Prediction of copper content in waste dump of Sarcheshmeh copper mine using visible and near-infrared reflectance spectroscopy

The pore structure characteristics of soil are closely related to soil engineering properties. For saline soil distributed in seasonally frozen areas, existing studies have focused on the influence of freeze–thaw cycles on pore structure, while the influence of soluble salt in the soil is not well understood. This study aims to explore the influence of salt content and salt type on the pore structure of freeze-thawed soil. Soil samples with different salt contents (0–2%) and types (bicarbonate salt and sulfate salt) were subjected to 10 freeze–thaw tests, and their pore size distributions (PSDs) were obtained by mercury intrusion porosimetry tests. In addition, the PSDs were quantitatively analyzed by fractal theory. For both salts, the PSDs of the tested soil samples were bimodal after the freeze–thaw cycles, and the porosity of saline soil samples increased with increasing salt content overall. However, the contents of various types of pores in soil samples with two salt types were quite different. The variation in bicarbonate salt content mainly affected the mesopore and macropore contents in the soil samples, and their change trends were opposite to each other. For soil samples with sulfate salt, the porosity and macropore content increased significantly when the salt content exceeded 1%. In addition, the pore structures in saline soil presented fractal characteristics after the freeze–thaw cycles, and the fractal dimension was positively correlated with macropore content. This study may provide references for understanding the engineering properties of saline soil in seasonally frozen areas at the microscale.     

River water quality: Monitoring,statistical models and missing information

The paper comprises three main parts. First there is a review of the need for and nature of monitoring in the British context. Next follows a discussion of available statistical modelling techniques coupled with some original work on the application of Box-Jenkins type models to industrial-urban and rural catchments. Finally, the results of a project to evaluate techniques for handling missing data are discussed. The paper concludes with some more general remarks on the actual application of the results presented.     

Prediction of heavy metals in acid mine drainage using artificial neural network from the Shur River of the Sarcheshmeh porphyry copper mine,Southeast Iran

Water is one of the basic and fundamental requirements for the survival of human beings. Mining of the sulphide mines usually produce a significant amount of acid mine drainage (AMD) contributing to huge amounts of chemical components and heavy metals in the receiving waters. Prediction of the heavy metals in the AMD is important in developing any appropriate remediation strategy. This paper attempts to predict heavy metals (Cu, Fe, Mn, Zn) from the AMD using backpropagation neural network (BPNN), general regression neural network (GRNN) and multiple linear regression (MLR), by taking pH, sulphate (SO₄) and magnesium (Mg) concentrations in the AMD into account in Shur River, Sarcheshmeh porphyry copper deposit, southeast Iran. The comparison between the predicted concentrations and the measured data resulted in the correlation coefficients, R, 0.92, 0.22, 0.92 and 0.92 for Cu, Fe, Mn and Zn ions using BPNN method. Moreover, the R values were 0.89, 0.37, 0.9 and 0.91 for Cu, Fe, Mn, and Zn taking the GRNN method into consideration. However, the correlation coefficients were low for the results predicted by MLR method (0.83, 0.14, 0.9 and 0.85 for Cu, Fe, Mn and Zn ions, respectively). The results further indicate that the ANN can be used as a viable method to rapidly and cost-effectively predict heavy metals in the AMD. The results obtained from this paper can be considered as an easy and cost-effective method to monitor groundwater and surface water affected by AMD.     

Prediction of blast fragmentation using multivariate analysis procedures

An extensive multivariate analysis procedure for prediction of blast fragmentation distribution is presented. Several blasts performed in various mines and rock formations in the world are brought together and evaluated. Blast design parameters, the modulus of elasticity, in situ block size are considered to perform multivariate analysis. The hierarchical cluster analysis is used to separate the blasts data into different groups of similarity. Group memberships were checked by the discriminant analysis. The multivariate regression analysis was applied to develop prediction equations for the estimation of the mean particle size of muckpiles. Two different prediction equations were developed based on the rock stiffness. Validation of the proposed equations on various mines is presented and the capability of the prediction equations was compared with one of the most applied fragmentation distribution models appearing in the blasting literature. Prediction capability of the proposed models was found to be strong. Diversity of the blasts data used is one of the most important aspects of the developed models. The models are not complex and suitable for practical use at mines. Copyright © 2010 John Wiley & Sons, Ltd.     

