Albite ± Actinolite-Altered Porphyry Dykes in Archean Gold Deposits of the Boulder Lefroy-Golden Mile Fault System, Yilgarn Craton, Western Australia: Petrography, Chronology, and Comparison to Canadian Albitites

The Boulder Lefroy-Golden Mile fault system in the Archean Yigarn Craton is the most productive gold-mineralized structure in Australia (>2300 t Au). The New Celebration deposit (51 t Au) is part of a group of hematite- and anhydrite-bearing mesothermal deposits and Fe-Cu-Au skarns associated with monzodiorite-tonalite intrusions in the strike-slip fault system. Ore-grade biotite-carbonate and late sericite-carbonate-alkali feldspar replacement is bound to the contacts of a felsic (low Cr, Ni, V) quartz-plagioclase porphyry dyke dated at 2676 ± 7 Ma. The sodic-potassic alteration of the felsic boudinaged dyke contrasts with the albite-actinolite alteration in the adjacent mafic (high Cr, Ni, V) plagioclase porphyry dated at 2662 ± 4 Ma, although both share the same sulfide-oxide assemblage: pyrite ± chalcopyrite, magnetite ± hematite. The younger porphyry locally crosscuts foliation and is bordered by post-kinematic actinolite-pyrite selvages overprinting talc-chlorite-phlogopite-dolomite schist. It contains auriferous pyrite (70 ppb Au; 610 ppb Ag) where sampled for zircon U-Pb chronology at +224 m elevation. Above the sample site, the dyke was mined as gold ore (1–6 g/t Au) at +300–350 m. Temperature estimates based on actinolite-albite pairs (300–350 °C) agree with the fluid inclusion trapping temperature of main-stage auriferous veins (330 ± 20 °C). These relationships are interpreted to indicate syn-mineralization emplacement. Gold-related albite-altered porphyry dykes (albitites) also occur in the world-class Hollinger-McIntyre (986 t Au) and Kerr Addison-Chesterville deposits (336 t Au), Abitibi greenstone belt, Canada.

Genesis of langrial iron ore of hazara area, khyber pakhtunkhaw, parkistan

The Iron Ore of Hazara area has been studied at seven locations for detail mineralogical and genesis investigations. Thick bedded iron ore have been observed between Kawagarh Formation and Hangu Formation i.e Cretaceous-Paleocene boundary. At the base of Hangu Formation variable thickness of these lateritic beds spread throughout the Hazara and Kohat-Potwar plateau. This hematite ore exists in the form of unconformity. X-Ray Diffraction technique (XRD), X-ray Fluorescence Spectrometry (XRF), detailed petroghraphic study and Scanning Electron Microscope (SEM) techniques indicated that iron bearing minerals are hematite, chamosite and quartz, albite, clinochlore, illite-montmorillonite, kaolinite, calcite, dolomite and ankerite are the impurities present in these beds. The X-ray Fluorescence (XRF) results show that the total Fe2O3 ranges from 39 to 56% and it has high silica and alumina ratio is less than one. Beneficiation requires for significant increase in ore grade. The petroghraphic study revealed the presence of ooids fragments as nuclei of other ooids with limited clastic supply which indicate high energy shallow marine depositional setting under warm and humid climate. The overall results show that Langrial Iron ore is a low-grade iron ore and can be upgraded up to 62% by applying modern mining techniques to fulfill steel requirements of the country.

Genesis of Langrial Iron Ore of Hazara area, Khyber Pakhtunkhwa, Pakistan

In this paper, a detailed mineralogical and genesis investigation have been carried out in the seven locations of the Iron Ore in Hazara area. Thick bedded iron ore have been observed between Kawagarh Formation and Hangu Formation i.e, Cretaceous-Paleocene boundary. At the base of Hangu Formation, variable thickness of these lateritic beds spread throughout the Hazara and Kohat-Potwar plateau. This hematite ore exists in the form of unconformity. X-ray diffraction technique (XRD), X-ray fluorescence spectrometry (XRF), detailed petroghraphic study and scanning electron microscope (SEM) techniques indicated that those iron bears minerals including hematite, chamosite and quartz, albite, clinochlore, illite-montmorillonite, kaolinite, calcite, dolomite, whereas ankerite are the impurities present in these beds. The X-ray fluorescence (XRF) results show that the total Fe2O3 ranges from 39 to 56%, with high silica and alumina ratio of less than one. Beneficiation requires for significant increase in ore grade. The petroghraphic study revealed the presence of ooids fragments as nuclei of other ooids with limited clastic supply, which indicate high energy shallow marine depositional setting under warm and humid climate. The overall results show that Langrial Iron Ore is a low-grade iron ore which can be upgraded up to 62% by applying modern mining techniques so as to fulfill steel requirements of the country.

