سعید سلطانی محمدی

Chromite is one of the most used strategic metals. Among different types of Chromite deposits, Padiform Chromite is more considerable economically. On other hand, decision-making for mineral exploration investigation requires accurate estimation of the quality and quantity of new deposits. For estimation of the quality and quantity of deposits, quantitative mineral resource assessments are required. Quantitative mineral resource assessment is based on descriptive, density, and average grade-tonnage models. In this research, a three-part assessment method has been used to estimate podiform chromite deposits in Naeen’s Ophiolites. The three-Part assessment method is based on an inverse relationship between the permissive area for a specific deposit type and the number of deposits. Evaluation by the three-part assessment method shows that the understudy region is high potential. Results show a 50% chance of at least 19 deposits with at least 69400 metric tons of chromite & 10% chance of at least 40 deposits with at least 46700 tons of chromite existing in the understudy region. Geochemical anomalies have been used for validation. These anolmalies have been separated based on the concentration-area (C-A) and singularity index methods. At least three geochemical anomalies in the east margin of Naeen ophiolite were identified.

The background of 3D modeling of fluid inclusion data goes back to use of inverse distance weighting (IDW) method in the Caixiashan Pb and Zn deposit (Sun et al., 2011). This method in spite of having some advantages such as simplicity in basis is associated with disadvantages such as uncertainty in selection of weighting function and ignoring data distribution. Today, new methods have been proposed for estimation including the support vector machine method (Dutta et al., 2010). One of this method’s capabilities is in dealing with small data sets (Dutta, 2006; Zhang et al., 1998). In this study, fluid inclusion thermodynamic parameters have been estimated using support vector regression method. Predictive model of mineralization has been provided acording to 3D models resulted for fluid inclusion data and also assumption of proper thermodynamic conditions for chalcopyrite deposition in the Sungun porphyry copper deposit.

In this study, a total of 173 data sets of fluid inclusions were obtained from 59 locations. This dataset using genetic algorithm method divided into training and testing sets (80% and 20%, respectively). Modeling of fluid inclusion thermodynamic parameters has been done by support vector regression method. The SVR is based on the statistical learning theory and the structural risk minimization.

After preparing and determination of training and test datasets, radial basis kernel function (RBF) was selected in order to estimate and model the fluid inclusion thermodynamic parameters using the support vector regression method. Better functionality was the main reason of using this kernel. In the next step, parameters were needed to be carefully determined to obtain a model with high generalization ability. In this regard, the grid search method with cross validation was used to determine optimal values for the model parameters. Model was then trained using the training dataset and finally evaluated on the test dataset. Then fluid inclusion thermodynamic parameters for each block of deposit were estimated using support vector regression method. According to mineralogical and fluid inclusion studies in the Sungun porphyry copper deposit, it has been determined that chalcopyrite deposition is related to fluids with moderate to high salinity and temperatures of 300-400 °C. The predictive model was prepared based on these conditions and estimated thermodynamic Parameters in block model. In this model, each arbitrary block has been labeled on a scale of 1 to 4 (based on the favorable conditions for chalcopyrite deposition). These labels are possibility index for copper deposition. According to possibility index, proper zones have been determined in 3D model. In order to performance evaluation of support vector regression method, the predictive model was compared with 3D model of copper grade. The results of this comparison showed that prepared predictive 3D model has high consistent with copper grade block model.

In this study, 3D modeling of fluid inclusion data was performed to estimate the thermodynamic parameters affecting mineralization (homogenization and eutectic temperatures and salinity) using support vector regression method to determine potential mineralization points in the area. Using the 3D models, we found the homogenization and eutectic temperatures and fluids salinity (in different ranges of these factors) in the Sungun porphyry copper deposit. To evaluate the 3D modeling efficiency in advancing the exploration process of the porphyry deposits, the conformity between mineralization and thermodynamic variations of the fluid inclusions was investigated and, based on it; a tool called “Predictive Model” was presented for the evaluation of the occurrence of mineralization in different parts of the region. A comparison of the SVR-based predictive model and the copper grade block model shows acceptable conformity in low, medium, and high-grade regions.

