فهرست مطالب فرامرز دولتی ارده جانی

Heap leaching structures and waste dumps are the most important mining structures, and paying attention to their stability issues at different stages of construction and operation is important. Investigating the thermo-hydromechanical parameters of the materials of these structures is very important to evaluate stability and geotechnical issues. Various factors affect the thermo-hydromechanical properties of materials, which can change the aforementioned properties and ultimately cause structural instability. Therefore, it is necessary to study the effect of various factors such as pH, salinity, temperature, consolidation, and precipitation (moisture) on the thermo-hydromechanical properties of materials and to investigate their behavior over time. This paper reviews some studies to investigate the effect of the mentioned factors on the thermo-hydromechanical properties of different soils. The results show that the change of some factors like pH affects the hydrodynamic properties of materials, which reduces soil strength parameters. Finally, one may conclude that by examining the hydrodynamic properties of the materials of mining structures and monitoring them over time, a favorable understanding of the behavior of the structure can be achieved, and its behavior can be predicted to some extent.

In order to design an appropriate dewatering system in an open pit mine, it is necessary to know the hydrogeological conditions and factors affecting the condition of groundwater in the mining area. One of the important factors for investigating the hydrogeological conditions of a mining site is to know the status of the catchment area, temporal and spatial changes of the piezometric levels and the range of disturbances caused by mining activities, climate conditions and the tectonic state of the mining area. The hydrological and hydrogeological conditions of Sangan iron mine are also affected by climate factors such as precipitation and tectonic factors including the number of faults and their trends, their distances and their relationship with each other. Therefore, in this study, the influence of various factors including precipitation, distance from waterways, tectonic status and faults, and the pit development during the exploitation period on the fluctuation of groundwater were investigated. By examining the fluctuations of the piezometric levels caused by these factors in the period from March 2011 to March 2014 and statistical analysis of the results, it was found that among the affecting factors, the Cn-pit expansion has the greatest effect on the groundwater level fluctuation of the western anomalies of Sangan mine. By drawing the ccorrelation diagram between the distance from Cn-Pit and the groundwater level drop of observation piezometers of the nearby areas of the Cn-pit, and obtaining a coefficient of determination equal to 0.94 (R2=0.94), it was found that the radius of the influence of the Cn-pit expansion on the changes in the groundwater level of the adjacent areas is about 700 meters. In addition, it was found that the amount of precipitation and the distance from the waterways do not have a significant effect on the groundwater level fluctuations in the study area.

Fractured network modeling is the main prerequisite for fluid flow simulation in many applications such as groundwater resource management, oil and gas reservoir simulation, geothermal energy resource modeling and etc. The aim of this study is to develop an iterative object-based algorithm for fractured network modeling that considers both statistical parameters and spatial connectivity of fractures. The presented algorithm uses the functions of fracture neighborhood matrices in image processing to make clear the fracture continuity and to determine their spatial distribution using the matrix of interconnected fracture cell detection, Sobel matrix, Prewitt matrix and Laplacian matrix. The objective function defines the differences between the features of the reference fracture network and the stochastic generated fracture network using a norm of two. To solve this objective function, optimization algorithms of Gravitational search, particle swarm and hybrid of these two metaheuristic algorithms have been used. In this paper, the metaheuristic algorithm of particle swarm optimization enjoys more validity in fracture network modelling. Therefore, among the metaheuristic algorithms, PSO algorithm regenerates the reference fracture network with the accuracy of 98.89%.

This work investigates the reactive transport of volatile hydrocarbons in the unconfined aquifer system of Tehran oil refinery and the industrial area of Ray, Tehran. A 2D finite volume model is presented to predict the soil gas contamination caused by LNAPL traveling on the phreatic surface through the vadose zone of the aquifer incorporating physical, chemical, and biological processes. A multi-purpose commercial software called PHOENICS is modified by incorporating extra codes to solve the model equations numerically. The model predictions closely agree with the field measurements, showing that the LNAPL migration is typically affected by the volatilization process. LNAPLs represent a potential long-term source of soil and groundwater contamination in the studied site. A comparison of the simulation results in a time step of 36 years with the results of field studies shows that the presented numerical model can simulate the reaction transfer of evaporated hydrocarbons in the unsaturated region. The concentrations have decreased in the time step of 36 years compared to the values ​​shown in the time step of 50 years. This decrease in the hydrocarbon gas-phase concentrations in the unsaturated zone is due to excavations at the site for field studies. Through these excavations, a significant volume of the gaseous phase trapped below the earth's surface is released into the atmosphere, which reduces the accumulation of volatile gases beneath the earth's surface.

