Characterisation of the Sarcheshmeh copper mine tailings,Kerman province,southeast of Iran

Mineral processing operation at the Sarcheshmeh porphyry copper mine has produced huge quantities of tailings materials containing sulphide minerals in particular pyrite. These tailings materials were geochemically and mineralogically characterised to assess pyrite and chalcopyrite oxidation, acid mine drainage generation, and trace element mobility to lead development of a proper remediation plan. Five vertical trenches up to 4.2 m deep were excavated from the tailings surface, and 70 solid samples were taken in 0.3 m intervals. The samples were first mineralogically analysed. Pyrite was the main sulphide mineral found in the tailings. The gangue minerals include quartz ± muscovite–illite ± chlorite ± albite ± orthoclase ± halite. The samples were geochemically analysed for total concentrations of 62 elements, paste pH, SO₄ ^2?, CO₃ ^2?, and HCO₃ ^?. The maximum concentrations of SO₄ ^2? (1,300, 1,170, 1,852, 1,960 and 837 mg/L) were observed at a depth of 0.9 m in profiles A, B, C, D and E, respectively. The tailings have a high acid-producing potential and low acid-neutralising potential (pyrite 4–6 wt %, calcite 1 wt %). Fe₂(SO₄)₃, CuSO₄, MgSO₄ and MnSO₄ were the dominant secondary sulphate minerals in the tailings. The lowest pH values (2.9, 3 and 3) were measured at a depth of 0.3 m in the profiles A, B and C, 3.9 at a depth of 0.6 m in the profile D and 3 at a depth of 0.9 m in the profile E. The upper portions of the profiles C (1.8 m) and D (2.1 m) were moderately oxidised, while oxidation in the profiles A, B and E did not extend more than 1.2, 1.2 and 1.5 m beneath the tailings surface. Zn, Pb, Rb, U, Hf, Nd, Zr and Ga show almost a constant trend with depth. Cd, Sr, Th, La and Ce increased with increasing depth of the tailings materials while, Co, V, Ti, Cr, Cu, As, Mn, Ag, Mo and Ni exhibit initially a decreasing trend from tailings surface to the depths that vary between 0.9 and 1.2. They then remained constant with the depth. The results show pyrite and chalcopyrite oxidation at surface layers of the tailings and subsequent leaching of the oxidation products and trace elements by infiltrated atmospheric precipitation.     

Assessment of fractal dimension and geometrical characteristics of the landslides identified in North of Tehran,Iran

The aim of the presented study is to assess the fractal dimension (D) and the geometrical characteristics (length and width) of the landslides identified in North of Tehran, Iran. At first, the landslide locations (528 landslides) were identified by interpretation of aerial photographs, satellite images and field surveys, and then to calculate the fractal dimension (D), we used the computer programming named as FRACEK. In the next step, geometrical characteristics of each landslide such as length (L) and width (W) were calculated by ArcGIS software. The landslide polygons were digitized from the mentioned landslide inventory map and rotated based on movement direction. The fractal dimension for all landslides varied between 1.665 and 1.968. Subsequently, the relationship between the length/width ratios and theirs fractal D values for 528 landslides was calculated. The results showed that correlation coefficients (R), which are different regression models such as exponential, linear, logarithmic, polynomial, and power, between D and L/W ratio are relatively high, respectively (0.75, 0.75, 0.76, 0.78, and 0.75). It can be concluded that the fractal dimension values and geometry characteristics of landslides would be useful indices for the management of hazardous areas, susceptible slopes, land use planning, and landslide hazard mitigation.     

Volume,gravitational potential energy reduction,and regional centroid position change in the wake of landslides triggered by the 14 April 2010 Yushu earthquake of China

In recent years, earthquake-triggered landslides have attracted much attention in the scientific community as a main form of seismic ground response. However, little work has been performed concerning the volume and gravitational potential energy reduction of earthquake-triggered landslides and their severe effect on landscape change. This paper presents a quantitative study on the volume, gravitational potential energy reduction, and change in landscape related to landslides triggered by the 14 April 2010 Yushu earthquake. At least 2,036 landslides were triggered by the earthquake. A total landslide scar area of 1.194 km² was delineated from the visual interpretation of aerial photographs and satellite images and was supported by selected field checking. In this paper, we focus on possible answers to the following five questions: (1) What is the total volume of the 2,036 landslides triggered by the earthquake, and what is the average landslide erosion thickness in the earthquake-stricken area? (2) What are the elevations of all landslide materials in relation to pre- and post-landsliding? (3) How much was the gravitational potential energy reduced due to the sliding of these landslide materials? (4) What is the average elevation change caused by these landslides in the study area? (5) What is the vertical change of the regional centroid position above sea level, as induced by these landslides? It is concluded that the total volume of the 2,036 landslides is 2.9399?×?10⁶ m³. The landslide erosion thickness throughout the study area is 2.02 mm. The materials of these landslides moved from an elevation of 4,145.243 to 4,104.697 m, resulting in a decreased distance of 40.546 m. The gravitational potential energy reduction related to the landslides triggered by the earthquake was 2.9213?×?10¹² J. The average regional elevation of the study area is 4,427.160 m, a value consistent with the assumption that the accumulated materials were remained in situ. This value changes from 4,427.160 to 4,427.158 m with all landslide materials moved out of the study area, resulting in a reduction in elevation of 2 mm. Based on the assumption that all landslide materials moved out of the study area, the elevations of the centroid of the study area’s crust changed from 2,222.45967 to 2,222.45867 m, which means the centroid value decreased by 1 mm. This value is 0.001 mm when assuming that the materials were remained in situ, which is almost negligible, compared with the situation of “all landslide materials moved out of the study area.”     

