<div>Australia's Identified Mineral Resources is an annual national assessment that takes a long-term view of Australian mineral resources likely to be available for mining. The assessment also includes evaluations of long-term trends in mineral resources, world rankings, summaries of significant exploration results and brief reviews of mining industry developments.</div>

The Australian Resource Reviews are periodic national assessments of individual mineral commodities. The reviews include evaluations of short-term and long-term trends for each mineral resource, world rankings, production data, significant exploration results and an overview of mining industry developments.

<div>This guide and template details data requirements for submission of mineral deposit geochemical data to the Critical Minerals in Ores (CMiO) database, hosted by Geoscience Australia, in partnership with the United States Geological Survey and the Geological Survey of Canada. The CMiO database is designed to capture multielement geochemical data from a wide variety of critical mineral-bearing deposits around the world. Samples included within this database must be well-characterized and come from localities that have been sufficiently studied to have a reasonable constraint on their deposit type and environment of formation. As such, only samples analysed by modern geochemical methods, and with certain minimum metadata attribution, can be accepted. Data that is submitted to the CMiO database will also be published via the Geoscience Australia Portal (portal.ga.gov.au) and Critical Minerals Mapping Initiative Portal (https://portal.ga.gov.au/persona/cmmi).&nbsp;</div><div><br></div>

<div>The production of rare earth elements is critical for the transition to a low carbon economy. Carbonatites (&gt;50% carbonate minerals) are one of the most significant sources of rare earth elements (REEs), both domestically within Australia, as well as globally. Given the strategic importance of critical minerals, including REEs, for the Australian national economy, a mineral potential assessment has been undertaken to evaluate the prospectivity for carbonatite-related REE (CREE) mineralisation in Australia. CREE deposits form as the result of lithospheric- to deposit-scale processes that are spatially and temporally coincident.</div><div><br></div><div>Building on previous research into the formation of carbonatites and their related REE mineralisation, a mineral system model has been developed that incorporates four components: (1) source of metals, fluids, and ligands, (2) energy sources and fluid flow drivers, (3) fluid flow pathways and lithospheric architecture, and (4) ore deposition. This study demonstrates how national-scale datasets and a mineral systems-based approach can be used to map the mineral potential for CREE mineral systems in Australia.</div><div><br></div><div>Using statistical analysis to guide the feature engineering and map weightings, a weighted index overlay method has been used to generate national-scale mineral potential maps that reduce the exploration search space for CREE mineral systems by up to ∼90%. In addition to highlighting regions with known carbonatites and CREE mineralisation, the mineral potential assessment also indicates high potential in parts of Australia that have no previously identified carbonatites or CREE deposits.</div><div><br></div><div><b>Citation: </b>Ford, A., Huston, D., Cloutier, J., Doublier, M., Schofield, A., Cheng, Y., and Beyer, E., 2023. A national-scale mineral potential assessment for carbonatite-related rare earth element mineral systems in Australia, <i>Ore Geology Reviews</i>, V. 161, 105658. https://doi.org/10.1016/j.oregeorev.2023.105658</div>

This database contains geochemical analyses of over 7000 samples collected from or near mineral deposits from 60 countries, compiled 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 was compiled from a number of publicly-available sources, including federal and provincial government mineral deposit and geochemistry databases, and the ore samples normalised to average crustal abundance (OSNACA) database compiled by the Centre for Exploration Targeting at the University of Western Australia. Geochemical data cover the majority of the periodic table, with metadata on analytical methods and detection limits. Where available, sample descriptions include lithology, mineralogy, and host stratigraphic units. Mineral deposits are classified according to the CMMI mineral deposit classification scheme (Hofstra et al., 2021). Location information includes deposit or prospect name, and sampling location (i.e., mine, field site, or borehole collar). This dataset will be updated periodically as more data become available. Geoscience Australia: D Champion, O Raymond, D Huston, M Sexton, E Bastrakov, S van der Wielen, G Butcher, S Hawkins, J Lane, K Czarnota, I Schroder, S McAlpine, A Britt Geological Survey of Canada: K Lauzière, C Lawley, M Gadd, J-L Pilote, A Haji Egeh, F Létourneau United States Geological Survey: M Granitto, A Hofstra, D Kreiner, P Emsbo, K Kelley, B Wang, G Case, G Graham Geological Survey of Queensland: V Lisitsin

<div>GeoInsight aims to communicate geological information to non-geoscience professionals and guide users to datasets with ease via a web-based interface. The 18-month pilot project was developed as part of Geoscience Australia’s Exploring for the Future Program (2016–2024) using a human-centred design approach in which user needs are forefront considerations. Interviews and testing with users found that a simple and plain-language experience that provided packaged information with channels to further research is the preferred design. Curated information and data from across Geoscience Australia help users make decisions and refine their research approach quickly and confidently. </div><div><br></div><div>Geoscience Australia’s Exploring for the Future program provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government. </div><div><br></div><div>In the first iteration of GeoInsight, products were selected for minerals, energy, water and complementary information from Geoscience Australia’s Data Discovery Portal and Data and Publications Catalogue. These products were examined to (1) gauge the relevance of the information they contain for non-geoscientists and (2) determine how best to deliver this information for effective use by non-technical audiences. </div><div><br></div><div>This record documents the methodology used to summarise mineral commodities for GeoInsight. The method was devised to provide a straightforward snapshot of mineral production at the time of publication and future production/extraction potential based on Geoscience Australia datasets extrapolated to the regional scale across Australia. </div><div><br></div><div>The initial developmental stage has been dedicated to producing a workable foundation intended to evolve and incorporate more nuanced content centred on user feedback. Initial stages focused on extraction of data from databases across the widest possible breadth of commodities which could be supported by existing workflows and automation. A recommendation for future development is to incorporate the more nuanced information available from Geoscience Australia into future iterations of the GeoInsight platform. A wide range of information related to mineral potential is delivered by Geoscience Australia, very little of which is captured in the current version of GeoInsight. </div><div><br></div><div>Any updates to the methodology used in GeoInsight will be accompanied by updates to this document, including a change log.</div>