Considering that mining has many environmental impacts from the exploration phase to production and finally closure, it is necessary to plan the activities so that the concept of green mining is realized in its true meaning. This means that mining is carried out in order to obtain the minerals that are used in various industries; however, by taking appropriate measures, the impacts of mining on the environment are reduced to a minimum level. Since there is little information about the environmental, ecological, hydrological, and hydrogeological status in most mining areas, a comprehensive study of the area's water, soil, plants, and animal species should be conducted. The existence of permanent and seasonal rivers in the vicinity of some mines, in some cases being located in protected areas of the Iranian Department of Environment, and the presence of vegetation near some mines are among the matters that cause many environmental challenges in the mining areas. For this purpose, a series of comprehensive studies are critical in the pre-mining, during mining, and closure phases of the mine life. In addition, detailed studies should be done on factories such as smelters located in the mining areas. Life cycle assessment (LCA) is widely used in order to determine the environmental status of these factories. Furthermore, the issue of process water and water recycling, as well as waste management, should be considered. Nowadays, the environmental monitoring technology is one of the widely used tools in many mines in the world. Moreover, these mining companies' green space management system should be given special attention according to the obligatory standards of the Iranian Department of Environment. In this paper, a conceptual framework for the green mining method will be introduced for the coal mines to consider the economic and social aspects, and we pay a special attention to the health, safety, and environmental requirements.

In this paper, we investigate a probabilistic approach in order to predict how acid mine drainage is generated within coal waste particles in NE Iran. For this, a database is built based on the previous studies that have investigated the pyrite oxidation process within the oldest abandoned pile during the last decade. According to the available data, the remaining pyrite fraction is considered as the output data, while the depth of the waste, concentration of bicarbonate, and oxygen fraction are the input parameters. Then the best probability distribution functions are determined on each one of the input parameters based on a Monte Carlo simulation. Also the best relationships between the input data and the output data are presented regarding the statistical regression analyses. Afterward, the best probability distribution functions of the input parameters are inserted into the linear statistical relationships to find the probability distribution function of the output data. The results obtained reveal that the values of the remaining pyrite fraction are between 0.764% and 1.811% at a probability level of 90%. Moreover, the sensitivity analysis carried out by applying the tornado diagram shows that the pile depth has, by far, the most critical factors affecting the pyrite remaining

Cross-gradient based joint inversion method is one of the very useful methods for reduction of non-uniqueness and elimination of lacks of reconstructed models using separate inversion. This method have also some deficiencies against its advantages. It needs too much time when the dimension of problem is large and sometimes it's not runnable. In this paper data space method, as a fast procedure for inversion of some matrices in inversion process, was represented and cross-gradient based joint inversion algorithm supported by this method was developed in MATLAB software. To investigate the capability of code, a synthetic model include three cubes with various dimensions and depth extend was used. At first the calculated gravity and magnetic anomalies of this synthetic model were separately inverted using data space inversion method. Then these synthetic gravity and magnetic data were jointly inverted using the improved 3D inversion algorithm along with smoothness constraint. The obtained results showed significant improvements in the output model with the same conditions of separate data inversion. These improvements were in delineation of location and depth estimating aspects and also capability of code performance to recover such number of model’s parameters. After validating of results, the proposed routine was applied for 3D joint inversion of the gravity and magnetic data of one Hematite area around Jalalabad iron mine in Zarand city (Kerman province). Comparison of the inversion results with those obtained by drilling, validates correctness and ability of the presented algorithm even when we encounters with deficiency of data.

