Cell Based Associations: A procedure for considering scarce and mixed mineral occurrences in predictive mapping. (May 2015) - Tourlière, Bruno
Abstract: Cell Based Association is an innovative mineral favorability procedure designed to answer special needs of the mining industry in data wise critical situations where usual favorability methods may not yield satisfactory results. Those situations relate to input data quality (e.g. clustered points, mixed and scarce data, approximate location) or some assumptions that are considered unreasonable (e.g. map areas relevance, conditional independence). The principle of CBA consists in replacing polygons of geological units with a square cell grid (hence the 'cell-based'). Each cell contains a range of units ('association') that are binary coded in terms of their presence (1) or absence (0) within study area. The loss of resolution inherent to this procedure is compensated by the enriched information contained in each cell owing to the notion of (lithological) association. Lithological associations are considered as binary spectra and as such are classified using Ascendant Hierarchical Clustering (AHC) thus obtaining a synthetic map of lithological associations. The prospectivity map shows as favourable the cells of the same AHC classes that the ones including mineral occurrences. It was observed that CBA can distinguish between different ore deposit varieties from a blended mineral occurrences data set. CBA can theoretically include any spatialized data (e.g. geophysics, structural data) as an extra variable to specify classification and narrow favourable areas. Doing so would make it an independent favorability mapping procedure and is still under development. Cell size in a grid is a critical parameter of the procedure; it must be compatible with the looked-for phenomena and should have a sufficient lithological variability. In addition to its use for producing favorability maps, a CBA-derived map could help in understanding the background information contained in geological maps. CBA can also be applied to other fields, such as agriculture and urban planning. Highlights: CBA, a procedure to address the problem of scarce data (MO) in predictivity mapping. Map gridding and binary coding of the lithologies. Hybrid classification of the associations contained in the cells. Predictivity mapping displays classes that include MO. Predictivity mapping becomes more resilient to map and MO data quality.