نشریه روش های تحلیلی و عددی مهندسی معدن
پیاپی 26 (بهار 1400)

The mining sector in many countries, including Iran, is one of the most important paths for achieving sustainable development. The significant correlation between the mining sector and other sectors of the national economy doubles the importance of this sector. Therefore, identifying and expanding the entrepreneurial opportunities in this field can, directly and indirectly, provide a very important path for employment, especially in the deprived regions, such as Sistan and Baluchestan province. Thus, this article tries to provide a model for identifying and developing entrepreneurial opportunities in the mining sector in Sistan and Baluchestan, using a compound approach. The presented methodis a combined exploratory approach that analyzes the interviews using data-based theory technique to examine this explanatory model. According to the data analysis, a process and multidimensional model has been presented for the first time at the level of mining businesses to explain the reasons and consequences of entrepreneurial development. According to the data analysis, 321 final codes were extracted through open coding, and then, using axial coding, they were classified into 151 concepts and 18 categories. Finally, in the selective coding stage, a process and multidimensional model is presented for the first time at the level of mining businesses. The findings indicate the proposed strategies in identifying entrepreneurial opportunities are: integration of knowledge and mining activities, privatization of the mining sector, and transformation of research and development. Also, the results of strategies in the development sector are: promoting mining, improving the efficiency of mining activities, competitiveness in attracting investors, and branding in the field of mining. and finally, some suggestions were proposed for experts in this field.

There are several methods of seismic migration and the main objective of those is to place the reflectors in their true positions. One way for seismic migration is the algorithms that directly apply imaging conditions; on the other hand, the inversion-based imaging method implemented through different strategies to obtain a better depth model that fits the observed data. One of these inversion methods named least square migration solves the inverse problem through direct migration and demigration. The least squares migration has the main advantage that it can gradually reduce errors caused by initial migration. In this paper, particularly the reverse time migration (RTM) is used as an operator of migration and demigration.  Therefore, two numerical schemes are developed to implement least-squares migration with the reverse time migration method.

Methodology and Approaches:

The Helmholtz equation is used to derive the forward modeling operators named reverse time migration (RTM) operator with the Born approximation that is donated as linear inversion. Thus, the linear least square reverse time migration (LSRTM) is the inversion procedure to obtain the final image. LSRTM uses the RTM results as the initial reflectivity model and Born modeling to simulate the seismic data. The reflectivity model is updated by calculating the differences between observed and calculated data through the conventional an adaptive gradient. After multiple iterations, the differences are minimized and this is taken to suggest that the final reflectivity model reflects the real subsurface interface.

The results indicate that the LSRTM through an adaptive gradient procedure can successfully produce the subsurface migrated image free of artifacts including the steep dip structures during a reasonable computational cost.

Summary In this paper, the capability of foundation and support for the Sardasht dam is investigated. Finally, according to the results of numerical modeling in UDEC software, the diameter and distance of grout injection boreholes and dimensions and angle of seal curtain of the dam foundation dam have been suggested.   Introduction The injection is a process whereby a cement slurry is pushed into rock formations through a borehole, thereby reducing the permeability and deformation of the rock mass and increasing its resistance. The rocks are almost impermeable and rock mass permeability is often a function of discontinuity systems. Due to the different and unpredictable behavior of rock masses, there is no specific law for determining the distance of injection holes and generally relies on the experience and judgment of the design engineer when deciding on the borehole distance. So, it is proposed to use numerical methods in predicting the radii of injection for grouting material and also determining the ideal spacing between adjacent holes. As the main results of this study, the optimum pattern for drilling sealing systems for different locations of the Sardasht dam was determined and compared to the empirical models using the discrete element method in UDEC. The optimum deviation angle of the holes was investigated, too.   Methodology and Approaches The Sardasht dam is a trench with a clay core with a height of about 106 meters and a length of 280 meters. The total embankment volume of the dam body is estimated to be about 3 million cubic meters and the volume of clay core is about 516000 cubic meters. The water diversion system consists of two tunnel strings with an inner diameter of 7m and lengths of 627m and 682m in the right support and its height is about 46m. The right tunnel is used as the lower evacuator during the operation period. In order to investigate the in-situ condition of rock mass in Sardasht Dam area, rock mechanical parameters including rock mass quality index (RQD), specific gravity, uniaxial compressive strength, and geometry and resistive properties of discontinuities have been determined and measured. Then, the quality of grout injection in walls and foundation of the Sardasht dam was modeled, using numerical modeling in UDEC discrete element software.   Results and Conclusions The results of this study show that the appropriate borehole spacing for the walls and the foundation should be taken as 3m and 5m, respectively. Also, the results obtained from the numerical modeling of the optimum injection pressure in the construction area of the Sardasht dam were determined for different depths. Based on the numerical modeling results in order to minimize water leakage from the Sardasht dam foundation, the optimum angle of curtain installation should be 17 degrees.