This work presents a quantitative predicting likely acid mine drainage (AMD) generation process throughout tailing particles resulting from the Sarcheshmeh copper mine in the south of Iran. Indeed, four predictive relationships for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate concentration, and pH have been suggested by applying the gene expression programming (GEP) algorithms. For this, after gathering an appropriate database, some of the most significant parameters such as the tailing particle depths, initial remaining pyrite and chalcopyrite fractions, and concentrations of bicarbonate, nitrite, nitrate, and chloride are considered as the input data. Then 30% of the data is chosen as the training data randomly, while the validation data is included in 70% of the dataset. Subsequently, the relationships are proposed using GEP. The high values of correlation coefficients (0.92, 0.91, 0.86, and 0.89) as well as the low values of RMS errors (0.140, 0.014, 150.301, and 0.543) for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate concentration, and pH prove that these relationships can be successfully validated. The results obtained also reveal that GEP can be applied as a new-fangled method in order to predict the AMD generation process.

The main problem of traditional methods of environmental impact assessment is that in most of the existing algorithms and methods, such as Leopold matrix, Folchi method and RIAM matrix, the main attention is to the destructive effects of the proposed plan and the advantages of the plan are less noticeable. Data envelopment analysis (DEA) is a new approach to assessing the industrial units also considers the positive economic and social impacts of the project and provides a comprehensive assessment of the industrial unit. In the present study, the Alborz Sharghi Coal washing plant in northern Iran has been considered as a case study, and 19 plant activities and 11 environmental components have been used to evaluate the effects of the plant. To solve the problem, two commonly used DEA approaches, called BCC and CCR, have been used.  

The problem that always has been challenging is that the focus of the various EIA methods is on the environment. Although the economic and social issues are not considered. This approach makes this idea to the stakeholders that the environmental impact assessments are the obstacle in their way. however, in recent years, the concepts such as sustainable development and corporate social responsibility has convinced the industrial units that the simultaneous attention to economic issues, indigenous communities and the environment around the factory, can increase the economic benefits of that industrial unit in the long time. So, introducing a new approach to assessing the environmental impact of the units is essential. This approach can systematically conduct the EIA process and simultaneously addresses the economic and social issues of the project.  

In the present study, the authors trying to use the Data Envelopment Analysis (DEA) method as an environmental impact assessment (EIA) against traditional methods such as Folchi and RIAM.  The case study is East Alborz Coal Washing Plant in the northern Iran. In this case, 19 impacting factors (IF) and 11 environmental components (EC) were considered. Also, two most popular approaches of the DEA, called CCR and BCC, have been compared and the output-oriented BCC method has been introduced as a new way for the environmental impact assessment with the sustainable development approach.  

The DEA method can reduce the uncertainty of the results and also make the final results more reliable due to there is no need to determine the weights of the impacting factors. Assessing the environmental impact of the plant by BCC and CCR approach in two modes of minimizing inputs and maximizing outputs showed that "ecology" and "landscape" components are two environmental components that are the least efficient and should be seriously considered. Moreover, it can be said that the EIA of the coal washing plant with the output-oriented BCC approach is closer to the concepts of sustainable development. The use of "Potential Improvement" method as one of the results of the DEA analysis, helps to examine the status of the impacting factors and can be used in the selection of more efficient development plans. The results of "Total Potential Improvement" showed that the BCC model (maximize-outputs) has a more rigorous evaluation of impacting factors and two factors of "input feed dump" and "water consumption" to achieve an efficient state should be reduced about 13%.

