International Journal of Mining & Geo-Engineering
Volume:57 Issue: 2, Spring 2023

In current study, sampling from the Baghak anomaly in Sangan mines has been carried out based on radioactivity and radiation measurement methods. The goal of this study is to survey the presence or absence of such a relation (between rare earth and radioactive elements) in a skarn mine which is a different case study in central Iran. Mineralogical studies (based on optical and electronic microscopic observations), univariate and multivariate statistical investigations and geochemical analyses are applied. Results show that the Baghak anomaly is due to a significant amount of U, Ce, La and a high concentration of REEs. It seems that mineralization of Th and REEs occurred simultaneously with the formation of iron skarn, while uranium mineralization in hydrothermal form occurred in a secondary phase after the skarn iron mineralization. Finally, it could be acknowledged that in addition to presence of such a relation in the mineralization (central Iran mineralizations), there is an acceptable correlation between these elements in Baghak iron-skarn mineralization.

In the open-pit mining method, it is necessary to design the ultimate pit limit before mining to determine issues, such as the amount of minable reserve, the amount of waste removal, the location of surface facilities, and production scheduling. If the obtained profit from the extraction of the pit limit becomes maximum, it is called the optimum pit limit. Various algorithms have been presented based on heuristic and mathematical logic for determining the optimum pit limit. Several algorithms, such as the floating cone algorithm and its corrected forms, the Korobov algorithm and its corrected form, dynamic programming 2D, the Lerchs and Grossmann algorithm based on graph theory have been presented to find out the optimum pit limit. Each of these algorithms has particular advantages and disadvantages. The designers of the corrected form of the Korobov algorithm claim that this algorithm can yield the true optimum pit in all cases. Investigation shows that this algorithm is incapable of yielding the true optimum conditions in all models, and in some models the method produces an optimum with a negative value. In this paper, this algorithm has been evaluated, and a modification model is also presented to overcome its disadvantage. This new algorithm was named the Korobov algorithm III. In this paper, this new algorithm was considered in different models of two and three-dimensional space. A case study for designing of the optimum pit limit in three-dimensional space was done for a gold mine in the sewed country. The outcomes of Table 9 show that this new method designs a pit limit with a value of 69428.59 that has better results than previous Korobov algorithms.

Geoelectrical methods are considered common subsurface geophysical imaging tools that provide significant insight into the electrical properties of targets. Considering the three-dimensional nature of subsurface structures, geoelectrical survey data and their 3D inverted models can yield reliable and accurate results. In this research, using an unstructured tetrahedral meshing, three-dimensional forward and inverse models of electrical resistivity and chargeability data were performed for geological structures with travertine layers.  The Application of the unstructured mesh for the discretization of subsurface geological units increases the speed and accuracy of the modelling procedure, as well as the flexibility in designing and implementing meshing on tough topographies and the complex-shaped geometry of the target mass. Using an open-source and full-item Python software named ResIPy, the forward and inverse models were calculated and interpreted precisely. According to the geological background of the studied area, to investigate the applicability and efficiency of the 3D geoelectrical modelling method in imaging the subsurface travertine deposits, three synthetic scenarios were modeled according to the geological setting of the studied area.  The results of the 3D inversion of the synthetic models indicated the accuracy and validity of this procedure in the exploration of underground travertine deposits. As a real case study, the electrical resistivity and chargeability survey datasets in the Atashkohe travertine mine were inverted in 3D, aimed to inferring schematic geological sections along the three surveyed profiles. The survey was conducted with electrode spacing of 10 and 15 meters, using a combination of dipole-dipole and pole-dipole arrays. Considering the two-dimensional nature of these data and the relatively large distance between the two main profiles, the three-dimensional inversion results may increase the error rate. Therefore, the 2D batch inversion was preferably utilized in order to obtain a more realistic and sensible geological model. According to the geological studies and instrumental analysis of the rock samples, three types of geological structures were identified throughout the study area. Based on the subsurface electrical characteristics inferred along each profile, three geological layers were designed to illustrate the underground structures. The comparison of the inferred geological models and the drilling results along one of the survey profiles demonstrated acceptable compatibility and concordance, indicating the efficiency of the research utilized approach.

From an economic, technological, and environmental perspective, sulfur removal from coal resources has received increased attention in recent years. The present work investigates the ability of chemical (Meyers and Molten caustic leaching (MCL)) and biological methods for the desulfurization of Tabas coal. Accordingly, in the Meyer process, at 1 M ferric sulfate concentration, during 90 minutes at 90 ° C, 61.78 % of ash and 82% of pyrite, and 51.35% of total sulfur were removed from Tabas coal, respectively. The MCL method was also investigated. Hence, based on the MCL experimental condition of caustic soda /coal ratio of 2, leaching time of 60 minutes, and constant temperature of 180 ° C, 71.82 % of ash, 88% of pyrite sulfur, and 57.85% of total sulfur content were removed from Tabas coal, respectively. Furthermore, biodesulfurization of Tabas coal was conducted using a mixed culture of acidophilic iron- and sulfur-oxidizing mesophilic bacteria. The effect of time, bacterial medium, solid/liquid (S/L) %, and the absence of bacteria were investigated, and based on the results, time was the most significant parameter. Accordingly, 68.98% of ash, 92% of pyrite sulfur, and 72.43% of total sulfur were removed from Tabas coal with 20% v/v bacterial inoculum during 20 days, respectively.