Today, due to the development of cities and the lack of space on the ground, as well as the increase in traffic problems, has resulted in increase of the need to build underground spaces to develop urban transportation systems. This study also examines the effect of the sequence of excavation of adjacent tunnels. The case study used in this study is the access tunnels of Iran Mall collection. For numerical modeling, the Midas GTS finite element software is used in 3D mode, and the effect of the sequence of tunnel excavation on the surface settlement and the internal forces of the structures of these tunnels have been investigated. The results showed that successive excavation of adjacent tunnels resulted in lower settlement values ​​than the simultaneous excavation of tunnels and had a significant effect on the temporary support of the tunnels in the values ​​of forces and bending moment. Finally, in order to validate the results of the surface settlement obtained from the numerical model, the monitoring data of the surface settlement of Iran Mall project were used, it was determined that the numerical results are close to the monitoring data.   Summary In this study, the effect of the sequence of adjacent tunnel excavation was investigated and the study sites of access tunnels of Iran Mall collection located in 22nd district of Tehran were studied. In this study, Midas GTS finite element software was used for design and simulation in three dimensions and the effect of tunnel excavation sequence on axial and shear forces and bending moment on the temporary support of these tunnels and surface settlement were investigated.   Introduction Evaluating the interaction between underground spaces with each other and also between these spaces with existing structures and providing appropriate solutions is one of the most important topics of tunneling in urban environments, which is of particular importance. In some cases, it is necessary to excavate tunnels in the vicinity of each other, which leads to significant impact effects. These interactions depend on the distance between the tunnels, the size of the tunnel cross section, the rigidity of the support system, and the method used to excavate the tunnels.   Methodology and Approaches Since numerical methods can be used to model the tunnel in different and complex conditions of soil mass and rock, and the stresses and displacements created around the tunnels as well as the forces and bending moment on the tunnel support system can be predicted with high accuracy. This research is designed and simulated using 3D Midas GTS finite element software.   Results and Conclusions The results showed that successive excavation of adjacent tunnels resulted in lower settlement values at the ground level than simultaneous excavation of tunnels and had a significant effect on the temporary support of the tunnels in the values of forces and bending moment. Finally, in order to validate the results of the surface settlement from the numerical model, the monitoring data of the surface settlement of Iran Mall project were used, it was determined that the numerical results are close to the monitoring data.

Summary This paper presents statistical and intelligent models to predict stability of different surfaces in underground open stope mining method on the basis of the stability graph method. For this purpose, logistic regression and support vector machine were used to predict the stability state of walls and back, separately. Introduction The open stope mining method can be considered as the most common method for underground mining of hard rock metal mines around the world. The most common method for the design and evaluation of stopes in the mining technique is the stability graph method which was introduced by Mathews et al. in 1981. The stability graph method is based on data collected from different mines including hydraulic radius, stability number (tunneling modified quality index, rock stress factor, joint adjustment factor, and gravity factor), and related stability conditions. The empirical nature of this method, which results in uncertainties in the values of the parameters, and the existence of different types of charts with different stability regions, interpreting the results becomes a challenge. This increases the risk of error in interpreting the results. In addition to these problems, considering the same conditions for all surfaces, such as walls and back, causes obvious errors in the results of this method. In this regard, this paper incorporated statistical and intelligent methods to predict stability state for a database of open stopes by means of stability graph parameters. The novelty of this paper in addition to the used methods is developing models for evaluation of walls and back separately.   Methodology and Approaches New models to predict the stability state of walls and surface of underground open stopes were developed by using statistical and intelligent techniques. For this purpose, logistic regression (LR), as the most common statistical method for classification, was used. Besides, support vector machine (SVM), which is based on the machine learning theory, was applied as a powerful intelligent technique for classification problems.   Results and Conclusions Results of developed models were compared to those of the stability graph method. The following main conclusions were derived from this study: The accuracy of back stability prediction by using the stability graph method, LR and SVM were 29%, 86%, and 95%, respectively. The accuracy of wall stability prediction by using the stability graph method, LR and SVM were 71%, 81%, and 90%, respectively. In comparison with the stability graph method, LR models of back and walls increase the accuracy of predictions by 57% and 10%, respectively. In comparison with the stability graph method, SCM models of back and walls increase the accuracy of predictions by 66% and 19%, respectively. It was concluded that SVM models possess higher performance in the prediction of stable states when compared to the LR models.

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.

An important part of mineral deposit modeling is block modeling. In this paper, various geostatistical methods such as estimation variance, kriging efficiency and Gaussian conditional simulation have been used to select the optimal block dimensions for Surk iron ore and Esfordi phosphate mines.

Methodology and Approaches:

Statistical analysis of Surk and Esfordi iron ore data shows that the data follow an abnormal distribution. Normal Score Transform (NST) is applied for transferring data to Gaussian distribution. Variography of iron and phosphorus composite data was performed in different directions. At first, the optimal block size was determined using kriging efficiency values. In this method, the criterion for selecting the optimal dimensions was to maximize the kriging efficiency. Because the optimal dimensions obtained by kriging efficiency method are not compatible with the operating conditions and anisotropy of the deposit, in the next step, geostatistical simulation method was used. In the simulation process, 20 realizations were considered. The criterion for selecting the optimal block dimensions was to minimize the variance between the realizations in each block.

In the Gaussian conditional simulation method, with the idea that the smaller the variance between the realizations, the greater the similarity of the realizations,  the optimal dimensions were calculated for the Surk mine as 7.5×7.5×7.5 m3 and for the Esfordi mine as 10×10×7.5 m3. The dimensions obtained from Gaussian conditional simulation method are in good agreement with the anisotropic ratios of each mine.According to the optimal dimensions obtained from the Gaussian simulation method, the reserve estimation was made. Based on 20% iron cut off grade, the tonnage of Surk mine is 9.84 million tons with an average grade of 46.07%, and based on the cut off grade of 5% phosphorus, the tonnage of Esfordi mine is 11.93 million tons with an average grade of 13.85%.