Geostatistical modeling, in recent years, has been widely and increasingly used in analyzing underground structures. Using the combination of uncertainty reduction techniques and modeling processes could increase the identification accuracy of underground structure models. In this study, Indicator Kriging (IK) method is employed to model four geological units comprising leached, oxidized, supergene and hypogene zones. The aforementioned units are all located within Miduk Copper Deposit. To evaluate the uncertainty index based on the modelled geological structure, Bernoulli variance is employed. In order to reduce the uncertainty amount of the executed geological model, post-processing procedure is performed using resampling of the interpolated model. This procedure is reiterated as many times as possible until a geological model with an acceptable uncertainty amount is acquired. At the final step, blast holes data is used in order for the validation of the results. The yielded results indicate that the amount of uncertainty dropped from 5.87% to 2.98% after implementing the post-processing procedure.

Summary In this research, the application of localized uniform conditioning (LUC) method for SMU classification into waste and ore based on 0.2% cutoff grade has been compared with the conventional estimation method in the Miduk copper mine. Based on the blast holes, for the two extraction panels. Finally, even assuming the same level of cost due to under-estimation and over-estimation, the LUC method is more desirable than Log-kriging.   Introduction In the cases of the borehole data have large grid compared with the dimensions of the blocks, using the estimation techniques based on linear regression for modeling small blocks are. To solve this problem, Abzalov in 2006 introduced a new method of LUC which was result of a series of corrections on uniform conditioning (UC). In this method, after calculating the grade distribution functions for large panels, first, based on the uniform conditioning method, the large panel is divided into small blocks according to the increase of grade; small blocks located on the panels are ranked. This method is able to present the grade distribution functions for large panels based on UC method, and also can localized results of UC models.   Methodology and Approaches In this research, we used Log-kriging and LUC techniques in order to classify waste and ore materials. The dimensions of the SMU units were chosen as 5×5×15 meters. Then, using Log-kriging and LUC methods the grade of each SMU blocks in the block model was estimated. Then, according to the 0.2% cutoff grade, the SMU was divided into waste and ore. The best way to compare these two methods is to compare them with the output of the ore control unit. According to the short distance between blast holes, the separation of the ore control unit was considered as real data. Based on this data obtained from the ore control unit, a block model was prepared and separation of waste and ore blocks was done. Then, the results of the separation based on the LUC, Log-kriging and blast hole model output were compared.   Results and Conclusions Based on the blast holes, for the two extraction panels, 2450 and 2465, their results showed that the total blocks were separated to 4% waste and 96% of the ore; therefore, the ore percentage changes were considered for the conclusion. For the method of Log-kriging, in these two panels, 49% of the separation of the ore is matched with the ore control output, while in the 51% of the cases the ore was classified as waste. Also, according to the outputs of the LUC method, in these two benches, 98% of the classification of the ore was matched to the ore control output, and 2% of the separation of the ore was classified as waste. Therefore, even assuming the same level of cost due to under estimation and over estimation, the LUC method is more desirable.

Due to the relation of mineralization zones with grade variability in porphyry copper deposits, the preparation of the three-dimensional model of these zones is one of the pre-estimation steps in evaluation this type of deposits. The quality of this model has a significant impact on the quality of the grade estimates, the proper design of long-term extraction and ultimately reducing the problems between the mine and the processing plant. The usual way to prepare this model is to use a constrained modeling technique, which is a complex and time consuming process. One of the possible solutions for the preparation of these models is the use of unconstrained methods, such as intelligent methods. This paper attempts to study the performance of artificial neural network and support vector machine in the separation of mineralization zones (including leached, hypogene and supergene zones) in Miduk copper deposit. The northing co-ordinate, easting co-ordinate and height of the samples are used as input variables, and the observed mineralization zones in them are used as the output variable. Investigating the results of these intelligent algorithms in the separation of geological zones shows that the support vector machine classifier has a better performance than the artificial neural network. The better performance of the support vector machine method is shown by 1) the higher accuracy of this method in the training and testing stages and 2) the comparison between the block model with the grade control observations.

In recent years, economic geology studies have become very popular method in mineral exploration studies. Modeling fluid inclusion data is one of the common studies in economic geology. In this research artificial neural networks method, as one of the machine learning algorithms, is used for three-dimensional modeling and application of the results of fluid inclusion analysis in Sungun porphyry copper deposit. For this purpose, fluid inclusion data is used for directly separation of related alteration zones with mineralization (Potassic, Phyllic and Potassic- Phyllic). Due to the relation that exists between alteration zones and mineralization areas, based on 173 fluid inclusion data the separation of alteration zones is modeled by artificial neural networks method in Sungun porphyry copper deposit. According to the validation studies, it can be concluded that precision of this model is appropriate (83%) and trained model could be used for separation of alteration zones in Sungun porphyry copper deposit.

