Publications
Kenex co-founder Greg Partington and other members of the Kenex team have kept up an incredible track record of publishing papers in conference proceedings and scientific journals since Kenex was founded. You can read them here.
Resource assessment using GIS modelling of orogenic gold mineralisation potential in New Zealand
Prospectivity modelling of orogenic gold mineralisation has been completed over New Zealand using the GIS based weights of evidence modelling technique. New Zealand orogenic gold deposits are restricted to the South Island and lower North Island and are divided into two groups (Paleozoic and Mesozoic) based on their age and host rock association. Modelling of Paleozoic and Mesozoic orogenic gold deposits was undertaken to illustrate the power of GIS modelling in regional and nationwide resource evaluation and how it can be used to quickly identify and rank in terms of prospectivity areas of land where new orogenic gold deposits might exist. The mineral deposit modelling was constrained by the mineral systems concept which defines those parts of a mineralisation system that are critical to the ore-forming process. Both of the New Zealand gold models 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. The models were validated against known areas of historical gold mining such as the Reefton deposits (Paleozoic) and Macraes Flat (Mesozoic). Two prospectivity maps showing areas favourable for Paleozoic and Mesozoic orogenic gold formation were produced. The prospectivity modelling successfully identified known areas for both types of orogenic gold mineralisation as well as several new localities not currently covered by existing tenements. The spatial modelling techniques used here can be applied elsewhere to evaluate resource potential, whether for gold, or any other land based resource, and can help planners and land owners manage future developments and their assets more effectively. Both models supersede those undertaken in 2002 by Crown Minerals and GNS Science under the purview of Dr Greg Partington (now Director of Kenex Ltd.). The new models were re-run due to the addition of new data and new modelling techniques and appear to have much better definition and are better for targeting at a prospect scale.
Developing Models using GIS to Assess Geological and Economic Risk: An Example from Mineral Exploration in Oman for VMS Copper Gold Mineralisation
It is important to understand the financial risk involved in any business venture and recent economic conditions make this even more critical. There are a variety of tools and techniques that when used with modern GIS and the mineral system concept allow sophisticated economic risk analysis to be carried out, including assessing uncertainty. A weights of evidence model for VMS copper-gold mineralisation was created for the northern part of the Semail Ophiolite Belt in Oman and this has been used in conjunction with economic modelling to target, prioritise and plan follow-up exploration. Individual predictor themes of geology, geochemistry and geophysical data were combined into a single predictive map for VMS copper-gold mineralisation. The immediate benefits of carrying out this type of analysis include effective data compilation, quality control of digital data, understanding of critical geological factors to be used in follow-up exploration, ranking of prospects, prioritising exploration, exploration budgeting and management, understanding of risk and cost reduction. The prospectivity model identified 79 targets above an upper threshold in the study area. Nine of the targets are known historic mines or current operations, 11 of the targets are known undeveloped prospects and 59 of the targets are new unexplored prospects. The prospectivity model was not only used to target, but also used to plan new exploration programs to collect missing data that could add the most value to developing the target. Economic factors were developed for each of the targets identified by the modelling to allow a more complete understanding of the exploration risk. This allows targets with differing geology, amounts of metal and economic factors to be compared, ranked and prioritised. An exploration risk value was calculated by combining the geological probability values with the economic parameters so that positive exploration risk values were considered to be potential investment targets whereas targets with negative risk values were considered being more of a gamble. There are twenty-six targets in the study area with positive exploration risk values, which not only confirm the study areas' prospectivity, but also economic potential. The work in Oman confirms the potential for new discoveries in the region, which even at low copper prices still make attractive exploration targets.
Predictive modelling for environmental management and mineral exploration – potential applications for the marine minerals industry
Deep sea mineral exploration has progressed significantly in the past few years, however it remains a nascent industry when compared to terrestrial mineral exploration and mining and the offshore petroleum industry. Given that in general marine exploration is more costly than terrestrial exploration the ability to focus exploration efforts and funds should be highly desirable to those companies involved. Similarly, the detailed understanding and distribution of species or habitats in the marine environment in many areas which coincide with prospective minerals deposits is often limited. Predictive modelling could therefore be a valuable tool for aiding the management of both facets of a marine minerals project. Although a GIS is a perfect way of visualising data and producing maps from that data, GIS also allows you to create new data through using statistically based gridding techniques or predictive maps using spatial data modelling techniques. This modelling is where businesses can really add value, using their data more effectively rather than just passively using it to generate maps and figures. Basic statistical gridding allows you to predict unknown values from within a single layer such as topography, bathymetry, geochemistry, vegetation, hydrology, water temperature or climate data. However the real power of GIS is when spatial modelling is applied to combine several layers to predict outcomes based on probability such as mineral prospectivity, agricultural sustainability, geotechnical risk, environmental risk, and onservation planning. Adapting the technique for locating or ranking prospective seafloor massive sulphide or manganese nodule targets or for aiding baseline and detailed environmental planning are some of the possible applications for predictive modelling for the marine minerals industry.
Exploration targeting using GIS: more than a digital light table
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.
Resource assessment using GIS modelling of orogenic gold mineralisation and wind energy potential in Wellington, New Zealand
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.