Characterization of large reservoir models with a great number of uncertain parameters is frequently carried out by ensemble-based assimilation methods, due to their computational efficiency, ease of implementation, versatility, and non-necessity of adjoint code. In this study, multiple ensemble-based assimilation techniques are utilized to characterize the well-known PUNQ-S3 model. Accordingly, actual measurements are employed to determine porosity, horizontal and vertical permeabilities, and their associated uncertainties. In consequence, the uncertain parameters of the model will gradually be adapted toward the true values during the assimilation of actual measurements, including bottomhole pressure and production rates of the reservoir. Monotonic reduction of root-mean-squared error and capturing the key points of the maps (such as direction of anisotropy and porosity/permeability contrasts) verify successful estimation of the geostatistical properties of the PUNQ-S3 model during history matching. At the end of the assimilation process, the RMSE values for Deterministic Ensemble Kalman Filter, Ensemble Kalman Filter, Ensemble Kalman Filter with Bootstrap Regularization, Ensemble Transform Kalman Filter Symmetric Solution, Ensemble Transform Kalman Filter Random Rotation, and Singular Evolutive Interpolated Kalman filter are 1.120, 1.153, 1.132, 1.132, 1.129, and 1.113, respectively. In addition to RMSE, the quality of history match as well as prediction of future performance are looked into in order to assess the performance of the assimilation process. Obviously, the results of the ensemble-based assimilation methods closely match the true results both in the history match section and in the future prediction section. Besides, the uncertainty of future predictions is quantified using multiple history-matched realizations. This is due to the fact that Kalman-based filters use a Bayesian framework in the assimilation step. Accordingly, the updated ensemble members are samples of the posterior distribution through which the uncertainty of future performance is assessed.

The Birjand region is a part of the South Khorasan province, situated in the structural-magmatic zone of eastern Iran. As a part of the continental shelf, it forms from subduction during the Cenozoic and subsequent continental collisions. This region is favorable for copper and gold mineralization for various geological reasons. The ultimate goal of this study is to create a Cu geochemical potential map to delimit prone regions for further mining activities. A total of 2468 geochemical samples were gathered to run a 20-element analysis. Taking data preprocessing approaches such as correction of outlier data and data normalization into consideration, a fractal graph through Concentration-Number (C-N) model was produced to isolate different geochemical populations of Cu, Pb, Zn, Ag, Ba, and Ni for Cu targeting. Then, a Prediction-Area (P-A) graph was plotted for each geochemical variable to determine the weight of each evidence map. The results show that Barium map indicates a prediction rate of 72% and specifying 28% of the studied areas as mineralization prone areas. The zinc geochemical map presents an ore prediction rate of 65% and 35% of area as potential zone. In addition, copper with an ore prediction of 56% covered 44% of the Birjand region. Finally, a hybrid evidence map was overlaid. Accordingly, the geochemical potential areas are further located towards the south and south-east of Birjand, which are closely related. Moreover, there are highly favorable areas in the middle part. It is noteworthy that the copper potential map has higher efficiency over each individual geochemical evidence, with an ore prediction rate of 75% and occupying 25% of the area as favorable zones.

Dealing with numerous reviews and widespread inquiries, it has been concluded that much more information and interpretive parameters are accessible regarding the subsurface structures when using a particular frequency range in the spectral induced polarization (SIP) measurements. Therefore, the interpretation uncertainty would diminish which causes studies with more valid and authentic outcomes. This could be achieved by using a comprehensive and general model which is appropriate for representing electrical features variation in terms of frequency, known as the Cole-Cole model. By using the SIP method and applying a defined broad of frequencies, it would be conceivable to describe items such as medium properties, spectral behavior of the studied area, and the intensity of each single parameter. The widespread use of the SIP method requires accurate and fast modeling and inversion algorithms. An integral part of every geo-electrical data inversion is an accurate and efficient forward modeling resulting in numerical simulation of responses for a given physical property model. In other words, like every other geophysical method, a reliable spectral-induced polarization inversion is highly dependent on the accuracy of the forward problem. Forward modeling is accomplished over a 2D earth structure to generate complex resistivity data by simulating current flow into the earth's surface and solving the Poisson equation containing complex values. In this contribution, a finite difference algorithm is applied to solve the complex partial differential equations (PDEs) restricted by a mixed boundary condition. A spatial Fourier transform of the PDEs, with respect to a defined range of wavenumbers, is carried out along the strike direction to elucidate 3D source characteristics. Eventually, it is necessary to conduct an inverse Fourier transform to obtain potential solutions in the spatial domain. To verify the accuracy of the proposed numerical algorithm, some synthetic models are simulated and the forward responses, including resistance and phase values with respect to a specific frequency spectrum, are calculated. Furthermore, a comparison between our numerical results and those of Geotomo geo-electrical software is provided.

