Optical and SEM-Based Microscopy Integration for Optimisation of Geometallurgical Modelling and Ore Deposit Characterisation - R Hartner (The Australasian Institute of Mining and Metallurgy, 2011)
Digital optical microscopy (DOM) and automated SEM-based (ASEM) mineralogy systems (MLA, QEMSCAN) have experienced significant developments within the last decade. However these developments have been independent from each other and the two mineralogical techniques have so far not yet been integrated to combine the strengths and technical benefits of both analytical platforms. This detailed comprehensive mineralogical information is critical support for geometallurgy.Major hardware and software advances in DOM in the last few years have provided important new capabilities with potential applications to automated mineralogy. These technological advances have been largely driven by sectors outside mining (eg medical pathology) and have not yet been widely adopted within the minerals industry. The advent of DOM offers significantly more automated mineralogy capabilities than traditional expert-mineralogist driven optical microscopy. This is based on advances in automated image acquisition, high resolution cameras for digital imaging, imaging of large areas through mosaic options, integration of multiple layers and application of advanced image analysis techniques.Ongoing research involves combining the outputs of DOM and ASEM-based microscopy to create new capabilities for integrated microscopy based on development of advanced cross-platform image fusion and data integration between DOM and ASEM (exploiting the benefits of both analytical platforms). This requires non-linear image registration and transfer of mineralogical identification from ASEM to DOM systems using sophisticated image manipulation and data analysis software.Examples will be given of image fusion and data registration for a range of different ore types. Image fusion techniques are demonstrated using a porphyry copper deposit sample where sulfides and precious metals are classified using the MLA and gangue mineralogy obtained from DOM images. Data integration enables creation of a library containing optical property variability information for minerals identified by the MLA; thus reducing the reliance on skilled mineral identification by supplementing human interpretation