A role for data richness mapping in exploration decision making

Abstract

Exploration programs involve long, multi-branched decision pathways with many potential outcomes from a single starting point. Critical decisions along this pathway include the choice of the area(s) in which to explore, and the choice to acquire new data sets to assist in exploration. Included in this is a factoring of risks, including the technical risks associated with imperfect data and the value relative to cost of collecting more data. In our experience it is not common practice to explicitly assess and quantify the impact of data on exploration decisions. A better understanding of the opportunity provided by data has the potential to improve the quality and/or ease of decision making. In this study we explore the potential role of data richness mapping as part of a decision making workflow. We outline a method to quantitatively assess data richness following a four level hierarchy, namely presence of data (L1), quality of data (L2) and attributes of data (L3). Measures of these are combined across several data sets to give an overall estimate of data richness (L4). Using this data richness map, along with past exploration records and a prospectivity model, we conduct an illustrative case study for gold exploration in the Bryah Sub-basin (Yerrida Basin) region of Western Australia. The first application is to produce a simple data based classification of regions with different technical risk characteristics. The second application is to combine this with a prospectivity model to better understand exploration potential. The third application uses data richness mapping to understand which data sets are most likely to have an impact on regional exploration. In each case we show how an understanding of data richness can help support exploration decision making. Introduction: Modern exploration often demands the integration of concepts that operate across a variety of scales

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