Introduction
Although there is currently a global mining boom, most of the commodities produced are from mining of mineral deposits discovered more than a decade ago. The preceding hiatus in global demand for minerals caused reduced exploration budgets, with an emphasis on district-scale to near-mine (brownfields) exploration rather than the province-scale (greenfields) exploration required for ground acquisition as the first stage of the exploration-to-mining pipelines of the major mining companies. The exploration maturity of known mineral provinces progressively increases as exploration proceeds and the more obvious exposed ore deposits, or those with an obvious geochemical and/or geophysical signature, are discovered. The mineral explorer basically has two choices: (i) to explore relatively immature terranes using proven methodologies which have been successful in mineral provinces that are now mature; or (ii) to explore the more mature provinces using a more conceptual approach to targeting and, at the same time, attempt to improve existing direct-detection methodologies or design new ones with greater sensitivity and/or depth penetration. The first option commonly involves greater risk, as the terranes are often immature due to factors such as political instability, uncertain mining legislation or their wilderness value. Thus, it is important to develop better conceptual targeting strategies, so that greenfields exploration can proceed effectively in relatively mature, low political-risk mineral provinces to define the brownfields exploration districts and world-class mineral deposits of the future.
In this thematic issue, Hronsky & Groves in their paper Science of targeting: definition, strategies, targeting and performance measurement provide a succinct summary of the philosophy of exploration and strategies for conceptual targeting. Conceptual targeting requires the scientific assessment of all available relevant data on the terranes to which it is to be applied. Most world-class to giant ore deposits form via the effective conjunction of efficient ore-forming processes, in suitable host-rocks and/or host environments, under optimum physico-chemical conditions, in high-energy systems, and in specific tectonic, structural and/or magmatic environments. Thus, conceptual targeting models require the integration of a wide variety of parameters, or their proxies, derived from an extensive range of geoscientific datasets. Fortunately, there are now extensive primary digital databases available from industry, government and academic sources from which the relevant parameters or their proxies can be derived. As a multicomponent spatial analysis of the parameters is required, conceptual targeting, or prospectivity (endowment) analysis, is normally carried out using Geographic Information Systems (GIS) linked to relevant database systems. Most of the papers that follow Hronsky & Groves illustrate this type of scientific approach to the assessment of the prospectivity of relatively mature, data-rich terranes or to conceptual targeting within them.
Ford & Blenkinsop's Evaluating geological complexity and complexity gradients as controls on copper mineralisation, Mt Isa Inlier follows up previous suggestions that fractal analysis is useful to define geological complexity and complexity gradients which can delineate the loci of circulating mineralising hydrothermal fluids and hence depositional sites of ore deposits. They apply this approach to copper mineralisation in the Mt Isa Inlier, Queensland. Nykanen, Groves, Ojala, Eilu & Gardoll's Reconnaissance-scale conceptual fuzzy-logic prospectivity modelling for iron oxide copper – gold deposits in the northern Fennoscandian Shield, Finland, discusses a region where several new discoveries (or rediscoveries) have been made in, and around, the districts defined by their analysis.
The following three papers deal with GIS-based prospectivity analysis for orogenic-gold deposits. Nykanen, Groves, Ojala & Gardoll's paper Combined conceptual/empirical prospectivity mapping for orogenic gold in the northern Fennoscandian Shield, Finland involves combined geological, geophysical and geochemical databases to assess prospectivity using a fuzzy-logic approach. Bierlein, Northover, Groves, Goldfarb & Marsh in their paper Controls on mineralisation in the Sierra Foothills gold province, central California, USA: a GIS-based reconnaissance prospectivity analysis then describe a regional-scale GIS analysis in a first attempt to define prospective districts for world-class deposits. In a somewhat different approach, Bierlein, Fraser, Brown & Lees' paper Advanced methodologies for the analysis of databases of mineral deposits and major faults discusses prospectivity analysis of mineral deposits and major faults (largely for orogenic-gold deposits) using database interrogation and pattern recognition software derived from the biological sciences.
The final paper Predictive targeting in Australian orogenic-gold systems at the deposit to district scale using numerical modelling by Potma, Roberts, Schaubs, Sheldon, Zhang, Hobbs & Ord describes modern computational simulations of hydrothermal fluid flow as a sophisticated methodology for predictive targeting in data-rich mineral districts. They provide two examples of this modelling methodology as applied to orogenic-gold systems in Victoria and Western Australia.
It is hoped that these examples of predictive exploration targeting in a digital environment stimulate the use of these methodologies in greenfields to brownfields exploration worldwide. It is only by definition of the most prospective terranes at the greenfields scale that the mineral industry will be able to continue to discover new mineral deposits and meet the expanding market for those mineral commodities that fuel the booming global economy.