用卫星高光谱数据提取江西德兴铜矿矿山废水的pH值污染指标

在对德兴铜矿矿山废水的光谱特征深入分析研究的基础上,总结了不同类型水体(酸性水、碱性水以及河流水)的特征光谱,并利用地物谱特征开展矿山废水pH值污染指标提取研究。针对水体光谱反射率低、特征光谱不明显的特点,采用矿区卫星Hyperion高光谱数据,应用ISA算法和掩膜技术识别出水体分布并进一步与MNF变换有效结合,根据波段散点图进行不同pH值水体的有效分割。为矿山废水污染的诊断和监测提供了新技术和理论支撑。     

用卫星高光谱数据提取江西德兴铜矿矿山废水的pH值污染指标

在对德兴铜矿矿山废水的光谱特征深入分析研究的基础上,总结了不同类型水体(酸性水、碱性水以及河流水)的特征光谱,并利用地物谱特征开展矿山废水pH值污染指标提取研究。针对水体光谱反射率低、特征光谱不明显的特点,采用矿区卫星Hyperion高光谱数据,应用ISA算法和掩膜技术识别出水体分布并进一步与MNF变换有效结合,根据波段散点图进行不同pH值水体的有效分割。为矿山废水污染的诊断和监测提供了新技术和理论支撑。     

Geochemical evolution and quality assessment of water resources in the Sarcheshmeh copper mine area (Iran) using multivariate statistical techniques

Hydrochemical data were gathered throughout the last 12 years from 57 sampling stations in the drainage basin of the Sarcheshmeh copper mine, Kerman Province, Iran. The mean values of these data for each sampling station were used to evaluate water quality and to determine processes that control water chemistry. Principal component analyses specified the oxidation of sulfide minerals, dissolution of carbonate and sulfate minerals and weathering of silicate minerals as the principal processes responsible for the chemical composition of water in the study area. Q-mode cluster analysis revealed three main water groups. The first group had a Ca-HCO₃–SO₄ composition whereas the second and third groups had Ca–SO₄ and Ca–Mg–SO₄ composition, respectively. The results of this study clearly indicated the role of sulfide minerals oxidation and the buffering processes in the geochemical evolution of water in the Sarcheshmeh area. Due to these processes, extensive changes occurred in the chemical composition of water by passage through the mining area or waste and low-grade dumps, so that the fresh water of the peripheral area of the pit evolved to an acid water rich in sulfate and heavy metals at the outlet of the pit and in the seepages of waste and low-grade dumps.     

江西九江城门山铜矿汞气测量找矿方法

在城门山铜矿系统开展了土壤壤中汞、热释汞、全汞的测量.结果表明,花岗闪长斑岩和破碎带上覆的土壤中具有明显的壤中汞气、热释汞的富集,土壤中汞气的主要源是深部的硫化物矿体.汞异常能够准确地反映出下伏矿体的赋存部位.土壤全汞因为本底汞的影响不能很好地反映矿化信息,利用土壤全量汞与热释汞含量的比值来抑制本底汞,突显热释汞,能更清晰地显示深部的矿化信息,是示踪深部矿化信息的有用的地球化学指标.     

江西九江城门山铜矿汞气测量找矿方法

在城门山铜矿系统开展了土壤壤中汞、热释汞、全汞的测量。结果表明,花岗闪长斑岩和破碎带上覆的土壤中具有明显的壤中汞气、热释汞的富集,土壤中汞气的主要源是深部的硫化物矿体。汞异常能够准确地反映出下伏矿体的赋存部位。土壤全汞因为本底汞的影响不能很好地反映矿化信息,利用土壤全量汞与热释汞含量的比值来抑制本底汞,突显热释汞,能更清晰地显示深部的矿化信息,是示踪深部矿化信息的有用的地球化学指标。     

湖南国庆矿区地电化学方法寻找隐伏铜矿预测研究

本文通过在湖南国庆矿区开展地电化学方法测量工作,运用元素变异系数对国庆矿区地电提取测量的14种元素进行找矿潜力分析,得出Cu、Bi两种元素成矿潜力较大;通过R型聚类分析、R型因子分析明确了14种元素的组合特征,划分了三个元素组合(即F1组合:Cr-Co-Ni-Zn-As-Mo-Sb-W-Pb,F2组合:Cu-Bi,F3组合:Au-Ag-Hg)。运用直方图解法、累频法、EDA分别计算研究区异常下限,并进行对比,显示累频法处理本区域地电提取数据较为合理。运用单点元素组合衬度累加法在研究区内圈定组合异常,根据异常的套合情况在研究区内圈定了3个找矿远景区,为在研究区内寻找隐伏铜矿提供了线索与依据。     

江西九江城门山铜矿汞气测量找矿方法

在城门山铜矿系统开展了土壤壤中汞、热释汞、全汞的测量。结果表明,花岗闪长斑岩和破碎带上覆的土壤中具有明显的壤中汞气、热释汞的富集,土壤中汞气的主要源是深部的硫化物矿体。汞异常能够准确地反映出下伏矿体的赋存部位。土壤全汞因为本底汞的影响不能很好地反映矿化信息,利用土壤全量汞与热释汞含量的比值来抑制本底汞,突显热释汞,能更清晰地显示深部的矿化信息,是示踪深部矿化信息的有用的地球化学指标。     

