An improved method for assessing the degree of geochemical similarity (DOGS2) between samples from multi-element geochemical datasets

The multi-element aqua regia National Geochemical Survey of Australia (NGSA) database is used to demonstrate an improved method for quantifying the degree of geochemical similarity (DOGS2) between soil samples. The improvements introduced here address issues relating to compositional data (closure, relative scale). After removing the elements with excessive censored (below detection) values, the rank-based Spearman correlation coefficient (rs) between samples is calculated for the remaining 51 elements. Each element is given equal weight through the rank-based correlation. The rs values for pairs of samples of known similar origin (e.g. granitoid-derived) are significantly positive, whereas they are significantly negative for pairs of samples of known dissimilar origin (e.g. granitoid- v. greenstone-derived). Maps of rs for all samples in the database against various reference samples are used to obtain correlation maps for lithological derivations. Likewise, the distribution of soils having a geochemical fingerprint similar to established mineralized provinces can be mapped, providing a simple, first order mineral prospectivity tool. Sensitivity of results to the removal of up to a dozen elements from the correlation indicates the method to be extremely robust. The new method is compliant with contemporary compositional data analysis principles and is applicable to various digestion methods.The NGSA project was part of the Australian Government’s Onshore Energy Security Program 2006–2011, from which funding support is gratefully acknowledged

MINERAL RESOURCE ASSESSMENT OF BATTERY CRITICAL ELEMENTS IN A COPPER PORPHYRY DEPOSIT

Elements for the construction of batteries are currently experiencing growing demand due to the need to create lithium-ion batteries in electronics, electric car engines and other vehicles as well as energy distribution and storage systems against the background of the green development of the economies of countries. The increased interest for these elements causes a risk of supply in recent years, which calls these elements critical. The combined use of continuous and categorical variables will reveal some patterns of the distribution of elements, as well as to improve the allocation of hidden geological domains. Therefore, the mineral resource assessment and modeling of orebody is containing these elements, with the introduction of categorical variables such as rock type, alteration and mineralization zone into the algorithm is important research not only for the mining industry, but also for the development of global green technologies. This paper is devoted to the identification of an algorithm based on machine learning and geostatistics for assessing mineral resources of the abovementioned critical battery elements (i.e., Co, Cu, Li, Mo, Ni) over real copper-porphyry deposit, where their local and spatial distributions are being adjusted by geological properties such as mineralization, rock types, and alterations. The results of this study can show whether copper-porphyry deposits are suitable as the main source of critical elements. Moreover, the outcome of comparing several techniques for domaining and simulation steps of the algorithm will make it possible to identify the most suitable one, which will be also the criterion for results analysis