The Kamchatka–Kuril island arc forms the northeastern margin of the Eurasian continent, including the Kuril Islands, Kamchatka Peninsula and parts of the Koryak Highlands. This composite island arc, over 2000 km long, contains abundant epithermal gold deposits with total resources currently assessed at approximately 30 Moz of gold. Epithermal gold deposits of the Kamchatka–Kuril arc cluster in several gold districts associated with Neogene volcanism, Koryak, Northern Kamchatka, Central Kamchatka, Kumroch and Southern Kamchatka (Vilyuchinsky), and there are also smaller deposits in the Kuril Islands.The Vilyuchinsky district comprises mainly intermediate and felsic volcanic rocks of Miocene and Pliocene age overlying a sedimentary flysch sequence, and locally overlain by Quaternary basalts. Shallow subvolcanic intrusions represent the roots of Neogene palaeovolcanic structures that were exposed by erosion. Epithermal gold deposits in the Vilyuchinsky district are of the adularia-sericite type and form north west trending linear arrays that are spatially associated with the Vilyuchinsky crustal fault that bounds the district to the North. Mineralisation is spatially associated with, and is commonly hosted by, subvolcanic intrusions of dacite and andesitic-dacite exposed in the deeply eroded palaeovolcanic centres.The Vilyuchinsky district was mapped and explored at a scale of 1 : 50 000. All creeks were systematically sampled at a density of approximately one sample per 500 m along the stream infilled to 1 : 25 000 in geologically favourable areas. Analysis of stream sediment assays indicates that gold has a multi-modal statistical distribution, suggesting the presence of different levels of geochemical anomalies. Gold assays ≥ 20 ppb are classified as anomalous, and values ≥ 100 ppb are classified as highly anomalous. In the Kamchatka region, deposits containing more than 1 Moz of gold coincide with stream sediments that contain ≥ 100 ppb of gold. In the Vilyuchinsky district, such samples are found only around the Porozhistoe deposit. Copper and lead values in stream sediments are classified as anomalous at ≥ 100 ppb and ≥ 30 ppb, respectively.Base metals and gold are commonly distributed in three separate zones surrounding diorite porphyry centres (e.g. at the Balaganchik prospect). Copper anomalies occur in the vicinity of diorite porphyry intrusions, lead is present 2–3 km from the centres, forming an intermediate geochemical halo, and gold veins and related stream sediment anomalies form an outer halo most distant, commonly >5 km from the diorite centres. The zoned nature of the stream sediment anomalies can be used as a geochemical vector to facilitate exploration.

The Jordanian Uranium Mining Company (JUMCO) needed to determine the most appropriate method for sampling and assaying the surficial uranium deposits of central Jordan. This type of mineralisation presents a number of challenges including; i. occurrence of best mineralisation within 5 m of surface, ii. observed secular isotopic disequilibrium, iii. erratic distribution of uranium mineralisation, iv. problems with drill sample recovery close to surface, v. difficulty in reliably logging geology in percussion drill chip samples. Previous exploration drilling programs that relied on gamma logging were shown to be unreliable because of secular disequilibrium close to surface. The percussion chip samples used for chemical assaying were shown to be unreliable because of poor sample recoveries and the possible loss of fine-grained uranium mineralisation. In an effort to maximise sample recovery, avoid disequilibrium problems and assess the short-scale continuity of mineralisation, trenching was chosen as the preferred exploration method to evaluate the upper part of the surficial uranium deposits of central Jordan.

Structure is the principal control on gold mineralisation, and the paradigms reflect the punctuated evolution of our understanding of that control. Since the 1950s, structure paradigms for gold control have been a mechanism for gaining research funding, building a public front, enabling publication, communication between government and academia, and building interaction between research groups and industry. Critically, the authors ask if the paradigms have helped us find and mine gold? The 10 paradigms discussed here are: (1) syngenetic (1950s–1980s), (2) late compression (1980s–1990s), (3) brittle strike-slip (1980s), (4) complexity (mid-1980s to present), (5) stress mapping (1990s), (6) earthquake paradigm (1990s–2000s), (7) detail and micro-detail in texturally chronological sequence (2000s), and (8) conglomerate-related (1990s–2000s). The most recent, innovative initiatives are more fluid- than structure-centric, such as the rapid energy transfer of the (9) lightning paradigm, and (10) the non-linear/disequilibrium paradigm. In this paper, the authors overview these paradigms, the critical geology that supports the paradigm and practical exploration implications. Authors confront each paradigm's premise and results with empirical facts in gold exploration. Our study indicates that the structural paradigms are more descriptive than predictive, and consequently few if any have genuinely found gold. However, many paradigms encourage structural geology and mostly produce quality science an essential component of all mineral exploration. Finally, the authors recommended a simple, critical, and knowledge-based modus operandi. In order to be successful in finding more gold, the authors emphasise the need for more, not less, structural geology and this should be early in the exploration program. An interpreted 3D image of the structural geology and mineralisation (not necessarily computer-generated, but based on high-quality information) is a critical tool from the earliest stages of exploration and mining for gold.

