<div>Maps showing the potential for carbonatite-related rare earth element (REE) mineral systems in Australia. Each of the mineral potential maps is a synthesis of three or four component layers. Model 1 integrates three components: sources of metals, energy drivers, and lithospheric architecture. Model 2 integrates four components: sources of metals, energy drivers, lithospheric architecture, and ore deposition. Both models use a hybrid data-driven and knowledge driven methodology to produce the final mineral potential map for the mineral system. An uncertainty map is provided in conjunction with the mineral potential map for Model 2 that represents the availability of data coverage over Australia for the selected combination of input maps. Uncertainty values range between 0 and 1, with higher uncertainty values being located in areas where more input maps are missing data or have unknown values. An assessment criteria table is provided and contains information on the map creation.</div>

<div>Australian sediment-hosted mineral systems are important sources of base metals and critical minerals that are vital to delivering Australia’s low-carbon economy. In Australia, sediment-hosted resources account for ~82% and ~86% of the total zinc (Zn) and lead (Pb) resources respectively. Given their significance to the Australian economy, four national-scale mineral potential models for sediment-hosted Zn-Pb mineral systems have been developed: clastic-dominated siliciclastic carbonate, clastic-dominated siliciclastic mafic, Mississippi Valley-type and Irish-type. In addition to the potential for Zn-Pb mineralisation, the uncertainty related to data availability has been examined. The mineral potential models were created using a mineral systems-based approach where mappable criteria have been used to assess the prospectivity of each system. Each model has been derived from a large volume of precompetitive geoscience data. The clastic-dominated siliciclastic carbonate mineral potential model predicts 92% of known deposits and occurrences within 15.5% of the area, the clastic-dominated siliciclastic mafic mineral potential model predicts 85% of deposits and occurrences within 27% of the area, and the Mississippi Valley-type mineral potential model predicts 66% of known deposits and occurrences within 31% of the area. Each model successfully predict the location of major sediment-hosted Zn-Pb deposits while highlighting new areas of elevated prospectivity in under-explored regions of Australia, reducing the exploration search space by up to 85% for sediment-hosted Zn-Pb mineral systems.</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>

<div>The production of rare earth elements (REEs) is critical to the global transition to a low carbon economy. Carbonatites represent a significant source of REEs, both domestically within Australia, as well as globally. Given their strategic importance for the Australian economy, a national mineral potential assessment has been undertaken as part of the Exploring for the Future program at Geoscience Australia to evaluate the potential for carbonatite-related REE (CREE) mineral systems. Rather than aiming to identify individual carbonatites and/or CREE deposits, the focus of the mineral potential assessment is to delineate prospective belts or districts within Australia that indicate the presence of favourable criteria, particularly in terms of lithospheric architecture, that may lead to the formation of a CREE mineral system.</div><div><br></div><div>This study demonstrates how national-scale multidisciplinary precompetitive geoscience datasets can be integrated using a hybrid methodology that incorporates robust statistical analysis with mineral systems expertise to predictively map areas that have a higher geological potential for the formation of CREE mineral systems and effectively reduce the exploration search space. Statistical evaluation of the relationship between different mappable criteria that represent spatial proxies for mineral system processes and known carbonatites and CREE deposits has been undertaken to test previously published hypotheses on how to target CREE mineral systems at a broad-scale. The results confirm the relevance of most criteria in the Australian context, while several new criteria such as distance to large igneous province margins and distance to magnetic worms have also been shown to have a strong correlation with known carbonatites and CREE deposits. Using a hybrid knowledge- and data-driven mineral potential mapping approach, the mineral potential map predicts the location of known carbonatite and CREE deposits, while also demonstrating additional areas of high prospectivity in regions with no previously identified carbonatites or CREE mineralisation.</div> Presented at the AusIMM Critical Minerals Conference 2023.

<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>

<div>The mineral potential toolkit (aka minpot-toolkit) provides tools to facilitate mineral potential analysis, from spatial associations to feature engineering and fully integrated mineral potential mapping.</div>

<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>This database presents classified wind gust events for all Australian Automatic Weather Stations, based on semi-automatic classification of 1-minute observations of wind gust speed, temperature, dew point and station pressure. Wind events are classified based on the temporal evolution of the weather variables, using convolutional kernel transforms. Additional attributes include a number of derived variables (e.g. rainfall preceding and following the gust event), contemporaneous weather phenomena and binary classifications from a range of authors. </div><div><br></div><div>The main classification is described by Arthur, Hu and Allen (submitted to <em>Natural Hazards</em>, 2024). </div><div><br></div><div>Weather observation data are provided by the Bureau of Meteorology. Lightning data (2004-2024) was provided by TOA Systems Global Lightning Network. </div>

<div>Maps showing the potential for iron oxide copper-gold (IOCG) mineral systems in Australia. Each of the mineral potential maps is a synthesis of four component layers (source of metals, fluids and ligands; energy sources and fluid flow drivers; fluid flow pathways and architecture; and ore depositional gradients). The model uses a hybrid data-driven and knowledge driven methodology to produce the final mineral potential map for the mineral system. An uncertainty map is provided in conjunction with the mineral potential maps that represents the availability of data coverage over Australia for the selected combination of input maps. Uncertainty values range between 0 and 1, with higher uncertainty values being located in areas where more input maps are missing data or have unknown values. The input maps and mineral deposits and occurrences used to generate the mineral potential map are provided along with an assessment criteria table which contains information on the map creation.</div>

<div>GeoInsight was an 18-month pilot project developed in the latter part of Geoscience Australia’s Exploring for the Future Program (2016–2024). The aim of this pilot was to develop a new approach to communicating geological information to non-technical audiences, that is, non-geoscience professionals. The pilot was developed using a human-centred design approach in which user needs were forefront considerations. Interviews and testing found that users wanted a simple and fast, plain-language experience which provided basic information and provided pathways for further research. GeoInsight’s vision is to be an accessible experience that curates information and data from across the Geoscience Australia ecosystem, helping users make decisions and refine their research approach, quickly and confidently.</div><div><br></div><div>Geoscience Australia hosts a wealth of geoscientific data, and the quantity of data available in the geosciences is expanding rapidly. This requires newly developed applications such as the GeoInsight pilot to be adaptable and malleable to changes and updates within this data. As such, utilising the existing Oracle databases, web service publication and platform development workflows currently employed within Geoscience Australia (GA) were optimal choices for data delivery for the GeoInsight pilot.&nbsp;This record is intended to give an overview of the how and why of the technical infrastructure of this project. It aims to summarise how the underlying databases were used for both existing and new data, as well as development of web services to supply the data to the pilot application.&nbsp;</div>