Integration by design: driving mineral system knowledge using multi-modal, collocated, scale-consistent characterisation

Abstract. Recent decades have seen an exponential rise in the application of machine learning in geoscience. However, fundamental differences distinguish geoscience data from most other data types. Geoscience datasets are typically multi-dimensional, and contain 1D (drill holes), 2D (maps or cross-sections), and 3D volumetric and point data (models/voxels). Geoscience data quality is a product of the data's resolution and the precision of the methods used to acquire them. The dimensionality, resolution, and precision of each layer within a geoscience dataset translate into limitations to the spatiality, scale, and uncertainty of resulting interpretations. Historically, geoscience datasets were overlaid cartographically to incorporate subjective, experience-driven knowledge and variances in scale and resolution. These nuances and limitations that underpin the reliability of automated interpretation are well understood by geoscientists but are rarely appropriately transferred to data science. For true integration of geoscience data, such issues cannot be overlooked without consequence. To apply data analytics to complex geoscience data (e.g. hydrothermal mineral systems) effectively, methodologies that characterise the system quantitatively at a common scale, using collocated analyses, should be sought. This paper provides research and exploration insights from an innovative district-wide, scale-integrated geoscience data project, which analysed 1590 samples from 23 mineral deposits and prospects across the Cloncurry district, Queensland, Australia. Nine different analytical techniques were used, including density, magnetic susceptibility, remanent magnetisation, anisotropy of magnetic susceptibility, radiometrics, conductivity, automated mineralogy based on scanning electron microscopy (SEM), geochemistry, and short-wave infrared (SWIR) hyperspectral data with 561 columns of scale-integrated data (+2151 columns of SWIR data). All data were collected on 2.2 cm × 2.5 cm sample cylinders, a scale at which the confidence in the coupling of data from techniques can be high. These data are integrated by design to eliminate the need to downscale coarser measurements via assumptions, inferences, inversions, and interpolations. This scale-consistent approach is critical to the quantitative characterisation of mineral systems and has numerous applications in mineral exploration, such as linking alteration paragenesis with structural controls and petrophysical zonation. The Cloncurry METAL dataset is made freely available via the AuScope Data Repository: https://doi.org/10.60623/82trleue (Austin et al., 2024).