The Mechanisms Forming the Five–Floor Zonation of Quartz Veins: A Case Study in the Piaotang Tungsten–Tin Deposit, Southern China

It is common among many vein–type tungsten deposits in southern China that the thickness of ore veins increases from <1 cm to >1 m with increasing depth. A five–floor zonation model for the vertical trend of vein morphology was proposed in the 1960s and has been widely applied for predicting ore bodies at deeper levels, but the causative mechanisms for such a zonation remain poorly understood. The Piaotang tungsten–tin deposit, one of the birthplaces of the five–floor zonation model, is chosen as a case study for deciphering the mechanisms forming its morphological zonation of quartz veins. The vertical trend of vein morphology and its link to the W–Sn mineralization in Piaotang was quantified by statistical distributions (Weibull distribution and power law distribution) of vein thickness and ore grade data (WO3 and Sn) from the levels of 676 m to 328 m. Then, the micro–scale growth history of quartz veins was reconstructed by scanning electron microscope–cathodoluminescence (SEM–CL) imaging and in situ trace element analysis. The Weibull modulus α of vein thickness increases with increasing depth, and the fractal dimensions of both vein thickness and ore grade data (WO3 and Sn) decrease with increasing depth. Their vertical changes indicate that the fractures that bear the thick veins were well connected, facilitating fluid focusing and mineralization in mechanically stronger host rocks. Three generations (Q1–Q3) of quartz were identified from CL images, and the CL intensity of quartz is possibly controlled by the concentrations of Al and temperature. From the relative abundance of the Q1–Q3 quartz at different levels, the vertical trend of vein morphology in Piaotang was initially produced during the hydrothermal event represented by Q1 and altered by later hydrothermal events represented by Q2 and Q3. Statistical distributions of vein thickness combined with SEM–CL imaging of quartz could be combined to evaluate the mineralization potential at deeper levels.

A framework for near real-time ROM stockpile modelling to improve blending efficiency

Purpose In the mining industry, a run-of-mine (ROM) stockpile is a temporary storage unit, but it is also widely accepted as an effective method to reduce the short-term variations of ore grade. However, tracing ore grade at ROM stockpiles accurately using most current fleet management systems is challenging, due to insufficient information available in real time. This study aims to build a three-dimensional (3D) model for ROM stockpiles continuously based on fine-grained grade information through integrating data from a number of ore grade tracking sources. Design/methodology/approach Following a literature review, a framework for a new stockpile management system is proposed. In this system, near real-time high-resolution 3D ROM stockpile models are created based on dump/load locations measured from global positioning system sensors. Each stockpile model contains a group of layers which are separated by different qualities. Findings Acquiring the geometric shapes of all the layers in a stockpile and cuts made by front wheel loaders provides a better understanding about the quality and quality distribution within a stockpile when it is stacked/reclaimed. Such a ROM stockpile model can provide information on predicating ore blend quality with high accuracy and high efficiency. Furthermore, a 3D stockyard model created based on such ROM stockpile models can help organisations optimise material flow and reduce the cost. Research limitations/implications The modelling algorithm is evaluated using a laboratory scaled stockpile at this stage. The authors expect to scan a real stockpile and create a reference model from it. Meanwhile, the geometric model cannot represent slump or collapse during reclaiming faithfully. Therefore, the model is expected to be reconcile monthly using laser scanning data. Practical implications The proposed model is currently translated to the operations at OZ Minerals. The use of such model will reduce the handling costs and improve the efficiency of existing grade management systems in the mining industry. Originality/value This study provides a solution to build a near real-time high-resolution multi-layered 3D stockpile model through using currently available information and resources. Such novel and low-cost stockpile model will improve the production rates with good output product quality control.

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.