the optimal boundary between anomalies and background. Stream sediment geochemical data from a copper-mineralized area of the Sarduiyeh-Baft sheets in southeast Iran were utilized for analysis. The Geochemical Mineralization Probability Index (GMPI) was first calculated for Cu-Au, Mo-As, Pb-Zn, and porphyry distributions. Subsequently, fractal methods were used to identify anomalous populations within each GMPI. The GWO algorithm was then applied to these distributions to determine the optimal thresholds. Risk analysis, calculated as the ratio of covered copper occurrences to the covered area, revealed superior reliability for the GWO-derived limit compared to those obtained using fractal methods. For porphyry GMPI values, while the fractal reliability indices are 0.127, 0.44, and 0.5, the GWO limit achieved a value of 0.66. Risk analysis for Cu-Au distribution also caused more desired result for GWO limit rather that fractal ones. This demonstrates the enhanced performance and superior reliability of the GWO algorithm for optimizing anomaly detection thresholds in GMPI data.
Saeed Saadat
Abstract
In this work, the results of nearly 1400 stream sediment sample analysis are processed to better understand environmental pollution caused by mining activities in Eastern Iran. The stream sediment samples are analyzed for As, Sb, Fe, Cr, Ni, Co, Cu, Zn, Pb, Sr, and Hg. The mean concentration of these ...
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In this work, the results of nearly 1400 stream sediment sample analysis are processed to better understand environmental pollution caused by mining activities in Eastern Iran. The stream sediment samples are analyzed for As, Sb, Fe, Cr, Ni, Co, Cu, Zn, Pb, Sr, and Hg. The mean concentration of these elements follows the decreasing order of Fe > Sr > Zn > Cr > Cu > Ni > Co > Pb > As > Sb > Hg. Based on the assessment of pollution, extremely severe enrichment factor Co (EF > 25), and high enrichment of Sb, Hg, Cr, and Sr (EF > 10) are detected. Specifically, Cr and Ni in southern stream sediments show significantly elevated concentrations compared to the others. The range of the contamination factor varies from CF < 1 to CF > 6 for most elements. Geo-accumulation index shows high contamination levels by Cr and Co and high to severe contamination by Sb. The risk indices are low for all elements except for As and Co in the eastern part of the studied area. Principal component analysis, Spearman correlation coefficient, and cluster analysis are used to demonstrate similarities and differences between the elements. Pollution indices show that contaminations in some samples are the consequence of gold mineralization. The high correlation of Cu, Zn, and Sb is due to the sulfide mineralization of gold. The high correlation of Cr and Ni corresponds to ultramafic rocks and ophiolitic series. This study focuses on the impact of mining activities, even at early stages on the dispersion of some heavy metals in stream sediments. Based on the results presented here, while most contamination in the target area is rooted in geochemical and mineralization processes, mining activity also contributes to soil pollution for certain elements such as Cu and Zn. The most affected stream sediments are those within the vicinity of mining areas and attention should be paid to potential risks to the environment particularly during gold mining activities.
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.
Exploitation
A. Aryafar; H. Moeini
Abstract
Anomaly separation using stream sediment geochemical data has an essential role in regional exploration. Many different techniques have been proposed to distinguish anomalous from study area. In this research, a continuous restricted Boltzmann machine (CRBM), which is a generative stochastic artificial ...
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Anomaly separation using stream sediment geochemical data has an essential role in regional exploration. Many different techniques have been proposed to distinguish anomalous from study area. In this research, a continuous restricted Boltzmann machine (CRBM), which is a generative stochastic artificial neural network, was used to recognize the mineral potential area in Korit 1:100000 sheet, located 15 km south of Tabas, South Khorasan Province (East of Iran). For this purpose, 470 geochemical stream sediment samples were collected from the study area and analyzed for 36 elements. In order to achieve the goal, in the first step, the robust factor analysis on compositional data was applied to reduce the data dimension and to limit the multivariate analysis by selecting the main components of mineralization. In this procedure, the third factor (out of 6) consisting of Cu, Pb, Zn, Sn, and Sb, related to the metallogenic properties, was considered as the input set in CRBM. In continuation, the CRBM structure with the best efficiency after trying different parameters was stabilized. High-identified error values or anomalies were exteracted using two different thresholds (ASC and ASE) after training with the whole data and reconstructing it by CRBM. The anomalies were then mapped. These indicated the promissing areas. The field studies and existing mining indices confirmly demonestrated the results obtained by CRBM.
