Improving trace metal characterisation of ore deposits – a crucial step towards sustainable mining

Abstract: Sustainable mining, including the utilisation of an ore body to its full potential, is becoming increasingly important for human society as the demand for metals increases. In order to maximise the recovery of useful metals, detailed characterisation of the ore prior to processing is vital. Characterisation should include major and minor ore minerals, gangue minerals, and also trace metals. Trace metals despite their low abundance are often particularly important, either due to their high economic value and criticality for society, or their negative impact on the quality of the main commodity recovered and/or the environment. To properly characterise trace metals in an ore deposit the use of micro-analytical techniques is necessary. Nowadays, a plethora of techniques exist, each with their own strengths and weaknesses. In the mining industry, automated scanning electron microscopy systems are widely used. These systems allow for rapid mineralogical characterisation and quantification of a sample and are commonly used to quantify the mineralogy of the ore feed and subsequent products. Operators of these systems benefit from prior knowledge of the mineralogy of a sample/deposit to fine-tune their processing software to deliver data of highest quality. In this study, a method to improve trace metal characterisation in ore deposits with automated scanning electron microscopy systems is presented. It is implemented as a case study on the Liikavaara Cu-(W-Au) deposit in northern Sweden. The deposit is enriched in several trace metals including Au, Ag, Bi and Sn, and is planned for production in 2023. The mine will produce Cu as the main product and Au and Ag as by-products, and the processing of the ore will be performed in the nearby Aitik plant. For this study, a detailed geological and mineralogical investigation of the deposit was performed prior to analysis with the automated scanning electron microscopy system. A good understanding of the mineralogy is necessary to be able to select a representative sample for the subsequent automated analysis and to guarantee optimal data quality produced by the automated system, and to judge the performance of the automated system, to improve the method of analysis.Manuscript 1 deals with the geological description and genetic aspects of the Liikavaara ore deposit. Results indicate that Liikavaara is an intrusion-related vein-style deposit. Mineralisation is hosted by quartz-tourmaline and calcite veins in a metadiabase that is partly metamorphosed to biotite schist. A 1.87 Ga granodiorite intrudes the footwall. Aplite dikes, genetically related to the intrusion, crosscut the metadiabase host rock. Mineralised veins are concentrated in and around these dikes.Manuscript 2 deals with method development of automated mineralogical analysis. A sample from a mineralised quartz-tourmaline vein at Liikavaara was analysed in great detail with the QEMSCAN® system. Apart from ore minerals in major and minor abundance the sample also contains ore minerals in trace quantities, e.g. Au and Ag minerals. The sample was analysed using two different analytical settings, at two different laboratories, one typical of a production-focused industrial approach and one quality-focused scientific approach. A first analysis using the industrial approach was unable to detect any Au and Ag minerals in the sample. By modification of the QEMSCAN® mineral reference library, through iterative use of the data from both the industrial- and the scientific approach, detection and quantification of Au and Ag minerals was successful. This method can be implemented as an add-on for routine industrial analysis by automated scanning electron microscopy systems to gain information on trace metal occurrence and distribution. This information can then be used for targeted sample selection for further in-depth analysis of the trace metal content and occurrence in the deposit.