Presentations

The use of computers in mineral exploration in the last twenty years has dramatically changed the way we carry out exploration targeting (e.g. Bonham-Carter, 1994; Bonham-Carter et al., 1988; Mihalasky, 2001; Rattenbury and Partington, 2003; Partington and Sale 2004; Partington 2009; Carranza, 2009). This is especially true in the last five years where computer and GPS technology has developed to the stage where it is possible to digitally locate, accurately store, visualise and manipulate geological data at the scale of a mineral system. These tasks are commonly carried out using a Geographic Information System (GIS), which has become as an important tool to a geologist as his hammer. The aim of this paper is to provide a brief review of the techniques available to explorers using GIS and discuss the advantages and problems associated with using GIS techniques for exploration targeting.

Prospectivity modelling of orogenic gold mineralisation and ideal locations for wind farm development has been completed over southwest Wellington in New Zealand. This modelling used a combination of the GIS based weights of evidence and fuzzy logic techniques. These models were undertaken to illustrate the power of GIS modelling in regional resource evaluation and how they can be used to quickly identify areas of land which should be considered for wind farm development or those where new gold deposits might exist. The mineral deposit modelling was constrained by the minerals systems concept which defines those parts of a mineralisation system that are critical to the ore-forming process. The Wellington gold model identified possible sources of metals in the region, structures that could be used for fluid migration, mineral trap zones ideally suited to host a mineral deposit, and outflow zones that may indicate a subsurface deposit. Similarly, the Wellington wind farm model identified ideal sites to develop a wind farm using elements critical for successful turbine placement such as wind speed, terrain, sources of air turbulence, access and land use. The models were validated against known areas of historical gold mining such as at Terawhiti and the turbine locations of Meridian Energy's new West Wind development. The modelling clearly shows that the resource potential in southwest Wellington is greater for wind energy especially after consideration of potential archaeological and environmental restrictions which may rule out key areas of possible orogenic gold mineralisation identified by the model. The spatial modelling techniques used here can be applied elsewhere in New Zealand to evaluate resource potential, whether for wind, gold, or any other land based resource, and can help planners and land owners manage future developments and their assets more effectively.

A current popular model for the formation of IOCG deposits in the Mt Isa Eastern Succession involves fluids derived from the late orogenic granites mixing with a second external fluid source forming Fe- (commonly magnetite-) rich alteration zones that contain vein stockwork, breccia, dissemination or replacement style mineralization. This is assumed to be commonly spatially and temporally associated with felsic pluton emplacement and cooling around 1540-1500 Ma. This contrasts with an alternative model in which the fluids are entirely intra-basinal and amagmatic in origin. Recent dating studies at Osborne have highlighted a potential syn-peak metamorphic timing to mineralization (based on 1595 Ma Re-Os age dates on molybdenite and a 1595 ± 6 Ma U-Pb age date on hydrothermal titanite), with no apparent proximal major intrusion. There is also a potential link between mineralization and widespread mafic intrusive activity, which spans the entire range of known mineralization ages.
In order to investigate this considerable range of potential geological controls on IOCG mineralization a prospectivity analysis was undertaken, aimed at evaluating the relative importance of a range of spatial variables including: host rock type, proximity to felsic granites or mafic intrusives, stream geochemistry (Cu and Au), structure, and geophysics (including magnetics, gravity and wavelet-processed potential field data or “worms”). A data driven approach was taken in view of the considerable uncertainty in genetic models for IOCG deposits.
Important data sources include (1) the northwest Queensland Mineral Province Report, (2) mineral occurrence data and newly available open file geochemistry (Terra Search) available from the Queensland Department of Mines and Energy, (3) regional magnetics and gravity digital datasets available from Geoscience Australia. MapInfo spatial data modeling software (MI-SDM) was utilized in this study. The initial study area comprised six 1:100,000 sheets covering Cloncurry and the area to the south. A conventional weights of evidence analysis was undertaken.
A comparison of Contrast and Student C values for all evidential layers indicates the host lithology as the most important criterion, followed by geochemistry (Cu and then Au), structure, geophysics, felsic and mafic igneous intrusions. The results enable a list of target criteria to be statistically ranked. A comparison of these results can be made with expert driven predictions. The study area is being expanded to include the entire Eastern Succession, including solid geology maps interpreted through cover.
An important outcome for ore genetic models is the recognition that intersections of N to NW structures with other faults have the strongest spatial association with IOCG deposits after host rock and geochemistry. This result implies that fluid pathways are much more important than fluid sources for controlling the distribution of IOCG deposits. This understanding can possibly explain some of the diversity in the range of IOCG deposit types and models. A common mineralizing process could generate deposits in a variety of host rocks depending on the fluid pathways. The dominance of the fluid pathways means that fluid sources cannot be clearly recognized from spatial associations of the deposits alone, and mineralizing fluids may be complex and heterogeneous in view of their possible interactions with a variety of wall rocks. A detailed understanding of fluid pathways and structures at all scales is the most important direction for future research. Mechanical modeling directed at understanding fluid flow in the Mt Isa Eastern Succession based on this structural knowledge will also be an important tool.

The Epithermal and Mesothermal Gold Prospectivity modeling projects carried out by Crown Minerals provided explorers in New Zealand with a new compilation of historical exploration data combined with new geological information from the GNS QMap 1:250,000 scale mapping project. These data were used to produce predictive mineral potential maps for gold mineralisation in New Zealand.
The aim of these projects, to stimulate mineral exploration and investment in exploration, has been successful with eight new companies acquiring new tenement positions and committing significant exploration expenditure to exploring in New Zealand in the coming years. The projects were done at a national scale and consequently not all exploration data were compiled into the prospectivity models. Several of the new companies recognised the value of the prospectivity modeling work and committed exploration funds to continue the modeling process. They recognised a need to compile the remaining data and run the models again to allow detailed exploration targeting.
Detailed data compilations including digitising historic exploration stream sediment sample, rock chip sample, soil sample and drilling data have been completed. New models have been completed in Otago for mesothermal gold mineralisation and in the Coromandel and Northland for epithermal gold. The new models have been compared with the original regional scale models and used to target prospect scale exploration.
This work has allowed exploration models for epithermal and mesothermal mineralisation in New Zealand to be refined. More importantly this work has identified significant areas with potential to host gold mineralisation with little or no systematic geochemical data including soil sampling or drilling. Exploration work programs have been designed to acquire these missing data and exploration funds have now been committed to test the areas highlighted by the prospectivity modeling.
In summary, the Epithermal and Mesothermal Gold Prospectivity modeling work has successfully attracted new investment and ideas to the exploration scene in New Zealand. The projects had an estimated cost of NA$250,000 and will in the next two years, just through exploration expenditure, attract more than NZ$10 M in investment. If a mine is discovered the return on investment will be considerably greater.