The main objective of this paper is to estimate water seepage from the Azad dam foundation, based on the combined geotechnical investigations and multivariate geostatistical methods. Geological filed investigations and records at the 38 exploratory boreholes have been considered as the main source for seepage calculations. Due to expansion of reservoir and a few boreholes and limitation of geological investigations, distribution of permeability and permeability changes in the reservoir area is not an indicator for reservoir. In this research using geostatistical method (Kriging), Lugeon values have been estimated for abutments and foundation of the reservoir. Afterward, seepage was estimated for reservoir by using numerical method (FEM).results show that around 450 lit/min water was seepage from the dam. That is very close to the observed seepage from dam foundation. Based on the results, the total seepage estimated is around 450 lit/min that is relatively closed to the observed and measurements flow (350lit/min).

This study uses multiple indicator kriging estimator based on assayed data acquired from 48 exploration boreholes in Tabas coal mine in order to predict gas content of coal seams. Results of the estimated block models showed that approximately 12% of the total area has the gas content of less than 5 (m3/ton) (Low risk), 11% has 5-10 (m3/ton) (Medium Risk) and 15% has 10- 15 (m3/ton) (High Risk), while about 62% of total area has the gas content of more than 15(m3/ton) which is of a high risk. Therefore, according to the experiences from mined panels, in the zones with lower to median gasification risk, it is possible to keep the mining operation running with management and optimization of the ventilation system. However, in the zones with higher gasification rate, demethanization process is mandatory.

Providing of semi-autogenous (SAG) mill models for prediction of its effectiveness is one of the most useful tools for better design of grinding circuit. Many SAG mill models have been presented in the literature, but in most of them have not been predicted the mill performance in industrial scale. Semi-autogenous mill power has an effective impact on the mill performance. So in this study, a new model based on combination of radial artificial neural network and principal component is presented to predict semi-autogenous mill power. The feed moisture, mass flowrate, mill load cell weight, SAG mill solid percent, inlet and outlet water to the SAG mill and work index selected as input variables and evaluated the effect of them on the mill power. The results showed that the trained hybrid model of artificial neural network and principal component with R=0.8512 and RMSE= 65.7115 can be used to predict the semi-autogenous mill power in industrial scale. The sensitivity analysis results showed that all model input parameters had a significant effect on the output.

During recent years, investment in mineral resources exploration projects has been significantly growth in Iran. Since the implementation and completion of exploration projects require effort, long time and high cost as well as project managers technical capability, management techniques such as work breakdown structure in mineral exploration can prevent of wasting time and costs on these exploration. Skarn deposits exploration projects, as an important source of iron, copper, gold, lead and zinc, include major exploration projects. Skarn deposits are one of the most widespread types of deposits in the earth's crust and has been the subject of many studies over the centuries. Today, using of various techniques and methods of exploration can be these deposits more carefully studied, assessment and explored. In this paper, have been tried to used of  project management knowledge and  work breakdown structure, a appropriate model to perform the early stages of exploration of  lead – zinc skarn and finally combining the results in the GIS environment for mineral potential mapping in Mahneshan area be provided.

PM2.5 particulate matter is one of the major air pollutants in cities of Iran especially Tehran, which threaten health of Iranian people due to numerous health hazards. Risk levels of these particulate depend on spatial-temporal changes in the air. Hence, this study aimed spatial-temporal analysis of PM2.5 concentration in Tehran's air using the Geographic Information System.
Material and Hourly data of PM2.5 concentration was collected in 2016-2017 from 38 air pollution monitoring stations and was analyzed monthly, seasonally and annually. Inverse Distance Weighting interpolation method was used in order to present concentration maps of the PM2.5. Existence of spatial autocorrelation in data was analyzed by Moran's I and data clustering was accomplished by Global and Local Moran's I and finally Getis-Ord-Gi index was used to determine hot spots of this pollutant in Tehran.
Results of concentration maps of PM2.5 and global and local spatial autocorrelation with PM2.5 concentration hot spot analysis showed that the concentration of this pollutant has an incremental mode from the north to the south of Tehran so that areas in south of Tehran especially Ray stations were the most polluted areas in Tehran. In addition, results showed that the concentration of this pollutant was more in the two colder seasons so that most hot spots were identified in these seasons.
Using several spatial analyses simultaneously showed that Tehran can be separated into two parts of non-polluted north and polluted south, which this issue should be considered by urban planners to improve Tehran's air quality.