Mining operations and waste resulting from it can be considered as one of the most important sources of hazardous elements in the environment. Identification of the spatial distribution of toxic elements in the mine waste dump systems requires the assessment of environmental hazards and strategies. To achieve the goal, the sampling was conducted on a waste dump located in the east of Angouran mine at an area of 300,000 square meters. For this purpose, 38 soil samples were selected for study and modeling. Statistical studies of the data set for As, Cd, Cu and Co elements were performed. In addition, Q-Q plot was used to remove outlier from the data, which resulted in an increase in the correlation coefficient of the results. In the next step, statistical analyses were performed to determine variogram parameters for the studied elements and the distribution of toxic elements was modeled on the basis of sequential Gaussian simulation (SGS) method, which results in high concentrations As in the south, west, and north of the dump. Elevated concentration of Cd can be seen in the southwest and northeast parts of the dump. Also, high concentrations of Co and Cu accumulate in the western, southern and northern parts of the dump. In order to validate variograms, cross-validation method was employed which is a fundamental method for comparing the effect of different variogram models and simulation methods on interpolation results. Correlation coefficients for As, Cd, Cu and Co elements were 0.885, 0.8056, 0.6867 and 0.9792, which represent the validity of simulation results.

Science dos not have a clime about the moral issues or solving the social problems. Hence, we need an interdisciplinary branch to be familiar with the complexities of the human sciences while knowledge of scientific or engineering issues. Applied ethics and professional ethics have such a role, and in the field of Geoscience, a new branch called "Geo-ethics" plays this role. Geo-Ethics is one of the emerging branches of professional ethics. In professional ethics, the responsibilities and ethical priorities of members and business owners are emphasized. It also explains how to deal with difficult issues such as moral complexities in that branch. Geo-ethics is a common ground between philosophy, earth sciences, economics and sociology. In this research, we attempt to introduce the basic concepts of Geo-ethics and its origins and history. International professional ethics associations have also been introduced as global references. One of the fundamental issues of Geo-ethics is its relevance to society and social responsibility and also the issue of climate change and ecosystem of life. Dealing with ethical dilemas and related issues of scientific research are the other sides of Geo-Ethics. Also, promoting the moral level of young researchers and encouraging them to think critically about Geo-resource management and trying to find socio-economic solutions within the framework of sustainability for future generations is the other key issues in Geo-ethics.

Copper smelting and refinery factories are the final stages of a pyrometallurgical processing chain, and they cause many environmental challenges around the world. One of the most common environmental problems of these factories is toxic emissions. These toxic gases have harmful effects on the vegetation, animal species, soils, and water resources around the factories. Phytoremediation can play an important role in the reduction of the adverse effects of environmental pollutions arising from copper smelting and refinery factories. In this paper, we first discuss different types of pollutions caused by copper metallurgical factories, and present the main research approaches and studies conducted on these factories. In the second part, we provide a summary and comparison of different remediation technologies used to reduce the environmental pollutions of these factories. Besides, the advantages and disadvantages of each method is also investigated. In the third part, we review the different aspects of the phytoremediation including the effective mechanisms, different types of plants, application environments, and the effective factors. The next part includes the selection of suitable plants for the phytoremediation process applied for copper metallurgical factories and investigation of the native and cultivated hyperaccumulator plants. In addition, different efficiency indices are introduced for evaluating the phytoremediation efficiency and selecting an appropriate hyperaccumulator plant. At the final stage, some appropriate plant species for various types of phytoremediation are introduced. The effects of different environmental stresses and the possibilities of integrating phytoremediation with other remediation technologies as well as the advantages and disadvantages of phytoremediation are eventually investigated.

Newly minerals could be formed as a result of oxidation, hydrolysis, precipitation and dehydration processes in acid mine drainage (AMD) environment. The occurrence of secondary minerals within the dump No. 7 from Miduk copper mine was studied using mineralogical approaches including X-ray diffraction (XRD), Scanning Electron Microscope (SEM-EDS) and Raman Spectroscopy (RS). Geochemical invetigations, including saturation index and speciation, were conducted on pore water of the dump which simulated from paste pH test. Acidic pH value (ranges from 1.47 to 4.23), a high concentration of SO42- (ranges from 3.95 to 286 g/L) and a high level of Fe (varies from 120.9 to 70860 mg/L) of the waste leached indicate that sulphide oxidation especially pyrite occurs within the dump. Complex FeSO4+ was dominant species of ferric iron in the leached solution phase. According to mineralogical and geochemical studies, newly formed minerals including coquimbite, ferricopiapite, plumbojarosite, rozenite, alunogen and brochantite were identified within the dump. Retention of As, Pb and Cu by iron sulphate, especially coquimbite, were confirmed in the dump. The results of the present study could be employed to develop a comperehensive environmental management program at the mining sites.

