Our Services:

Mineral Targeting

Kenex predictive models have helped target highly prospective ground in many countries including New Zealand, Australia, Papua New Guinea, Oman, Turkey and Finland. We have helped companies such as Auzex Resources, HPD Exploration, and Aurora Minerals raise exploration capital from market based investors. Kenex is able to assist you to acquire highly prospective land before your competitors even know it exists, tell you which data types are the most useful to collect in your exploration area, and help you prioritise work programs based on the prospectivity of your tenements. Kenex can really add value to your exploration by allowing you to focus your money and time on the most prospective land.


The development of a mineral targeting model by Kenex includes the following steps:

  1. Data Compilation: The initial step is deciding on the data that will be required to run the model and getting this data into a digital form. Today much of the data required for mineral exploration is already distributed in a digital GIS format and is available either free-of-charge or through a licence agreement from most geological surveys. Kenex organises the digitising of the remaining data and compiles a coherent digital GIS database that will be used for the spatial modelling. Data sets used in a model might include: rock chip, stream sediment, soil, and drill hole geochemistry, geological mapping (faults, folds, lithology, deformation, veining, age), geophysics, mapped alteration zones, mineral occurrences, and existing mine sites.

  2. Expert Knowledge: No model could be completed without expert subject knowledge provided by leading industry experts. With more than 75 years of combined industry experience in-house and hundreds of contacts, partnerships, and professional associations, Kenex is able to cast a critical eye over all information in the datasets used for the model, determine the importance of key themes, and knows the intricacies of the subject matter. This expert knowledge allows us to develop and use the best and most current mineral deposit models to find new mineral deposits and re-evaluate existing prospects

  3. Spatial Analysis: Themes and datasets are tested using spatial statistics and are converted into the key predictive maps required for the model. This stage is the most time consuming for the modelling geologist as it requires scores of different filtering, analytical, statistical, and data preparation techniques.

  4. Modelling: Kenex uses Arc-SDM on an ESRI GIS platform and MI-SDM on a MapInfo GIS platform to run the models and prepare the predictive maps. As one of the global leaders in prospectivity modelling we maintain close links with the software developers to ensure that we're all constantly developing the modelling techniques and the science behind them. Kenex Managing Director Dr Greg Partington regularly publishes research papers on the subject.

  5. Model Output and Targeting: The modelling process generates a data layer that shows the prospectivity of mineralisation occurring at a given location. This data set can be displayed as a map that highlights the most prospective areas. It could be applied to a set of land parcels showing which are most likely to have the event modelled occurring in it, or used to rank the importance of existing exploration tenements or land use zones.

  6. Reporting / Exploration Advice: Models are output to the clients specifications. These can range from a complete digital dataset in the form of a GIS through to a large colour coded map showing the prediction values. Kenex also includes a report on all our models discussing the results, how they were obtained, and what data was included. A common request from our exploration clients is for expert advice on prioritising their exploration, tenement management and sampling programs based on the modelling results. Prospectivity models are also frequently used to showcase the mineral potential during IPO's, project fundraising, and other market related reporting.

More details about mineral prospectivity modelling >

Key People:

Greg Partington


Katie Peters


Simon Nielsen


Genevieve Luketina


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