Investigating 2-D MT inversion codes using real field data

There are currently a significant number of two-dimensional (2-D) and three-dimensional (3-D) inversion codes available for magnetotelluric (MT) data. Through various 2-D inversion algorithms suggested so far, the classical Occam's inversion, the data space Occam's inversion, the nonlinear conjugate gradient (NLCG) method, and the Gauss–Newton (GN) method are fundamental driving methods to find optimum earth models, and OCCAM, DASOCC, NLCG, and MT2DInvMatlab are possible candidates one can find in the public domain that implement these algorithms for 2-D MT inversions, respectively. In this study, we investigate the pros and cons (strength and weakness) of these codes to help one use them efficiently in practical works and, as an introductory guide, further develop (sophisticate or extend) them, especially for the 3-D case. To achieve this goal, we applied each one of the four aforementioned codes on a profile of real MT field dataset. Then, further investigations have been done by performing several inversion tests to see how each code can find the appropriate model to reconstruct the subsurface resistivity structure. Numerical experiments show that the two parameters, regularization and target misfit, in addition to the main criteria of inversion (such as the forward and the sensitivities calculation method, and the type of inversion algorithm), are very important to produce the expected model in inversion. The regularization parameter that acts to trade off between model norm and data misfit can affect the inversion process in terms of both the computational efficiency and the accuracy of the obtained model. Also, lack of insufficient precision to choose the target misfit can lead the inversion to produce and reach an incorrect model.     

A new rating system approach for risk analysis of rock slopes

In this paper, an approach is presented to analyze the stability risk of rock slopes based on a new rating system. Three factors are used to estimate the risk level of rock slopes: (1) failure probability, (2) element at risk rating, and (3) vulnerability rating. Element at risk and vulnerability ratings are both given a range from 0 to 10, and the probability of failure is varied between 0 and 1, so the risk rating ranges between 0 and 100. This risk rating can be used to determine both the quantitative and qualitative risk levels of slopes at the same time. The method is tested on the western sector of the slopes facing Songun copper plant phase III, Iran, to clarify its procedures and assess its validity. Deterministic kinematic analyses showed that the slope has a potential for circular failure. Risk assessments revealed that the risk levels of the slope in both static and pseudo-static conditions are “very low” and “high,” respectively.

     

Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network

The longitudinal dispersion coefficient is a key element in determining the distribution and transmission of pollution, especially when cross-sectional mixing is completed. However, the existing predictive techniques for this purpose exhibit great amounts of uncertainty. The main objective of this study is to present a more accurate model for predicting longitudinal dispersion coefficient in natural rivers and streams. Bayesian network (BN) approach was considered in the modeling procedure. Two forms of input variables including dimensional and dimensionless parameters were examined to find the best model structure. In order to increase the performance of the model, the clustering method as a preprocessing data technique was applied to categorize the data in separate groups with similar characteristics. An expansive data set consisting of 149 field measurements was used for training and testing steps of the developed models. Three performance evaluation criteria were adopted for comparison of the results of the different models. Comparison of the present results with the artificial neural network (ANN) model and also well-known existing equations showed the efficiency of the present model. The performance of dimensionless BN model 30% is more than dimensional ones in terms of the root mean square error. The accuracy criterion was increased from 70 to 83% by performing clustering analysis on the BN model. The BN-cluster model 43% is more accurate than ANN model in terms of the accuracy criterion. The results indicate that the BN-cluster model give 16% better results than the best available considered model in terms of the accuracy criterion. The developed model provides a suitable approach for predicting pollutant transport in natural rivers.     