The importance and necessity of using underground spaces in the present era is not overlooked. Caverns are used as an important bunch of underground spaces for the storage of radioactive waste, hydrocarbons, and hydroelectric power plant projects. Construction of caverns has particular complexity and water seepage into the caverns is one of the most important problems. The purpose of the present study was to investigate the 3D modeling of the fracture network of a cavern for the pumped storage power plant of Rudbar Lorestan dam by using DFN method and 3DEC software and then determining the tensor of hydraulic conductivity by using the model. To achieve the goal, statistical analyses were performed on the in situ data extracted from the area. Power law distribution was determined as a suitable statistical distribution proportional to the data. Then, the DFN model was created and validated using the Watson-Williams test. In the following, a block model of DFN made from the fractures was constructed to determine the Representative Elementary Volume for different dimensions from 1 to 12 m. It was found that a 7-meter model was appropriate. Furthermore, the tensor of hydraulic conductivity was determined by using a hydraulic head on a generated REV.

More than 21 Mt of coal wastes is being produced by Anjir Tangeh coal washing plant during 22 years. Wastes have been deposited in the area without considering of environmental regulations in such an unspoiled area. Moreover, the pervious researches revealed that the pile has a potential of acid mine drainage generation. Therefore, it is essential to present a model for investigating risk of acid mine drainage generation in this pile. This paper presents a probabilistic method to prediction of acid mine drainage is being generated within Anjir Tangeh coal wastes pile. After building a database, pyrite remaining fraction and pH were considered as output data while depth of the wastes, bicarbonate concentration and oxygen fraction were input parameters. Then, the best probability distribution functions were presented on each of input parameters applying Monte Carlo simulation. Also, the best linear statistical relationships between input data and output data were presented. Afterward, the best probability distribution functions of input parameters were inserted to the relationships to find the probability distribution functions of output data. Eventually, the results revealed that the remaining pyrite fraction and pH values were lower than 0.894 and 4.6 at a 5% probability level, respectively.

Mining and mineral industries are one of the main sources of environmental concerns for human societies. The process of mining and mineral processing of coal due to its nature and mechanism, has a high potential of creating different types of environmental pollutions. In this study, the Alborz Sharghi coal washing plant in Iran and its effects on the environment have been investigated using different environmental impact assessment methods (EIA). Initially, a list of plant activities and the environmental components have been prepared and after that three EIA methods include modified Folchi, AHP and RIAM matrix, have been used. Average results of expert suggestions show that factory waste water, toxic gases ans leaching from tailings with the score 8,7.9 and 7.9 respectively are the most hurmful activities of the company. The variety of the results of different EIA methods has always been one of the most significant challenges of the Environmental impact assessment. In current study, the results of various EIA methods have been coupled with each other by using some integration strategies like Borda, Copeland, Kemeny and Kohler. A novel strategy called DRS (Direct Ranking Strategy) have been developed in current research. The use of coupled environmental impact assessment (C-EIA) method to aggregate various EIA methods showed that the four components of "Groundwater", "Landscape", "Soil of the area" and "Ecology" are respectively the most critical environmental components of the Alborz sharghi coal washing plant.

