International Journal of Mining and Geo-Engineering
Year:2024 | Volume:58 | Issue:2|Papers Count:12

Euler deconvolution system is a well-known approach to estimate the depth of underground sources in potential field geophysics. Over-determined Euler linear equations are usually solved independently and separately for the gravity and magnetic data, and each result is an estimate for the depth of the potential sources. This technique is widely utilized to analyze individually the depth variations of magnetic and gravity sources. However, depth estimation of each of the mentioned potential fields may return specific and exclusive results regarding the complex nature of the subsurface structures, and the gravity and magnetic separate depth estimation solutions may be discordant in many aspects. In cases of low resolution for gravity and magnetic data sets, this study indicates that independently solved Euler depth estimation systems cannot yield reliable and accurate solutions of potential field sources. Combining the gravity and magnetic data and simultaneously solving the Euler equations for gravity and magnetic potential fields, this research presents a novel approach called the joint Euler method with a proper capability to return more accurate and improved depth estimations for boundary and body of potential field sources. The presented method was solved and examined over homogeneous and non-homogeneous synthetic scenarios with reduced resolution, and the depth solutions were also compared with the separate approach. After obtaining the desired results from the synthetic models, the joint Euler technique was applied to the gravity and magnetic data of the Kifl oil trap located in Iraq. The results were quite promising rather than the separate depth estimations, proving the sufficiency and applicability of the proposed potential field method in terms of interpretational aspects.

The exploration and management of abundant economic mineral resources of Pakistan, particularly the vast marble deposits in the northwestern region, hold immense potential for driving economic growth. The use of marble in the construction industry faces extensive challenges such as undeveloped mining processing methods, incomplete understanding of marble qualities, undefined selection criteria for suitable varieties, and the environmentally harmful consequences of excessive waste production. This research developed a laboratory investigation protocol to characterize distinct marble deposits in Buner, Pakistan, each offering unique compositions and petrographic features. Three marble varieties were identified including pure calcitic (over 90% calcite) with low silica content (0.1% to 2.5%); impure calcitic (non-carbonate minerals up to 20%) with 19.8% silica and 31% lime; and pure dolomite (over 20% dolomite) with 29% lime and 23% magnesium oxide. The distinctive petrographic features of the marble deposits, such as equigranular structures, subhedral to anhedral grains, granuloblastic textures, and schistosity in impure calcitic, as well as luster-displaying dolomite in pure dolomite, provide valuable insights into their geological characteristics. Furthermore, the physical properties of the marble types exhibit correlations with their compressive and tensile strengths. Notably, the low specific gravity, water absorption, and porosity of the investigated marble result in high strength values. The average compressive strength was found to be 31 MPa for pure calcitic, 35 MPa for impure calcitic, and 59 MPa for pure dolomite marble. Likewise, the tensile strengths measured 6 MPa, 7 MPa, and 9 MPa, respectively. While the investigated marble types prove to be excellent choices for dimension stone applications, it is crucial to note that they do not meet the standards required for cement production and paint manufacturing. This research contributes to the understanding of Pakistan's marble resources, refined processing methods, and sustainable construction practices.

The effect of removing suction on energy consumption, displacement, and separation of particles with different sizes and densities in the jig was investigated by CFD-DEM coupling. Water velocity functions were categorized into four modes: normal sinusoidal, partial removal of suction, complete removal of suction, and optimal. Particles rise to a certain height in the normal sinusoidal mode Particles rise to a certain height in the normal sinusoidal mode, while their jump height increases in the case of partial or complete removal of suction. The jump was controlled by fluid and added (Hutch) water velocities. Increasing the maximum jump height of particles leads to a decrease in separation, an increase in operational costs, and heightened particle mixing and energy consumption. In both modes of partial or complete suction removal, the fluid velocity should be reduced. The extent of velocity reduction depends on the power required to move the particles. Simulation of coarse particles (7 and 8 mm) revealed that in complete removal of suction, the velocity should be decreased to less than half of the normal sinusoidal mode, and the hutch water velocity should be equivalent to the velocity amplitude. The energy consumption for the optimal mode was significantly lower than that of the other modes.

Induced polarization (IP) tomography measurements as a near-surface geophysical method can provide information about the degree of chargeability of subsurface materials, and are commonly used in mineral exploration, engineering studies (e.g., sediment/bedrock interface identification, crushed zones and faults detection, and landslide and soil properties imaging.), as well as in environmental investigations (contaminant plums identification and landfill characterization). The purpose of these measurements is to obtain the distribution of polarizability characteristics inside an object, generally below the surface, at the boundary of the object, or outside the area in question. The result of such measurements can be mathematically modeled for the specific polarizability properties by the solution of Poisson’s equation restricted by appropriate boundary conditions. In this paper, we focus on the importance of simulating induced-polarization responses and retrieving chargeability distributions in geo-materials to enhance the characterization of subsurface structures. We present the methods for forward modeling and nonlinear inversion of induced-polarization measurements. To this end, in the first step, Poisson’s equation for a two-dimensional ground with arbitrary distribution of conductivity is solved using the finite difference numerical method and in the next step, based on the existing relations between conductivity and chargeability (Siegel’s formulation), the apparent induced polarization response is calculated. Finally, we solve the nonlinear chargeability inversion problem following a nonlinear apparent resistivity inversion. This is achieved by imposing physical constraints to prevent the estimation of unrealistic model parameters, using a Newton-based optimization method. To evaluate the efficiency of the proposed methodology, we utilized the proposed algorithm to two simulated examples and a real data set. Our numerical results show that the algorithm reliably represents the main features and structure of the Earth’s subsurface in terms of the resistivity and chargeability models. All the algorithms presented in this paper have written in the MATLAB programming language.

Gravity inversion methods play a fundamental role in subsurface exploration, facilitating the characterization of geological structures and economic deposits. In this study, we conduct a comparative analysis of two widely used regularization methods, Tikhonov (L2) and Sparse (L1) regularization, within the framework of gravity inversion. To assess their performance, we constructed two distinct synthetic models by implementing tensor meshes, considering station spacing to discretize the subsurface environment precisely. Both methods have proven ability to recover density distributions while minimizing the inherent non-uniqueness and ill-posed nature of gravity inversion problems. Tikhonov regularization yields stable results, presenting smooth model parameters even with limited prior information and noisy data. Conversely, sparse regularization, utilizing sparsity-promoting penalties, excels in capturing sharp geological features and identifying anomalous regions, such as mineralized zones. Applying these methodologies to real gravity data from the Safu manganese deposit in northwest Iran, we assess their efficacy in recovering the geometry of dense ore deposits. Sparse regularization demonstrates superior performance, yielding lower misfit values and sharper boundaries during individual inversions. This underscores its capacity to provide a more accurate representation of the depth and edges of anomalous targets in this specific case. However, both methods represent the same top depth of the target in the real case study, but the lower depth and density distribution were not the same in the XZ cross-sections. Inversion results imply the presence of a near-surface deposit characterized by a high-density contrast and linear distribution, attributed to the high grade of manganese mineralization.

Imaging the intra-sediment magma chamber in the Damavand region, northern Iran, is beyond the resolution of the local seismic observations. Gravity anomalies can precisely image the lateral extension of magma reservoir. In order to provide vertical extension of magma chamber, we apply inversion of magnetic data with a higher sensitivity to shallow structure in comparison to gravity data. More importantly, knowledge of magma chamber’s density allows prediction of its mechanical behaviour including the potential of eruption. As Damavand is estimated to be an active volcano, it is important to revisit the physical properties of the magma chamber to be able to evaluate the potential of eruption. Here, we apply the sparse norm inversion of Bouguer gravity anomaly and magnetic data to model the uppermost crust beneath Damavand volcano. Qualitative analysis of the Bouguer anomaly shows that the power of the spectrum remains almost unchanged by upward continuation using heights greater than 4 km. Thus, we conclude that the 4-km upward continued Bouguer anomaly represents the regional gravitational effects free from very shallow effects. Inversion of magnetic anomaly, interestingly, shows a susceptibility structure, with susceptibility contrast of up to ~ +0.025 SI, in the same place as density anomaly. This study proposes a 10-km wide magma chamber beneath Damavand from depth ~3 km to depth ~12 km. The resulted density structure is comparable with the obtained values from derived densities (using thermodynamic mineral phase equilibrium) based on geochemical data and those from conversion of seismic velocity to density. According to the geochemical data analysis, the lava is andesitic which is categorized among dense crustal rocks (2.8 g/cm3). But, our modeling results shows a density contrast of maximum + 0.25 g/cm3 between the magma chamber and the surrounding sedimentary rocks (with density of 2.45 g/cm3) above 5 km. Therefore, we can conclude that the shallow magma chamber, composed of dense andesite, is relatively warm and probably not completely consolidated. The high temperature of magma chamber appears to be neutralized by the impact of high density of andesite (naturally dense rocks) to result in moderate negative anomaly in tomography (i.e., ∆Vs=~ -2 %). Magma chamber’s temperature might exceed 750-800 ºC which is still beyond the solidus-liquid transition temperature of 1100 ºC. Therefore, we can conclude that the magma is no liquid and is partially consolidated.

The mining process involves several sequential stages, including drilling, blasting, loading, transportation, and mineral processing. Among these stages, blasting costs (BC) exhibit greater sensitivity compared to others. Inadequate blasting practices can lead to additional drilling, increased explosive consumption, and environmental consequences such as ground vibrations. The variability in blasting patterns and ore rock hardness results in variations in BC. Consequently, there's a need for a method that can establish a relationship between design, geotechnical parameters, and blasting costs while accounting for uncertainties in input parameters. In this study, the rock engineering system method (RES) was employed to construct a complex and non-linear model for predicting blasting costs, considering uncertainties in geotechnical parameters. Data from six limestone mines in Iran were utilized, incorporating 146 data points. The input parameters used for creating this relationship included hole diameter, burden, Emulsion, hole number, hole length, spacing, stemming, sub-drilling, rock hardness, ANFO, number of electric detonators, unia­xial compressive strength, and specific gravity. The model was built using 80% of the data (117 data points) to establish the RES-based method, with the remaining 20% (29 data points) dedicated to evaluating and validating the model. To assess its performance, the RES-based method was compared to other statistical regression techniques, utilizing statistical indicators such as root mean square error (RMSE), mean square error (MSE), and coefficient of determination (R2). The results demonstrated that the RES-based method significantly outperformed other statistical approaches, with impressive accuracy, as indicated by MSE=0.00608, RMSE=0.078, and R2=0.9518 in predicting explosion costs. Therefore, the model developed through this method can be effectively applied in mining and rock mechanics projects, providing a high level of accuracy.

Current research deals with the numerical investigation on the behavior of helical piles resting in cohesionless soil subjected to compressive load. The effect of diameter of pile shaft (DS), diameter of helical plate (Dh), depth of pile (H), Inter-helical spacing ratio (S/Dh) and number of helix (m), and type of sand on the load-displacement behaviour of helical pile was evaluated. The numerically determined compression capacity of the helical pile was compared with the existing theories. Apart from this, artificial neural network technique was employed on the obtained results to develop the model equation. An increase in the compression capacity of single and double helical pile was observed with increase in pile depth, friction angle of sand and diameter of pile shaft. For double helical pile, the optimum inter-helix spacing ratio was found 3.

For the construction of railway embankments, geotechnical engineers pay special attention to slope stability studies. The factor of safety values plays a crucial part in assessing the safe design of slopes. The factor of safety values is used to determine how close or far slopes are from failing due to natural or man-made causes. The factor of safety is a numeric value to indicate the relative stability, it doesn’t tell about the actual risk level of any structure, but the reliability index and probability of failure quantify the risk level. The present study discusses the findings of a study to determine the factor of safety of an embankment of height 12.3 m by using Geo-studio 2012 software. In this article, the fragility curve for six different types of cross-sections was also developed i.e. the graph between the probability of failure ( ) and horizontal seismic coefficient ( ), for various values of  (i.e. 0.1, 0.12, 0.144, 0.18, 0.2, 0.3, 0.4 and 0.5). It is observed from the developed fragility curve, as the  value increases  value decreases. A fragility curve can be used to calculate failure probability over a range of seismic zones, and for design purposes, a given seismic zone and probability of failure a unique reliable side slope is selected. Further, two machine learning (ML) models namely, Deep Neural Network (DNN) and Support Vector Regression (SVR) have been developed for the prediction of the factor of safety for different sides slope. Obtained correlation values (R) for SVR and DNN are approximately 0.95 and 0.82 respectively. From the help of the predicted factor of safety fragility curve against horizontal seismic coefficient is drawn for both SVR and DNN models, that for reducing the time of calculation and ease in working best result giving model will be suggested for further analysis of railway embankment.