This study considers realistic data for a planned open-pit iron ore mine, but is applicable to any open pit situation. By interpolating drill hole data, grades are generated for a rectangular block model. Each block’s grade vector has components for each analyte (chemical element or compound) influencing ore value. Deriving the average (E-type) over many simulations leads to each block being assigned its expected value, and thus underestimates the overall grade variability. Alternatively, interpolation by means of conditional simulation is a method that implements random sampling from an infinite population of solutions. Each conditional simulation has appropriate overall grade variability, but estimating any block’s mean and variance requires sampling from multiple conditional simulations. For a block model, an ore/waste selection criterion maximises the expected tonnage at a target grade. This criterion is a linear composite of the grade components, with positive coefficients for the beneficial analyte (Fe) and negative coefficients for the deleterious analytes (such as SiO2, Al2O3 and P). Although conditional simulation often leads to a similar expected grade as kriging for each block, the expected maximum tonnage of ore selectable at a target grade may differ from that obtainable from the E-type solution. We apply the linear composite selection criterion to each of 25 conditional simulations, as well as to the E-type block model. Simulation confirms the distribution of product tonnage to have an expected tonnage that is over 20% greater than that of the E-type model. The method also enables a selection probability to be computed for each block, and thus a probabilistic pit boundary distribution to be identified and used in mine planning. Proposed extensions to this method will consider risk-based scheduling of the multiple selection solutions through minimisation of a derived stress factor and treating the mining process as an iterative system with actual or artificial depletions modelled in line with the mine plan, using the updated state (with new information) to re-evaluate the mine plan for subsequent periods.

In order to develop downstream processing routines for iron ore and to understand the behaviour of the ore during processing, extensive mineralogical characterisation is required. Microscopic analysis of polished sections is effective to determine mineral associations, mineral liberation and grain size distribution. There are two main imaging techniques used for the characterisation of iron ore, i.e. optical image analysis (OIA) and scanning electron microscopy (SEM). In this article, a QEMSCAN system is used as an example of SEM methodology and results obtained from it are compared against results obtained by the CSIRO Recognition3/Mineral3 OIA system. Both OIA and SEM systems have advantages and drawbacks. Even though the latest SEM systems can distinguish between major iron oxides and oxyhydroxides, it is still problematic for SEM systems to distinguish between iron ore minerals very close in oxygen content, e.g. hematite and hydrohematite, or between different types of goethite. Scanning electron microscopy systems also can misidentify minerals with close chemical composition, i.e. hematite as magnetite and vitreous goethite as hematite. In OIA, iron minerals with slight differences in their oxidation or hydration state are more easily and directly recognisable by correlation with their reflectivity. In both methods, the presence of microporosity can result in some misidentification, but in SEM methods misidentifications due to microporosity can be critical. Low resolution during QEMSCAN analysis can significantly affect the textural classification of particle sections. The main conclusion of this study is that, for low iron content ores or tailings, SEM systems can provide much more detailed information on the gangue minerals than OIA. However, for routine characterisation of iron ores with high iron content and containing a variety of iron oxides and oxyhydroxides, OIA is a faster, more cost effective and more reliable method of iron ore characterisation. A combined approach using both techniques will provide the most detailed understanding of iron ore samples being characterised.

s from the 37th Annual Winter Meeting of the Geological Society’s Mineral Deposits Studies Group held on the 6–7th January 2014 at the University of Oxford, UK. The meeting was hosted by the Department of Earth Sciences and Keble College. Sessions included: critical metal supply – a global perspective; exploration in difficult times; the mineral industry – challenges in the 21st century; Ore deposit research in progress; and Metallogeny of new jurisdictions.

Rock density information is needed for the estimation of ore tonnage, and also for determining the amount of contained metal; this is because mineralisation grades are usually reported as a ratio of the economically viable components, metals or minerals, to weight units (e.g. grams/tonne). Despite the obvious importance of rock density for the accurate estimation of tonnage and grade of mineral resources, this parameter is often overlooked and receives significantly less attention than assayed metal grades. In particular, the quantity and spatial distribution of the rock density measurements may be chosen without considering the impact of the rock density measurements on the mineral resource estimation. This paper attempts to address several issues associated with current practices of rock density modelling. The first part of the paper overviews the methods most commonly used for measuring the dry bulk density (DBD) of rocks for the estimation of mineral resources. This is followed by a second part proposing the methodology of geostatistical modelling of the rock densities which is suggested as a mathematically supported approach for choosing optimal sampling grids for density samples. It is supported by several case studies of the deposits where geostatistically optimal DBD sampling grids were estimated and proposed for definition of the Measured and Indicated resources and for the grade control purpose. It was noted that the exploration team needs to assure that DBD data collected during the drilling campaigns are not only sufficient for definition of resources and reserves but are also closely enough spaced to allow accurate grade control. The optimal for grade control DBD grids, according to the geostatistical analysis, is similar to the chemical assay grids required for definition of the Measured resources or, less commonly, Indicated resources of the studied deposits.