Arsenic occurs naturally in the earth’s crust. Although arsenic is useful for various industrial, agricultural, medicinal and other purposes, it exerts a toxic effect in a variety of organisms, including humans. Arsenic could be entered into the environment through anthropogenic (produced directly by human activities) and natural (weathering and leaching from rocks and mineral layers of the earth and sedimentation) sources. This study evaluates the source of arsenic contaminations in the south of Kerman state by preparing the arsenic distribution map.
In south of Kerman Province, with a surface of ca. 5000 km2study, results of assaying the 1804 stream sediment samples for arsenic contamination are used for spatially modeling. Empirical Bayesian kriging (EBK) modelling has been applied to assess the prediction of arsenic contamination for other non-sampling.
The level of arsenic concentration is more than the standard value (12 ppm) in north-western part of the area. This contamination can cause several diseases such as skin, Bladder and Lung cancers, diabetes and Cardiovascular in these areas. Therefore it’s necessary to evaluate the frequency of these diseases in the contaminated area.
Comparison of contaminated area with the location of mineral deposits, mines, chemical industries and agricultural area shows that the source of this contamination could not be anthropogenic and natural source is more probable. Because the spatial modeling is prepared based on the stream sediment samples, it is necessary in the next step, to sample from soil, surface and underground water of contaminated area.

In mining areas, assessing toxic elements (e.g., arsenic) contamination in the soil and stream deposits is a critical issue. It is because mining activities release dangerous elements that enter the environment. In this paper, for modeling the spatial distribution of arsenic contamination in Sarduiyeh-Baft area, in Kerman Province, across an area of ca. 5000 km2, 1804 stream sediment samples were collected and analyzed. The recommended standard limit for arsenic in soil is 20 ppm, so samples showing arsenic concentration >20 ppm are contaminated samples, which need land reform processes. However, since the number of collected samples is limited, indicator Kriging method was used to identify the possibility of contamination. In the study area, there are 32 known occurrences of porphyry-Cu deposits. Thus, in order to estimate the arsenic contamination in the unsampled locations, indicator kriging method was used. The results indicate arsenic contaminations in north and northwest parts of the study area, which could be occurred by mining of the porphyry-Cu deposits. However, the results show that there is no arsenic contamination in the eastern part although there are several mining sites with high activities.

Risk management is today known as one of the nine major project management bodies of knowledge science. An effective risk management allows decision makers to identify the project's strengths, weaknesses, and opportunities. Also, they will be ready to respond if they arise. It includes identifying, recording, ranking and recognizing of response actions and also allocation of actions to corresponding risks. An appropriate risk management of a project helps project managers in getting safe conditions for project design and implementation. Therefore, one area in which there is a necessary need for decision making is project risk management. One of the main steps in risk management is risk assessment and risk ranking, which are important parts of risk management. On the other hand, tunneling projects are now risk-prone activities in mining engineering context. Missing or incomplete risk management in tunneling projects leads to negative consequences, such as longer project time and increased costs. Due to the multiplicity of risk factors in an urban mechanized tunneling project by Earth Pressure Balance ( EPB ) machine, ranking risk is a complex convoluted procedure. So, it is necessary to use multi-criteria decision-making ( MCDM ) methods. Hence, in this paper, in order to analyze the risks of a tunneling project in Tehran Metro-Line 7, we suggest to use Technique for Order of Preference by Similarity to Ideal Solution ( TOPSIS ) method. It is a method for aggregation that compares a set of alternatives by identifying weights for each criterion. It then normalizes scores for each criterion and calculates the geometric distance between each alternative and the ideal alternative. The obtained results show that financial and human resource factors get the first high risk ranks. Furthermore, the risk of dealing with a network of aqueducts is the most well-known problems of the Tehran Metro-Line 7 project. Conducted field studies of the project and visiting the workshop site confirmed the validity of analysis.