<div>Geological maps are powerful models for visualizing the complex distribution of rock types through space and time. However, the descriptive information that forms the basis for a preferred map interpretation is typically stored in geological map databases as unstructured text data that are difficult to use in practice. Herein we apply natural language processing (NLP) to geoscientific text data from Canada, the U.S., and Australia to address that knowledge gap. First, rock descriptions, geological ages, lithostratigraphic and lithodemic information, and other long-form text data are translated to numerical vectors, i.e., a word embedding, using a geoscience language model. Network analysis of word associations, nearest neighbors, and principal component analysis are then used to extract meaningful semantic relationships between rock types. We further demonstrate using simple Naive Bayes classifiers and the area under receiver operating characteristics plots (AUC) how word vectors can be used to: (1) predict the locations of “pegmatitic” (AUC = 0.962) and “alkalic” (AUC = 0.938) rocks; (2) predict mineral potential for Mississippi-Valley-type (AUC = 0.868) and clastic-dominated (AUC = 0.809) Zn-Pb deposits; and (3) search geoscientific text data for analogues of the giant Mount Isa clastic-dominated Zn-Pb deposit using the cosine similarities between word vectors. This form of semantic search is a promising NLP approach for assessing mineral potential with limited training data. Overall, the results highlight how geoscience language models and NLP can be used to extract new knowledge from unstructured text data and reduce the mineral exploration search space for critical raw materials.</div><div><br></div><div><strong>Citation: </strong>Lawley, C. J. M., Gadd, M. G., Parsa, M., Lederer, G. W., Graham, G. E., and Ford, A., 2023, Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling: Natural Resources Research. https://doi.org/10.1007/s11053-023-10216-1</div>

The Australian Resource Reviews are periodic national assessments of individual mineral commodities. The reviews include evaluations of short-term and long-term trends for each mineral resource, world rankings, production data, significant exploration results and an overview of mining industry developments.

<div>Disruptions to the global supply chains of critical raw materials (CRM) have the potential to delay or increase the cost of the renewable energy transition. However, for some CRM, the primary drivers of these supply chain disruptions are likely to be issues related to environmental, social, and governance (ESG) rather than geological scarcity. Herein we combine public geospatial data as mappable proxies for key ESG indicators (e.g., conservation, biodiversity, freshwater, energy, waste, land use, human development, health and safety, and governance) and a global dataset of news events to train and validate three models for predicting “conflict” events (e.g., disputes, protests, violence) that can negatively impact CRM supply chains: (1) a knowledge-driven fuzzy logic model that yields an area under the curve (AUC) for the receiver operating characteristics plot of 0.72 for the entire model; (2) a naïve Bayes model that yields an AUC of 0.81 for the test set; and (3) a deep learning model comprising stacked autoencoders and a feed-forward artificial neural network that yields an AUC of 0.91 for the test set. The high AUC of the deep learning model demonstrates that public geospatial data can accurately predict natural resources conflicts, but we show that machine learning results are biased by proxies for population density and likely underestimate the potential for conflict in remote areas. Knowledge-driven methods are the least impacted by population bias and are used to calculate an ESG rating that is then applied to a global dataset of lithium occurrences as a case study. We demonstrate that giant lithium brine deposits (i.e., >10&nbsp;Mt Li2O) are restricted to regions with higher spatially situated risks relative to a subset of smaller pegmatite-hosted deposits that yield higher ESG ratings (i.e., lower risk). Our results reveal trade-offs between the sources of lithium, resource size, and spatially situated risks. We suggest that this type of geospatial ESG rating is broadly applicable to other CRM and that mapping spatially situated risks prior to mineral exploration has the potential to improve ESG outcomes and government policies that strengthen supply chains. <b>Citation:</b> Haynes M, Chudasama B, Goodenough K, Eerola T, Golev A, Zhang SE, Park J and Lèbre E (2024) Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium. <i>Earth Sci. Syst. Soc. </i>4:10109. doi: 10.3389/esss.2024.10109

<div>These videos provide tutorials on how to use the Geoscience Australia Data portal in the classroom. They include a guide for basic navigation, how to load 2D map data sets (elevation, surface geology and critical minerals) as well as accessing a 3D data model (earthquakes).&nbsp;Additionally, they demonstrate how to directly compare multiple data and how to share collated data through a shareable link.</div><div>Videos included:</div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Introduction to using the Geoscience Australia Data Portal (2:15)</div><div>-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;How to access elevation, surface geology and critical minerals data in the Geoscience Australia Data Portal (4:26)</div><div>- How to view the global distribution of earthquakes using the Geoscience Australia Data Portal (2:51)</div><div><br></div><div>These videos are suitable for use by secondary students and adults.</div>