Geochemical appraisal of mine discharge and tailing at Malanjkhand copper mine,India

Geophysical investigation using Vertical Electrical Sounding (VES), Electrical Resistivity Tomography (ERT) and Seismic Refraction at a proposed conference center site along Ajibode-Labani road, Ibadan, southwestern Nigeria has been carried out. The investigation aims at characterizing and delineating the subsurface strata to understand the weathered profile at the site. Understanding the weathered profile is essential in determining the suitability of the site for engineering construction of the future conference center. A total of 25 VES and 10 ERT profiles were acquired in a systematic grid pattern using both Schlumberger andWenner configurations with Allied omega terrameter. TheVES data were processed and analyzed using WinResist and the ERT data were inverted using RES2DINV. The data were combined to form a 3-D data set of the site and RES3DINV was used to produce the depth slices. Seismic refraction data were also acquired with an ABEM seismograph and processed using SeisImager and Fajseis software. Seismic data were used in understanding the velocity distribution and thickness. The results of VES, ERT and seismic refraction show good correlation. Four sub-surface layers were delineated: top layer of reworked sand, clayey sand/ lateritic hard pan, clay/ sandy clay and fracture/ fresh basement. The 3-D model permits a pictorial view of the sub-surface in relation to materials that overlie the basement. The thickness of unconsolidated materials to bedrock varies from 2.7 m to 12.2 m which revealed inhomogeneity in weathering under the shallow sub-surface. It is found that the integrated geophysical tool is well suited to characterize and delineate sub-surface structure (weathered profile) for engineering site characterization.     

Flood susceptibility mapping using integrated bivariate and multivariate statistical models

Flooding can have catastrophic effects on human lives and livelihoods and thus comprehensive flood management is needed. Such management requires information on the hydrologic, geotechnical, environmental, social, and economic aspects of flooding. The number of flood events that took place in Busan, South Korea, in 2009 exceeded the normal situation for that city. Mapping the susceptible areas helps us to understand flood trends and can aid in appropriate planning and flood prevention. In this study, a combination of bivariate probability analysis and multivariate logistic regression was used to produce flood susceptibility maps of Busan City. The main aim of this research was to overcome the weakness of logistic regression regarding bivariate probability capabilities. A flood inventory map with a total of 160 flood locations was extracted from various sources. Then, the flood inventory was randomly split into a testing dataset 70 % for training the models and the remaining 30 %, which was used for validation. Independent variables datasets included the rainfall, digital elevation model, slope, curvature, geology, green farmland, rivers, slope, soil drainage, soil effect, soil texture, stream power index, timber age, timber density, timber diameter, and timber type. The impact of each independent variable on flooding was evaluated by analyzing each independent variable with the dependent flood layer. The validation dataset, which was not used for model generation, was used to evaluate the flood susceptibility map using the prediction rate method. The results of the accuracy assessment showed a success rate of 92.7 % and a prediction rate of 82.3 %.     

Prediction of river flow using hybrid neuro-fuzzy models

The complex nature of hydrological phenomena, like rainfall and river flow, causes some limitations for some admired soft computing models in order to predict the phenomenon. Evolutionary algorithms (EA) are novel methods that used to cover the weaknesses of the classic training algorithms, such as trapping in local optima, poor performance in networks with large parameters, over-fitting, and etc. In this study, some evolutionary algorithms, including genetic algorithm (GA), ant colony optimization for continuous domain (ACO_R), and particle swarm optimization (PSO), have been used to train adaptive neuro-fuzzy inference system (ANFIS) in order to predict river flow. For this purpose, classic and hybrid ANFIS models were trained using river flow data obtained from upstream stations to predict 1-, 3-, 5-, and 7-day ahead river flow of downstream station. The best inputs were selected using correlation coefficient and a sensitivity analysis test (cosine amplitude). The results showed that PSO improved the performance of classic ANFIS in all the periods such that the averages of coefficient of determination, R², root mean square error, RMSE (m³/s), mean absolute relative error, MARE, and Nash-Sutcliffe efficiency coefficient (NSE) were improved up to 0.19, 0.30, 43.8, and 0.13%, respectively. Classic ANFIS was only capable to predict river flow in 1-day ahead while EA improved this ability to 5-day ahead. Cosine amplitude method was recognized as an appropriate sensitivity analysis method in order to select the best inputs.     

Using dimensional-regression analysis to predict the mean particle size of fragmentation by blasting at the Sungun copper mine

Arabian Journal of Geosciences - A methodology was founded on the basis of a dimensional analysis procedure, together with multivariate nonlinear regression analysis which is used to predict mean...     

Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County,Three Gorges Reservoir,China

Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou County, China. First, a landslide inventory map was prepared using earlier geotechnical investigation reports, aerial images, and field surveys. Then, the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis. To determine the most effective causal factors, landslide susceptibility evaluations were performed based on four cases with different combinations of factors (“cases”). In the analysis, 465 (70%) landslide locations were randomly selected for model training, and 200 (30%) landslide locations were selected for verification. The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model. Finally, the receiver operating characteristic (ROC) curve was used to verify the accuracy of each model’s results for its respective optimal case. The ROC curve analysis showed that the machine learning model performed better than the other three models, and among the three statistical models, the IOE model with weight coefficients was superior.

     

基于数据包络分析的统计预测模型有效性评价

统计预测模型是进行中长期水文预报的主要手段之一,在统计预测模型建模过程中面临的一个重要问题是如何从诸多待选模型中挑选出一个预测投入较低、预测精度较高的模型。针对这一多属性综合评价问题,提出了利用数据包络分析中的CCR模型进行水文统计预测模型综合评价的方法。模型的输入指标包括预测因子指标和模型参数指标,输出指标为模型精度评价指标,评价结果为模型的相对效率。作为典型案例,对参考作物腾发量预测的20个径向基函数网络模型的有效性进行了评价,结果表明该评价方法是可行的。模型中预测旬参考作物腾发量的关键因子是最高、最低温度,其次是风速,再次是日照时数;将预测时段所属的旬序号作为网络输入可显著提高模型预测精度和相对效率。     

Application of multivariate statistical methods to indicate the origin and geochemical behavior of potentially hazardous elements in sediment around the Sarcheshmeh copper mine,SE Iran

Multivariate statistical techniques, i.e., correlation coefficient analysis, principal components analysis (PCA), and hierarchical cluster analysis (CA), were applied to the total and water-soluble concentrations of potentially hazardous metals in sediments associated with the Sarcheshmeh mine, one of the largest Oligo-Miocene porphyry copper deposits in the world. The samples were analyzed for hazardous metal concentration levels by inductively coupled plasma mass spectrometry method. Results indicate that the contaminant metals As, Cd, Cu, Mo, S, Sb, Sn, Se, Pb, and Zn were positively correlated with the total concentrations. These hazardous metals also have strong association in the PCA and CA results. Different anthropic versus natural sources of contaminant metals were distinguished by using CA method. Water-soluble fraction of hazardous metals showed that the hydro-geochemical behavior of these metals in sediments is different considerably. Elements such as Cd, Co, Cr, Cu, Fe, Mn, Ni, S, and Zn are readily water soluble from contaminated samples, especially from evaporative mineral phases, while the release of As, Mo, Sb, and Pb into the water is limited by adsorption processes. Results obtained from the application of multivariate techniques on the water-soluble fraction data set show that the hazardous metals are categorized into three groups including (1) Ni, S, Co, Cu, Cr, and Fe; (2) Se, Mn, Cd, and Zn; and (3) Sb, As, Mo, and Sn. This classification describes the hydro-geochemical behavior of hazardous metals in water–sediment environments of the Sarcheshmeh porphyry copper mine and can be used as a basis in remedial and treatment strategies.     

基于遥感信息提取技术的泰国铜金矿成矿预测

以泰国铜金热液型矿区为例,根据地质异常理论、典型铜金矿成矿模式,利用ETM+遥感影像、地质图、矿产地质数据库等多元地学数据,结合GIS技术,提取研究区地质（地层、构造）、遥感(羟基、铁染蚀变信息和线环构造)等致矿信息,采用证据权法对该区铜金矿进行成矿预测,并结合研究区主要控矿地质要素,圈定研究区铜金找矿有利地段,为该地区的找矿工作提供参考。     

Prediction of uniaxial compressive strength of sandstones using petrography-based models

The uniaxial compressive strength of intact rock is the main parameter used in almost all engineering projects. The uniaxial compressive strength test requires high quality core samples of regular geometry. The standard cores cannot always be extracted from weak, highly fractured, thinly bedded, foliated and/or block-in-matrix rocks. For this reason, the simple prediction models become attractive for engineering geologists. Although, the sandstone is one of the most abundant rock type, a general prediction model for the uniaxial compressive strength of sandstones does not exist in the literature. The main purposes of the study are to investigate the relationships between strength and petrographical properties of sandstones, to construct a database as large as possible, to perform a logical parameter selection routine, to discuss the key petrographical parameters governing the uniaxial compressive strength of sandstones and to develop a general prediction model for the uniaxial compressive strength of sandstones. During the analyses, a total of 138 cases including uniaxial compressive strength and petrographic properties were employed. Independent variables for the multiple prediction model were selected as quartz content, packing density and concavo–convex type grain contact. Using these independent variables, two different prediction models such as multiple regression and ANN were developed. Also, a routine for the selection of the best prediction model was proposed in the study. The constructed models were checked by using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes.