Exploration
Naresh Kumar Katariya; Bhanwar Singh Choudhary
Abstract
Slope stability and bench safety in iron ore open-pit mines in western India are comprehensively analysed in this research. To evaluate current mining conditions and identify areas at risk, the study integrates comprehensive field observations, laboratory testing, and advanced slope stability modelling ...
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Slope stability and bench safety in iron ore open-pit mines in western India are comprehensively analysed in this research. To evaluate current mining conditions and identify areas at risk, the study integrates comprehensive field observations, laboratory testing, and advanced slope stability modelling using Slide 6.0 software. Factors of safety (FOS) of various mining sections varied from 0.475 to 1.495, as per limit equilibrium analysis with Slide 6.0. This signifies the presence of possibly unstable slopes that require certain stabilisation measures to ensure operational safety. The research considers how significant environmental factors, like temperature, wind speed, rainfall, and soil moisture, influence slope stability in addition to the geotechnical analysis. Rainfall and soil moisture were found to have a high and statistically significant positive correlation (Pearson correlation = 0.706, p = 0.005), implying that an increase in rainfall results in increased soil moisture content, which in turn affects the behaviour of slopes. Also, a moderate degree of negative relationship between temperature and wind speed was revealed (partial correlation = -0.593, p = 0.042), meaning that smaller wind speeds are characteristically associated with increased temperatures. These findings highlight the importance of continuous monitoring of the environment in open-pit mine operations and the importance of considering environmental factors when assessing slope stability. The information collected in this study provides a solid foundation for developing valuable recommendations intended to enhance safety, better control slopes, and promote the long-term development of mining activities in the region.
Environment
Clement Kweku Arthur; Yao Yevenyo Ziggah; Victor Amoako Temeng
Abstract
Blast-induced noise is one of the most persistent environmental challenges in surface mining, posing significant health risks to workers and nearby communities. Accurate prediction of noise levels prior to blasting is essential for mitigating its adverse impacts. This study proposes an explainable ensemble ...
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Blast-induced noise is one of the most persistent environmental challenges in surface mining, posing significant health risks to workers and nearby communities. Accurate prediction of noise levels prior to blasting is essential for mitigating its adverse impacts. This study proposes an explainable ensemble machine learning framework for predicting blast-induced noise using data from an open-pit gold mine in Ghana. Four ensemble models namely: Extreme Gradient Boosting (XGBoost), Gradient Boosting, Adaptive Boosting (AdaBoost), and Categorical Boosting (CatBoost), were developed and evaluated using a comprehensive dataset of 324 blasting events. Performances of the developed models were assessed using coefficient of determination (R²), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of the variation of the root mean squared error (CVRMSE), with XGBoost emerging as the best-performing model (R² = 1.0000, RMSE = 0.0005, MAE = 0.0004, MAPE = 0.0010, CVRMSE = 0.0013). To address the black-box nature of ensemble method, Shapley Additive exPlanations (SHAP) was employed, offering both global and local interpretability. SHAP analysis identified the distance from the blast site to the monitoring point as the most influential factor. This integrative approach not only enhances predictive accuracy but also improves model transparency, supporting sustainable mining practices aligned with United Nations Sustainable Development Goals (SDGs) 3 and 15.
Exploitation
Gebremariam Mesele; Miruts Hagos; Bheemalingeswara Konka; Tsegabrhan Gebreset; Misgan Molla; N Rao Cheepurupalli; Girmay Hailu; Negassi Debeb; Assefa Hailesilasie
Abstract
The Dallol Depression, located in the northern Danakil Depression, has a complex geological history shaped by Afar rifting, containing approximately 1.7 km of evaporite deposits. These deposits, heavily influenced by volcanic activity and extensional tectonic faulting, exhibit significant structural ...
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The Dallol Depression, located in the northern Danakil Depression, has a complex geological history shaped by Afar rifting, containing approximately 1.7 km of evaporite deposits. These deposits, heavily influenced by volcanic activity and extensional tectonic faulting, exhibit significant structural variability. This research focuses on the potash-bearing section of the salt sequence, which consists of several distinct layers including the marker bed, sylvinite member, upper carnallitite member, bischofitite member, lower carnallitite member, and kainitite member. Employing satellite imagery (Landsat Thematic Mapper), geological and structural mapping, borehole data, and seismic analysis, this study characterizes the sub-surface features of the evaporites and estimates their reserves. The RockWorks software facilitated the development of a subsurface stratigraphic map and a three-dimensional fence diagram for enhanced interpretation. Seismic data indicate that while the upper layers of the evaporite deposits are largely horizontal and undeformed, deeper layers exhibit considerable tectonic disturbance. Thickness variations were observed, with evaporite and alluvial deposits being thinner at the southeastern rim and thicker in the eastern concession center. The total potash reserve is estimated at approximately 2.96 billion tons, of which 877.76 million tons (29.60%) remain unexploited. Current borehole designs restrict the company's extraction capacity to 24.64%. This study recommends revising mining strategies, incorporating updated borehole designs and advanced geophysical methods to improve potash recovery and promote sustainable practices in the Dallol region.
Exploration
V.S.S.A Naidu Badireddi; Vije durga raju Mullagiri; MVS sekhar Bezawada; Ambili V; K S N Reddy
Abstract
The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources ...
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The Bavanapadu-Nuvvalarevu coastal sector in Andhra Pradesh, India, hosts substantial subsurface heavy mineral (HM) resources, presenting significant economic potential. This study employs ArcGIS raster techniques to estimate Total Heavy Mineral (THM) and Total Economic Heavy Mineral (TEHM) resources in a 39 square kilometers area, integrating geospatial analysis with field data from core sediment samples. The findings reveal a total of 2.681953 million tons of THM, including 2.434422 million tons of TEHM, with the highest concentration observed in the top 1-meter sea bed sediment layer (1.605286 million tons). Ilmenite, garnet, and sillimanite dominate the mineral assemblage, accompanied by smaller quantities of zircon, monazite, and rutile, offering an estimated revenue potential of $634 to $851 million USD. The application of ArcGIS methodologies, particularly inverse distance weighting (IDW) interpolation, enabled precise mapping of HM distribution, despite challenges such as wide sample spacing and shallow core penetration. While the study highlights the economic and industrial significance of the Bavanapadu sector, it also underscores environmental concerns, including habitat disruption and sediment degradation, associated with mining. Sustainable practices, such as advanced separation technologies, site rehabilitation, and comprehensive environmental impact assessments (EIAs), are essential to mitigate ecological impacts. This research demonstrates the efficacy of GIS-based techniques in resource estimation and sustainable mining, offering a replicable framework for coastal and offshore mineral resource management globally. The findings provide critical insights into balancing economic growth with environmental preservation, setting a benchmark for responsible heavy mineral extraction in dynamic coastal environments.
