Preprints
https://doi.org/10.5194/essd-2023-464
https://doi.org/10.5194/essd-2023-464
20 Feb 2024
 | 20 Feb 2024
Status: a revised version of this preprint is currently under review for the journal ESSD.

Integration by design: Driving mineral system knowledge using multi modal, collocated, scale-consistent characterization

James Austin, Michael Gazley, Renee Birchall, Ben Patterson, Jessica Stromberg, Morgan Willams, Andreas Björk, Monica Le Gras, Tina Shelton, Courteney Dhnaram, Vladimir Lisitsin, Tobias Schlegel, Helen McFarlane, and John Walshe

Abstract. Recent decades have seen an exponential rise in the application of machine learning in geoscience. Fundamental differences distinguish geoscience data from most other data types. Geoscience datasets are typically multi-dimensional, and contain 1-D (drillholes), 2-D (maps or cross-sections), and 3-D volumetric and point data (models/voxels). Geoscience data quality is a product of its resolution and the precision of the methods used to acquire it. The dimensionality, resolution, and precision of each layer within a geoscience dataset translates to limitations in 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. The nuances and limitations that underpin the reliability of automated interpretation are well understood by geoscientists, but are rarely appropriately transferred to data science. However, 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 must be used that characterise the system quantitatively, using collocated analyses, at a common scale. This paper provides research and exploration insights from an innovative district-wide, scale-integrated, geoscience data project, which analysed 1,590 samples from 23 mineral deposits and prospects across the Cloncurry District, Queensland, Australia. Ten different analytical techniques, including density, magnetic susceptibility, remanent magnetisation, anisotropy of magnetic susceptibility, radiometrics, conductivity, scanning electron microscopy (SEM)-based automated mineralogy, geochemistry, and short-wave infrared (SWIR) hyperspectral data with 561 columns of scale-integrated data (+2151 columns of SWIR). All data were collected on 2 cm x 2.5 cm sample cylinders; a scale at which the confidence in 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 to mineral exploration, such as linking alteration paragenesis with structural controls and petrophysical zonation.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
James Austin, Michael Gazley, Renee Birchall, Ben Patterson, Jessica Stromberg, Morgan Willams, Andreas Björk, Monica Le Gras, Tina Shelton, Courteney Dhnaram, Vladimir Lisitsin, Tobias Schlegel, Helen McFarlane, and John Walshe

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-464', Randolph Enkin, 23 Mar 2024
    • AC1: 'Reply on RC1', James Austin, 28 Mar 2024
  • RC2: 'Comment on essd-2023-464', Hanna Leväniemi, 06 Apr 2024
    • AC2: 'Reply on RC2', James Austin, 13 Jun 2024
  • EC1: 'Comment on essd-2023-464', Kirsten Elger, 31 May 2024
    • AC3: 'Reply on EC1', James Austin, 13 Jun 2024
James Austin, Michael Gazley, Renee Birchall, Ben Patterson, Jessica Stromberg, Morgan Willams, Andreas Björk, Monica Le Gras, Tina Shelton, Courteney Dhnaram, Vladimir Lisitsin, Tobias Schlegel, Helen McFarlane, and John Walshe

Data sets

Cloncurry METAL Database James R. Austin, Michael Gazley, Renee Birchall, Ben Patterson, Jessica Stromberg, Morgan Willams, Andreas Björk, Monica Le Gras, Tina D. Shelton, Courteney Dhnaram, Vladimir Lisitsin, Tobias Schlegel, Helen McFarlane, and John Walshe https://geoscience.data.qld.gov.au/data/dataset/cr126168/resource/geo-doc1310615-cr126168

James Austin, Michael Gazley, Renee Birchall, Ben Patterson, Jessica Stromberg, Morgan Willams, Andreas Björk, Monica Le Gras, Tina Shelton, Courteney Dhnaram, Vladimir Lisitsin, Tobias Schlegel, Helen McFarlane, and John Walshe

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Short summary
Cloncurry METAL aims to shift the “Big Data” paradigm in mineral system science by developing a quantitative, fully integrated, multi-modal, scale-consistent methodology for system characterisation. The data comprises collocated petrophysical-mineralogical-geochemical-structural-metasomatic characterisation of 23 deposits from a highly complex mineral system. This approach allows translation of mineral system processes into physics, providing a framework for smarter geophysics-based exploration.
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