Applying different scenarios for landslide spatial modeling using computational intelligence methods

Landslides every year impose extensive damages to human beings in various parts of the world; therefore, identifying prone areas to landslides for preventive measures is essential. The main purpose of this research is applying different scenarios for landslide susceptibility mapping by means of combination of bivariate statistical (frequency ratio) and computational intelligence methods (random forest and support vector machine) in landslide polygon and point formats. For this purpose, in the first step, a total of 294 landslide locations were determined from various sources such as aerial photographs, satellite images, and field surveys. Landslide inventory was randomly split into a testing dataset 70% (206 landslide locations) for training the different scenarios, and the remaining 30% (88 landslides locations) was used for validation purposes. To providing landslide susceptibility maps, 13 conditioning factors including altitude, slope angle, plan curvature, slope aspect, topographic wetness index, lithology, land use/land cover, distance from rivers, drainage density, distance from fault, distance from roads, convergence index, and annual rainfall are used. Tolerance and the variance inflation factor indices were used for considering multi-collinearity of conditioning factors. Results indicated that the smallest tolerance and highest variance inflation factor were 0.31 and 3.20, respectively. Subsequently, spatial relationship between classes of each landslide conditioning factor and landslides was obtained by frequency ratio (FR) model. Also, importance of the mentioned factors was obtained by random forest (RF) as a machine learning technique. The results showed that according to mean decrease accuracy, factors of altitude, aspect, drainage density, and distance from rivers had the greatest effect on the occurrence of landslide in the study area. Finally, the landslide susceptibility maps were produced by ten scenarios according to different ensembles. The receiver operating characteristics, including the area under the curve (AUC), were used to assess the accuracy of the models. Results of validation of scenarios showed that AUC was varying from 0.668 to 0.749. Also, FR and seed cell area index indicators show a high correlation between the susceptibility classes with the landslide pixels and field observations in all scenarios except scenarios 10_RF and 10_SVM. The results of this study can be used for landslides management and mitigation and development activities such as construction of settlements and infrastructure in the future.     

Evaluation of relative tectonic activity of Buin Zahra-Avaj area,northern Iran

Present active tectonics is affecting central Alborz and created various dynamic landforms in Buin Zahra-Avaj area, northern Iran. The area, located between the southern central Alborz and the edge of northwestern central Iran, is the result of both the Arabian–Eurasian convergence and clockwise rotation of the south Caspian Basin with respect to Eurasia in which most of the steep fault planes have a left lateral strike-slip component and most of the dip-slip faults are reverse, dipping SW. Since this region consists of several residential and industrial areas and includes several fault zones, the assessment of the structures of the present activity is vital. Six significant morphometric indices have been applied for this evaluation including stream length–gradient (SL), drainage basin asymmetry factor (Af), hypsometric integral (Hi), ratio of valley floor width to valley height (Vf), drainage basin shape (Bs), and mountain front sinuosity (Smf). The combined analyzed indices, represented through the relative tectonic activity (Iat), were used. The study area was divided into four regions according to the values of Iat. These classes include class 1 (very high activity,18%), class 2 (high, 20%), class 3 (moderate, 44%), and class 4 (low, 18%). The results of these indices are consistent with field observations on landforms and the deformation of Quaternary deposits.     

Prediction of local scour around complex piers using GEP and M5-Tree

Local scour around piers is one of the main causes of bridge failures. In this study, three robust techniques, artificial neural networks (ANNs), M5-Tree, and Gene Expression Programming (GEP), were employed for prediction of scour depth around complex piers. The clear water condition was chosen for all experimental tests. The results indicated that pier diameter (b _c) and foundation level (Y) are the main parameters for local scour. Furthermore, the minimum scour depth occurs in range of Y/b _c = 1.1~1.3. In next step, to evaluate the mentioned techniques, a wide range of dataset was collected from the present study and literature. The radial base function (RBF) with R ² = 0.945 and RMSE = 0.031 provides better prediction in comparison with conventional equations, M5-Tree (R ² = 0.883, RMSE = 0.292) and the GEP techniques (R ² = 0.811 and RMSE = 0.263). The equations developed by M5-Tree and GEP are more useful for practical purposes and can be easily employed to predict the depth of scour at complex piers.     

Effect of silica fume on compressive strength of oil-polluted concrete in different marine environments

In the present research, effect of silica fume as an additive and oil polluted sands as aggregates on compressive strength of concrete were investigated experimentally. The amount of oil in the designed mixtures was assumed to be constant and equal to 2% of the sand weight. Silica fume accounting for 10%, 15% and 20% of the weight is added to the designed mixture. After preparation and curing, concrete specimens were placed into the three different conditions: fresh, brackish and saltwater environments (submerged in fresh water, alternation of exposed in air & submerged in sea water and submerged in sea water). The result of compressive strength tests shows that the compressive strength of the specimens consisting of silica fume increases significantly in comparison with the control specimens in all three environments. The compressive strength of the concrete with 15% silica fume content was about 30% to 50% higher than that of control specimens in all tested environments under the condition of using polluted aggregates in the designed mixture.