Heavy metal pollution is one of the main environmental problems associated with mineral, industrial and agricultural activities in the world. Due to the long life of these toxic metals and the degree of solubility in acidic and even non acidic conditions, they can have destructive effects on water, soil and humans life in the long time. The lead-zinc mine of Anguran is one of the largest world-class sulfide-carbonate reserves, which, due to mining, has produced a significant amount of mineral wastes, which can be the source of heavy metals to the water and soil of the downstream areas. Therefore, the use of quick and cost-effective methods to classify the risk of contamination of this type of waste can be a useful tool for monitoring, recovery and reconstruction programs in the future. The purpose of this research is to use multivariate statistical techniques, such as discriminant analysis (DA) method and utilize of artificial intelligence technique in order to classify and predict the potential of pollution in mining wastes. To achieve the goals, the samples taken from different surficial parts of the waste dump were analyzed by using the ICP-MS method to determine the concentration of heavy metals. These metals includes: Al, As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb, Sb and Zn. The environmental risk assessment indices (ERAI) and potential load index (PLI) were then modelled. At the end, by using the DA and neural network (NN) methods, potential risk of contamination on the damp surface was classified in three low, medium and high levels with the accuracy of 91.49 and 93.6 percent respectively. The results also showed that these techniques can be used as effective tools for classifying new waste dumps and designing of new constructing dumps based on their contamination level.

One of the most important issues about the underground spaces is to calculate the amount of water seepage into them. Realistic estimation of seepage is a very influential factor in the development of an operational project. The main cause of the seepage is hydraulic conductivity. One of the most important ways of estimating the amount of water seepage into the underground spaces is the Lugeon and Lofran field tests. These tests are performed in rocky and soil environments, respectively. In this research, the effective factors on the amount of hydraulic conductivity changes have been investigated by using a discrete fracture network (DFN). Finally, a model has been developed to help determine the hydraulic conductivity of the fractured rock mass. Since work in a discrete environment software is complex in terms of software, it is very important to obtain a simple model. The number of fractures and dimensions of the block model are two important factors in the speed of modeling. Finally, the filter was intended to remove fractures smaller than 10 meters. Also, the dimension of the block model is at least 7 meters (with these dimensions, the answers independent of the size of the fracture block). Finally, the answers were verified with the lugeon test. The result is a high adaptation between experimental data and calculated data.

Possibility of mapping the distribution of Arsenic and Chromium in a mining area was investigated using combination of (VNIR) reflectance spectroscopy and geostatistical analysis. Fifty five soil samples were gathered from a waste dump at Sarcheshmeh copper mine and VNIR reflectance spectra were measured in a laboratory. Savitzky- Golay first derivative was used as the main pre-processing method before developing Genetic Algorithm Partial Least Squares Regression (GA-PLSR) and PLSR models for predicting toxic elements concentrations. Physicochemical mechanism that allows the prediction with reflectance spectroscopy was also investigated and it was found that, elements sorption by spectrally active Fe and clay contents of soil was the major mechanism helping the prediction of spectrally featureless As and Cr. Positive relationships were observed between performance of predicting models and iron and clay contents of the samples. Comparing to PLSR, higher performances for predicting both toxic elements concentrations were obtained by applying GA-PLSR model. Furthermore, similar spatial patterns for soil pollution hotspots were observed by geostatistical interpolation (kriging) of chemically measured and models’ predicted values. Results demonstrated that the amount and spatial variability of arsenic and chromium can be determined using VNIR spectroscopy and geostatistics in Sarcheshmeh mine’s waste dump.

Acid mine drainage (AMD) is one of the most environmental problems which is caused by mining activities. It may be generated from oxidation of sulfide minerals (specifically pyrite) in mining wastes, ore and mineral processing. As a result, prediction and measurement of pyrite can play a key role in investigation of AMD process. In this paper, a relationship based on multivariate regression is suggested for predicting of remained pyrite fraction within a coal waste pile located nearby Alborz- Sharghi coal washing plant.
For this purpose, the model uses the most important factors (independent variables) including depth, mole fraction of oxygen, local temperature and cumulative annual precipitation (from the initial deposition of the wastes within the pile) as input parameters and returns the remaining pyrite fraction (dependent variable) in the related depth of the pile as its output.
Findings: The results of the laboratory experiments reveal that the fraction of pyrite remaining gradually increased at the lower depth where the oxygen diffuses from the surface to 2 m of the pile. A comprehensive statistical analysis was conducted after obtaining and collecting of the required data which caused all the possible relationships among the target and independent variables to be suggested and presented. Then, the best model (relationship) according to Efroymson’s the step wise regression method was presented to predict of the remained pyrite fraction within the pile.
Discussion and The relatively high confidence level of the suggested model (87%) reveals its appropriate reliability. A strong correlation (R2=90%) between validation data and suggested model also confirms the desirable reliability of the model. The proposed model is recommended to be applied in similar coal waste piles and will economize time and cost in investigation of AMD process on the environmental management issue of mining wastes.

In this research, a combined hybrid artificial neural networks and genetic algorithms method was used to invert a four-layer sounding model including eight different curve types. For this purpose, 2000 sounding data was synthetically generated using Resix-IP software. Then, different types of curves were classified applying a feed forward artificial neural network with back propagation algorithm based on a trial and error process of training data. The network of classification included 22 neurons in input layer, 33 neurons in hidden layer and 8 neurons in output layer. Subsequently, the inversion of four-layer model of geoelectrical sounding data was conducted by genetic algorithms. The obtained results of resistivity values showed a very good agreement between the outputs of genetic algorithms and test data. For instance, the strong correlation can be reflected by coefficients (0.99, 0.82, 0.83 and 0.97) and (0.99, 0.92, 0.93 and 0.97) of resistivity values for layers No.1 to No.4 in turn in curve types AA and AK. Besides, in all of the curves, thickness of the first layer was appropriately estimated by genetic algorithms method, while in these two curve types, the correlation coefficients in the second layer (0.81 and 0.88) and the third layer (0.79 and 0.71) show a relative capability of this method.

The Curie point (approximately 580C for magnetite at atmosphere pressure) is the temperature at which spontaneous magnetization vanishes and magnetic minerals exhibit paramagnetic susceptibility. The depth at which temperature reaches the Curie point, is assumed to be the bottom of the magnetized bodies in the crust. Curie point temperature varies from region to region depending on the geology of the region and mineralogical content of the rocks. Therefore, one can normally expect shallow Curie Point Depths (CPD) at regions which have geothermal potential, young volcanism and thinned crust (Aydin and Oksum, 2010). The assessment of the variations of the Curie depth of an area can provide valuable information about the regional temperature distribution at depth and the concentration of subsurface geothermal energy (Tselentis, 1991).    There are two basic methods which have been used in the examination of the spectral properties of magnetic data to estimate the magnetic basement depth. The first is the method of Spector and Grant (1970) and the second is the method of Bhattacharyya and Leu (1975, 1977). Spector and Grant (1970) showed that the expectation value of the spectrum of an ensemble model was the same as the average depth to the top of an ensemble of the magnetized rectangular prism. The second method estimates the depth to the centroid of the body by using the interpretation of a single anomaly. This method is useful when no spectral peaks occur on the amplitude spectra (Li et al., 2010). Following this, a joint method was developed by Okubo et al. (1985) who combined and expanded the ideas of the methods to the purposes of geothermal exploration.    CPD estimation (Zb) is obtained in two steps as suggested by Okubo et al. (1985). First, the centroid depth (Z0) of the deepest magnetic source is estimated from the slope of the longest wavelength part of the spectrum divided by the wavenumber, where P(k) is the power density spectrum, k is the wavenumber, and A is a constant. Second, the average depth to the top boundary (Zt) of that distribution is estimated from the slope of the second longest wavelength spectral segment (Okubo et al., 1985), where B is a constant. The equation Zb = 2Z0 - Zt(Okubo et al., 1985) is used to estimate the depth to the bottom (Zb) of the inferred CPD from the centroid (Z0) and the top depth (Zt) estimated from the magnetic source for each sub-region.    The study area was divided into eleven overlapped blocks for the purpose of spectral analysis. Center of blocks are shown on the residual field map (Figure 3). Each block covers a square area of 140 km by 140 km. Power spectral analysis was conducted on the RTP values of each of the blocks by plotting the logarithm of spectral energies against the wavenumber. The CPD estimation procedure as suggested by Okubo et al. (1985) was carried out for the eleven blocks to obtain the Curie point depth for each block. Figure 5 shows Curie depth values and thermal springs of the study area. In this figure, Curie depth varies between 9.42 and 18.92 km. For the blocks of 3, 6 and 7, Curie point depth is significantly less than the other blocks, which could be due to the presence of high temperature hot springs in this area. According to the geological information and Curie point depth values, A, B, C and D area are recommended for more geothermal investigation.