Reconstruction of geological images using partial measurement is one of the most important topics in geosciences. In many methods, this is done using training images and very complex models which increase the computational complexity. In the first part of the article, a simple method based on spatial domain filters such as median and mean filter has been presented to reconstruct geological images. One of the most significant characteristics of this method is that it does not need the training image; moreover, its computational complexity is less than the other advanced methods. Via this method, it is easy to reconstruct binary, continuous, and three-dimensional images. The results show that the reconstruction accuracy of the proposed method is also acceptable. In the second part of the article, to introduce quantum computing to geosciences and encourage researchers to work on this issue, a quantum median filter is proposed to reconstruct geological images. According to the results, this method has much less computational complexity than classical methods such as DS. Also, its results are acceptable in terms of reconstruction rate. Due to the high speed of quantum algorithms and the widespread use of quantum computers in the near future, researchers in this field must become more familiar with quantum computing.

The massive deposit of medium-grained, white-colored sandstone of about 20 m thick, is located immediately above the coal seam in Quarry No. 2, resulted lesser yield due to lower powder factor (m3/kg) and over-sized boulder formations, specifically from the stemming zones at Chotia Opencast Coal Mine of M/s Prakash Industries Limited, which was operating at a depth of about 30 to 40 m with an average bench height of 5.5 m. The criticality of the problem led to the rectification of the blast design parameters through incorporation of pilot holes and pocket charges, decked charges, air-decking, evolution of static energy distributions, and fragment data analysis for establishing optimized design patterns with available machinery. Several test blasts along with on-site testing of explosive quality, rebound hardness tests of overlying strata, and rearrangements of firing patterns through surface delay connections were considered for adopting the best-suitable blast pattern for the mine. Generalized and perceptible inferences were made to apply the results in other mines with similar kinds of problems.

The blasting operation is one of the technologies used for breaking rock masses and reducing the rock mass into smaller sizes to improve transportation and further particle separation. The improvement of blast fragmentation supports the maximization of mining operation and productivity. Soft computing and regression model has been developed in this study to optimize small-scale dolomite blast operations in Akoko Edo, Nigeria. WipFrag software was used to analyze the results of 35 blasting rounds. As independent variables, one uncontrollable parameter and five controllable blast parameters were chosen to predict blast particle sizes using four mathematically motivated soft computing model approaches. The prediction accuracy of the developed models was tested using various model performance indices. The study revealed that rock strength influences blast fragmentation results, and based on the rock strength properties, the fragmentation block size increases with an increase in rock strength. The results of the model performance indices used for the evaluation of the proposed models showed that the modified Artificial Neural Network (ANN) called Hunter Point (HP-ANN) has the highest predictive accuracy. A new model evaluator was also developed in this study called the decision factor. Its application indicates that the HP-ANN model is the best model suitable for the prediction of blast fragment size distribution. Therefore, the developed models can be used to predict the blast result mean size (X50) and the 80% percentage passing size (X80) for mining engineering blasting practices.

The effective use of residual laterite soils is usually hindered because of their mineralogy and high fines content. This paper studied the potential improvement in the geotechnical and mineralogical properties of fly ash-treated residual laterite collected from Southwest Nigeria. Some physical and geotechnical properties, such as plasticity, compaction, unconfined compressive strength (UCS), and California Bearing ratio (CBR) of untreated and treated laterites were determined using ASTM standard methods. Stabilization was achieved by mixing the laterite with varying proportions (0%, 3%, 6%, 9%, 12%, and 15% by mass of dry laterite) of fly ash. Mineralogical analysis of untreated and treated laterite was done using the X-ray diffractometer (XRD). The results showed a slight initial increase at low proportions of fly ash (at 3%) in the plasticity properties and a subsequent decrease (of up to 65%) afterward. The UCS and CBR of the treated laterite increased over 100% (maximum UCS 110% and maximum CBR 183%) at 6% fly ash content. XRD analysis showed the formation of new minerals, predominantly portlandite, within the stabilized soils. This study confirmed that using fly ash for the stabilization of residual laterite soils is potentially viable for road construction.

This paper investigates the effect of jointed rock mass properties on the Minimum Required Caving Span (MRCS) in the block caving method using numeric and heuristic approaches. To do so, the effects of five parameters of jointed rock mass, namely joint set number, joint spacing, joint inclination angle, joint surface friction angle, and undercut depth on MRCS, were investigated using a discrete element code. For this purpose, many numerical models were generated with various rock mass parameters. Moreover, Gene Expression Programming and Artificial Neural Networks were employed to create a heuristic model for MRCS. The model parameters were subjected to sensitivity analysis. All model input parameters showed sensitivity to the model. There are several effective parameters on MRCS, but joint dip and joint set numbers are the most important and the smallest.