Various methods provided for reducing the seepage of water from the dam foundation and abutments that the most common techniques is Curtain Grouting. Quality and efficiency of Grouting operation depend on various factors such as absorption, grout properties and grouting Pressure. One of the most effective parameter is the grouting pressure, because of overpressure, causing hydraulic fracturing in rock mass and low Grouting pressure, causing partial stay of the operation and the site is not completely sealed. So far, different comments, equations, tables and charts have been provided to calculate the necessary pressure in grouting operation. In this paper, has been investigate the effect of grouting pressure in difference stag of Seymareh dam. For this purpose, the grouting pressure is Calculated based on the analytical equations and compared with the actual pressures and finaly the best Model is selected. The results show that Groundy and Zaruba equations (with horizontal joints) prepare more accurate estimates of the grouting pressure than others. Also, new Equation is developed to calculate of grouting pressure as a function of Lugeon number that is able to calculate the acceptable grout pressure.

In this study, Artificial Neural Network was used for predicting optimized conditions of column leaching on copper oxide ore. Optimization, control and analysis of heap leaching implicate an accurate, proper and comprehensive modeling. Providing such models need to identify all the effective parameters in process and the impact of these factors simultaneously on the output of a process. Important parameters such as height of column, particle’s sizes, acid flow rate and leaching duration were studied and it was investigated their impacts on recovery of copper. Experiments were performed in three columns with the heights of 2, 4 and 6 meters and in the particle size distributions of 25.4 and 50.8 mm. The results showed that the copper recovery has an inverse relation with the column height and particle sizes. This relation is direct with leaching duration and acid rate. The copper recovery obtained in the columns with heights of 2, 4 and 6m were 78.63%, 66.27%, and 52.89% respectively. According to the results, the Trained ANN modeling predicts the copper recovery based on operation conditions.

Knowing of the water table around the region and access to its contour maps is one of the most important planning tools for withdrawal underground aquifers and implementing civil projects. Generally, by using the piezometric wells in the region and different methods of estimation, the water table determined. Limitation of these methods is the inability to estimate water table on different days of the year. In this study, by using artificial neural network and time of the measurements of the water table as one of the inputs, the network is trained to estimate contour maps of water table on different days of the year. For this purpose, the water table data in Behbahan plain for the years 1370 to 1385 were used to training the network. Correlation coefficient 0.9906 between actual values and estimated values of the trained network indicates that the estimation is very good. Finally based on this network, contour map of water table in Behbahan plain is plotted for four different days in 1384.

Location of tunnel portal is always one of the vital concerns of geotechnical engineers and inappropriate selection of its position on financial conditions and the time the project will have significant impact. Locating the tunnels portal depends on different technical parameters (such as ecstatic and dynamic safety factor, hardness and position of relaxation pool) and financial factors (time, Maintenance, lining and soil stripping costs). In many situations, there are some proposed sites with different parameters, and it is necessary that the optimum tunnel portal location be selected with the help of multi-criteria decision making methods.In this paper, we have first studied the tunnel portal site selection criteria and their effects on the selection of the location of the tunnel portal. Then, using the Analytic Hierarchy Process (AHP) method, the decision making problem was hierarchically structured at three levels. To check the practicality of the method, the appropriate site for diversion tunnel portal was selected at Moshampa dam, Zanjan province, Iran.

Optimization of sampling scheme is one of the important and attractive issues for geo statisticians and geochemists. So far, various methods have been presented for optimization of sampling location (initial and additional samples). In this article, it is tried to optimize the location of additionalsampling in environmental studies of Sepahan-Shahr area, by simulated annealing method. In Sepahan-shahr area, adjacency to pollutant factor (Irankooh mining area), in addition to the winds blowing whole the year from the side of this mine; have increased the pollution potentiality of the area. In this article, it is tried to optimize the location of additional sampling on the basis of the data obtained from initial samplings. For this purpose, objective function has been defined as “estimation variance minimization”, and simulated annealing optimization method has been used for solving this optimization problem. All the codes related with computation of objective function are prepared by Matlab software. Additional sampling proposed by this method will result in reduction of average estimationvariance of the area from 0.34 to 0.22, which is the highest reduction of estimated uncertainty possible to make in the results of 20 additional samples.

Anomaly No.12A iron deposit is located in Bafgh block in Central Iran. In exploration period, 19 exploratory boreholes were drilled. According to results, the grade behavior is composed of three populations. Since dividing these population physically and geologically was not possible, therefore, the whole grade population was considered as one. In this regard, the normal, lognormal and three parameter lognormal models have been studied. According to the results, the three parameter model was chosed for the deposit.