The shape of a blasted rock mass, or simply muckpile, affects the efficiency of loading machines. Muckpile is defined with two main parameters known as throw and drop, while several blasting parameters will influence the muckpile shape. This paper studies the prediction of muckpile shape in open-pit mines by applying an artificial neural network designed by a genetic algorithm. In that regard, a genetic algorithm has been used in preparing the neural network architecture and parameters. Moreover, input variables have been reduced using the principal component analysis. Finally, the best models for predicting throw and drop are determined. Analyzing the performance of the proposed models indicates their superiority in predicting muckpile shape. As a result, the Mean Squared Error of throw is 0.53 for train data and 1.24 for test data. While for the drop, the errors are 0.45 and 0.58 for the training and testing data. Furthermore, sensitivity analysis shows that specific-charge effects drop and throw more.

The micro-resistivity imaging log is a crucial tool for measuring the heterogeneous features of a formation. It objectively and quantitatively describes various reservoir characteristics, including fine structures, thin strata, fissures, and sedimentary facies. In these imaging tools, measurements from button arrays create an electrical image of the wellbore. However, gaps between tool pads limit coverage, and damaged buttons may compromise image quality.In this study, we examine image log data for factors impacting data acquisition, followed by processing for basic correction, image enhancement, and static and dynamic image log creation. To achieve 100% coverage, the Minimum Weighted Norm Interpolation (MWNI) algorithm fills gaps between tool pads. Finally, the Anisotropic Diffusion Filter (ADF) reduces noise and enhances image log quality in MATLAB, providing a comprehensive image from logging tools. As image logs play a crucial role in illustrating the wellbore and reservoir, this study suggests a new workflow to successfully tackle the challenges linked with acquiring comprehensive image log coverage.

In this research, the levels of safety, health, and environmental risks in a building stone processing company (BSPC) have been identified using the integrated approach of EFMEA (Environmental Failure Mode and Effects Analysis) and William Fine method, along with the TOPSIS technique for prioritizing organizational safety layers, examining potential incidents, and enhancing organizational efficiency. To achieve this, data and risk assessment information were first collected and evaluated, and then, with the formation of an expert task force, brainstorming sessions were held to identify and analyze environmental risks in the production process using the EFMEA technique. Additionally, with the assistance of the William Fine method, safety and health risks in the production process were identified and examined. In the next step, the costs of corrective actions were calculated, and the results obtained from the tables of both EFMEA and William Fine techniques were combined, and decisions were made regarding risks with high and very high levels. Subsequently, using TOPSIS, protective layers were prioritized based on two criteria: cost and time. Following the risk assessment using the EFMEA method, 4 risks were classified as high-risk, 9 risks as medium-risk, and 2 risks as low-risk. Subsequently, employing the William Fine technique, a total of 41 hazards were evaluated across 5 worksheets. 5% of the hazards were categorized as very high-risk, 19% as high-risk, 27% as medium-risk, and 49% of the evaluated hazards were classified as low-risk. Ultimately, the results obtained from the integration of the William Fine and EFMEA techniques categorized 2 risks as very high-risk, 12 risks as high-risk, 20 risks as medium-risk, and 22 risks as low-risk. Furthermore, working at heights was selected as one of the risks with high risk, and protective layers and control measures were proposed and examined. The use of helmets, shoes, harnesses, and the establishment of a safety platform, considering both time and cost criteria, is the first priority for controlling risks in working at heights activity.