Analytical results and associated sample and analysis metadata from the analysis of minerals in earth material samples.

Analytical results and associated sample and analysis metadata from the analysis of minerals in earth material samples.

The Mineral Deposits and Mineral Resources OGC service provides data from Geoscience Australia’s OZMIN database in EarthResourceML 2.0 and ERML Lite 1.0 and associated contextual layers in simple WMS and WFS formats.

The Mineral Deposits and Mineral Resources OGC service provides data from Geoscience Australia’s OZMIN database in EarthResourceML 2.0 and ERML Lite 1.0 and associated contextual layers in simple WMS and WFS formats.

The Mineral Potential web service provides access to digital datasets used in the assessment of mineral potential in Australia. The service includes maps showing the potential for sediment-hosted base metal mineral systems in Australia.

This web service delivers datasets produced by the Critical Minerals Mapping Initiative (CMMI), a collaboration between Geoscience Australia (GA), the Geological Survey of Canada (GSC) and the United States Geological Survey (USGS). Data in this service includes geochemical analyses of over 7000 samples collected from or near mineral deposits from 60 countries, and mineral prospectivity models for clastic-dominated (Zn, Pb) and Mississippi Valley-type (Zn-Pb) deposits across Canada, the United States, and Australia.

The Mineral Potential web service provides access to digital datasets used in the assessment of mineral potential in Australia. The service includes maps showing the potential for sediment-hosted base metal mineral systems in Australia.

The Mineral Potential web service provides access to digital datasets used in the assessment of mineral potential in Australia. The service includes maps showing the potential for sediment-hosted base metal mineral systems in Australia.

The Heavy Mineral Map of Australia (HMMA) project1, part of Geoscience Australia’s Exploring for the Future program, determined the abundance and distribution of heavy minerals (HMs; specific gravity >2.9 g/cm3) in 1315 floodplain sediment samples obtained from Geoscience Australia’s National Geochemical Survey of Australia (NGSA) project2. Archived NGSA samples from floodplain landforms were sub-sampled with the 75-430 µm fraction subjected to dense media separation and automated mineralogy assay using a TESCAN Integrated Mineral Analysis (TIMA) instrument at Curtin University. Interpretation of the massive number of mineral observations generated during the project (~150 million mineral observations; 166 unique mineral species) required the development of a novel workflow to allow end users to discover, visualise and interpret mineral co-occurrence and spatial relationships. Mineral Network Analysis (MNA) has been shown to be a dynamic and quantitative tool capable of revealing and visualizing complex patterns of abundance, diversity and distribution in large mineralogical data sets3. To facilitate the application of MNA for the interpretation of the HMMA dataset and efficient communication of the project results, we have developed a Mineral Network Analysis for Heavy Minerals (MNA4HM) web application utilising the ‘Shiny’ platform and R package. The MNA4HM application is used to reveal (1) the abundance and co-occurrences of heavy minerals, (2) their spatial distributions, and (3) their relations to first-order geological and geomorphological features. The latter include geological provinces, mineral deposits, topography and major river basins. Visualisation of the mineral network guides parsimonious yet meaningful mapping of minerals typomorphic of particular geological environments or mineral systems. The mineralogical dataset can be filtered or styled based on mineral attributes (e.g., simplified mineralogical classes) and properties (e.g., chemical composition). In this talk we will demonstrate an optimised MNA4HM workflow (identification à mapping à interpretation) for exploration targeting selected critical minerals important for the transition to a lower carbon global economy. The MNA4HM application is hosted at https://geoscienceaustralia.shinyapps.io/mna4hm and is available for use by the geological community and general public. This Abstract was submitted and presented to the 2023 Goldschmidt Conference Lyon, France (https://conf.goldschmidt.info/goldschmidt/2023/meetingapp.cgi)

Indicator minerals are minerals that, when appearing as transported grains in sediments, may indicate the presence of mineralisation, hydrothermal alteration or a particular lithology within basinal watersheds[1]. Traditionally the application of indicator minerals in hard rock exploration has involved the use of a shovel, gold pan and hand lens. However, recent technological advancements in automated mineralogy and improvements in microanalytical workflows allow for the rapid identification and characterisation of a large number of indicator minerals across extensive sample suites[2,3]. Mineral network analysis techniques provide a visually intuitive approach to facilitate the rapid interrogation of automated mineralogy datasets[4]. Network analysis is a subfield of graph theory used to visualise complex systems. Mineral datasets and their associated geological systems can be visualised as network graphs comprising nodes (vertices) and edges (lines), where nodes and edges represent geological entities and their relationships, respectively. Visualisation of mineral relationships as 2-D or 3-D networks allows for rapid identification and interpretation of co-occurring mineral assemblages potentially indicative of mineralisation within large mineral datasets. We have developed an online Mineral Network Analysis (MNA) application that provides geoscientists with a toolkit to visualise and explore large mineral datasets, such as the Heavy Mineral Map of Australia project, which uses automated mineralogy techniques to analyse over 1,300 heavy mineral samples from across the continent[5]. We demonstrate the effectiveness of MNA techniques in domain analysis of a smaller dataset (150 samples; >5,710,000 unique mineral particles) generated from the Geological Survey of Western Australia’s geochronology collection. By using the application to rapidly filter samples in which the zinc-bearing minerals gahnite (ZnAl2O4) and faustite ((Zn, Cu)Al6(PO4)4(OH)8.4.5(H2O)) co-occur, we are able to highlight a sample from the Fraser Zone in Western Australia; an area prospective for base metal mineralisation[6]. In doing so, we highlight the potential utilisation of the MNA tool in modern mineral exploration. References [1]McClenaghan, M.B., 2005. DOI 10.1144/1467-7873/03-066 [2]Lougheed, H.D. et al., 2020. DOI 10.3390/min10040310 [3]Porter, J. et al., 2020. DOI 10.1016/j.oregeorev.2020.103406 [4]Morrison, S.M. et al., 2017. DOI 10.2138/am-2017-6104CCBYNCND [5]Caritat, P. et al., 2020. DOI 10.11636/Record.2020.031 [6]Walker, A.T. et al., 2022. DOI 10.1016/j.lithos.2021.106536 Presented at the 2022 Goldschmidt Conference