Magnetotelluric (MT) is a geophysical exploration method that utilizes simultaneous measurements of naturally occurring magnetic and electric fields. As this method utilizes natural electromagnetic (EM) signals with a wide frequency range, its exploration depth is from several meters to several kilometers. Depending on its frequency, it is used for petroleum, groundwater, geothermal, mineral, and geotechnical explorations. To determine electrical variation of a subsurface structure using the MT survey, five components of electric and magnetic field variation are measured on the earth’s surface in each measuring site. The two components of the horizontal electric field (Ex and Ey) and also two horizontal components of the magnetic field (Hx and Hy) are normally measured in the north-south (x) and east-west (y) directions. An extra measurement of the vertical component of the magnetic field (Hz) is sometime measured in each measuring site. The relationship between the electric and magnetic fields at the earth’s surface can be written as i ij j E  Z H where, i, j  x, y and ij Z is the complex impedance tensor of order 2 2. When the resistivity of the earth is a function of depth (i.e. in a one-dimensional earth), the diagonal elements of impedance tensor (Z) are equal to zero and its off-diagonal elements are equal in amplitude but opposite in signs. In two dimensional (2- D) structures where resistivity is invariant in the strike direction, diagonal terms become zero if the EM fields are defined in a coordinate system normal to the strike of the structure. In such cases, the impedance component of the electric field which is parallel to strike (i.e. transverse electric (TE) mode) would be different from those components of the electric field perpendicular to the strike (i.e. the transverse magnetic (TM) mode). In the case of 2-D structures, if the impedance is measured in an arbitrary orientation, the angle required to rotate the measurements into TE and TM modes can be determined from the impedance tensor. In the case of the three dimensional (3-D) earth, the entire components of Z would be non-zero. Presently, most of the MT survey is performed as electrical sounding and the measured data is then modeled and interpreted to sense the details of the subsurface structure. To provide a reasonable and physically meaningful model of the subsurface structure, its dimension must be determined somehow. To determine the dimensionality of the subsurface structure using MT data, several parameters such as conventional skew, ellipticity and polar diagrams of the impedance tensor elements are used in practice. As these parameters are very sensitive to the noise of data, the phase sensitive skew and some dimensionality indices were defined. In this study, it was attempted to use various parameters such as conventional skew, ellipticity and phase sensitive skew along with dimensionality weighting indices (D1, D2, D3) to determine the structural dimension of the Sabalan geothermal field in the NW of Iran using MT data. It was also attempted to model the MT data of several sites along a profile located in Moil valley in the NW of Sabalan in order to determine any possible location of the geothermal reservoirs. The obtained results indicated that the structure up to the medium depth was 1-D and the deepest structure was 2-D. As the subsurface structure of the area was 1-D at periods lower than 1 second, the averaged data of both TE and TM modes were first inverted one dimensionally using the WinGlink software to explore and delineate the locations of any geothermal reservoirs likely to be present in the study area. The inversion results illustrated a layered structure located from the ground surface to the depth level of 1500 meters above the sea level (m.a.s.l) which in turn confirmed the shallow depth structures were 1-D. The results also showed a highly conductive layer, with resistivity lower than 40 Ω.m located beneath the MT stations of 24 to 244. The results of drilling revealed that this conductive zone could be interpreted as a clay cap over the geothermal reservoir which elongates to an approximate depth of 1000 meter. This clay zone overlays a more resistive zone, with resistivity values from 40 to 100 Ω.m, which in turn can be interpreted as a geothermal reservoir. The data from a 2-D joint inversion of the TE and TM modes confirmed the results of the 1-D inversion of the MT data for shallow to intermediate depths. It further delineated that the location of the geothermal reservoir was at a depth zone of 500-1500 m.a.s.l. under the sounding location of 7-245 in the south to south eastern part of the study area.