This web service contains map layers and coverages for machine learning models, using raster datasets which include radiometric grid infill, cover depths and conductivity. All grids have been converted to cloud-optimised GeoTIFF (COG) format for use and delivery from an cloud-based object store (AWS s3). For potassium (K), thorium (Th) and uranium (U) radiometric infill grids, an equalised histogram was applied to each grid. The radiometric ternary image has no style applied, with from transparency for no-data values. A tile service (WMTS) is also integrated into the WMS to provide a high-performing service for integration into web maps and online mapping portals.

This web service contains map layers and coverages for machine learning models, using raster datasets which include radiometric grid infill, cover depths and conductivity. All grids have been converted to cloud-optimised GeoTIFF (COG) format for use and delivery from an cloud-based object store (AWS s3).

<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

The Proterozoic succession in the NDI Carrara 1 drill hole, Northern Territory, consists predominantly of tight shales, siltstones, and calcareous clastic rocks. As part of Geoscience Australia’s Exploring for the Future program, this study aims to derive porosity, permeability and gas content from both laboratory testing and well log interpretation from machine learning approaches, to improve the Proterozoic shale gas reservoir characterisation. The Proterozoic Lawn Hill Formation was divided into four chemostratigraphic packages. The middle two packages were further divided into seven internal units according to principal component analysis and self-organising map clustering on well logs and inorganic geochemical properties. Artificial neural networks were then applied to interpret the mineral compositions, porosity and permeability from well logs, density and neutron-density crossplot interpretations. Gas content was estimated from the interpreted porosity, gas saturation, total organic carbon and clay contents. Petrophysical interpretation results are summarised for all chemostratigraphic packages and units. Package 2 (1116–1430.1 m) has the highest potential among the four chemostratigraphic packages. P2U1 (1116–1271 m) and P2U3 (1335.5–1430.1 m) units have the most favourable petrophysical properties for organic-rich shales with the average total gas contents of 1.25 cm3/g and 1.30 cm3/g, geometric mean permeability of 4.79 µD and 17.56 µD, and net shale thickness of 54.4 m and 85.3 m, respectively. P3U4 unit (687.9–697.9 m) has high gas content and permeability, with the net shale thickness of 29.1 m. Besides the organic-rich shales, the tight non-organic-rich siltstone and shale reservoirs in package 1 (below 1430.1 m) have average gas saturation of 14% and geometric mean permeability of 1.31 µD, respectively. Published in The APPEA Journal 2023. <b>Citation:</b> Wang Liuqi, Bailey Adam H. E., Grosjean Emmanuelle, Carson Chris, Carr Lidena K., Butcher Grace, Boreham Christopher J., Dewhurst Dave, Esteban Lionel, Southby Chris, Henson Paul A. (2023) Petrophysical interpretation and reservoir characterisation on Proterozoic shales in National Drilling Initiative Carrara 1, Northern Territory. <i>The APPEA Journal</i><b> 63</b>, 230-246. https://doi.org/10.1071/AJ22049

<div>With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses a digital soil mapping framework to combine data from recent geochemical surveys and environmental covariates to predict and map Li content across the 7.6 million km2 area of Australia. Soil samples were collected by the National Geochemical Survey of Australia at a total of 1315 sites, with both top (0–10 cm depth) and bottom (on average 60–80 cm depth) catchment outlet sediments sampled. We developed 50 bootstrap models using a Cubist regression tree algorithm for both depths. The spatial prediction models were validated on an independent Northern Australia Geochemical Survey dataset, showing a good prediction with an RMSE of 3.82 mg kg-1 for the top depth. The model for the bottom depth has yet to be validated. The variables of importance for the models indicated that the first three Landsat bands and gamma radiometric dose have a strong impact on Li prediction. The bootstrapped models were then used to generate digital soil Li prediction maps for both depths, which could select and delineate areas with anomalously high Li concentrations in the regolith. The map shows high Li concentration around existing mines and other potentially anomalous Li areas. The same mapping principles can potentially be applied to other elements.&nbsp;</div> <b>Citation:</b> Ng, W., Minasny, B., McBratney, A., de Caritat, P., and Wilford, J.: Digital soil mapping of lithium in Australia, <i>Earth Syst. Sci. Data</i>, 15, 2465–2482, https://doi.org/10.5194/essd-15-2465-2023, <b>2023</b>.

Prediction of true classes of surficial and deep earth materials using multivariate geospatial data is a common challenge for geoscience modellers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.

<div><strong>Output Type: </strong>Exploring for the Future Extended Abstract</div><div><br></div><div><strong>Short Abstract: </strong>Most geological mapping either over-estimates the amount of bedrock exposed at the surface or can miss local bedrock exposures in geological units describing cover materials (i.e. alluvium and colluvium). A machine learning, Convolutional Neural Network (CNN) has been applied to detect outcrops (exposed bedrock at the earth’s surface) and areas of very shallow cover over bedrock (i.e. sub-crop) at one meter resolution. We used a multi-feature training dataset consisting of sites associated with urban areas, roads, outcrops, waterbodies, soil (includes bare soil and soil covered by green and dry vegetation), trees, and shadows. Even though we were only interested in mapping outcrop, a multi-criteria label set significantly improved overall accuracy of the model. The explanatory variables or covariates included high-resolution satellite imagery, Sentinel-2 imagery, and terrain derivatives. The modelling approach was tested over an area in central West NSW, Australia. Labels were split into 80% for training and 20% for out-of-sample validation. Spatial K-groups were used in the training set to minimize auto-spatial correlation between neighbouring points and reduce the potential for overfitting. Two CNN model architectures were evaluated: Simple-Net and UNet. The Simple-Net structure consists of 2D Convolution layer and flatten layer, whereas the UNet architecture includes a mixture of 2D convolution layer, max pooling, up sampling and flatten layer. These models were tested with and without the use of high-resolution imagery. The UNet model incorporating high resolution imagery gave the best results (accuracy of 0.841 and an F1 score of 0.814), compared with Simple-Net (accuracy of 0.823 and an F1 score of 0.786). However, the Simple-Net’s model without the incorporation of high-resolution imagery was a slight improvement over the UNet architecture and due to the lack of national coverage for high-resolution imagery, the Simple-Net model offers better scalability. The detection of outcrop/sub-crop has broad application in improving the spatial explicitness of existing geological maps, improving sample detection and interpretation of litho-stratigraphy and geochemistry. High spatial resolution of outcrop/sub-crop also has implications in the agricultural and civil engineering sectors, ecology and in understanding surface and near-surface hydrological systems. After this proof-of-concept phase we plan to up-scale the approach nationally using a more representative labels and national covariates.</div><div><br></div><div><strong>Citation: </strong>Du, Z., Wilford, J. &amp; Roberts, D., 2024. Find a rock or a rock nearby using Convolution Neural Networks. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts. Geoscience Australia, Canberra. https://doi.org/10.26186/149631</div>

Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry-related variables due to the lack of backscatter-derived data. In this study, we applied random forests (RFs), hybrid methods of RF and geostatistics, and generalized boosted regression modelling (GBM), to seabed sand content point data and acoustic multibeam data and their derived variables, to develop an accurate model to predict seabed sand content at a local scale. We also addressed relevant issues with variable selection. It was found that: (1) backscatter-related variables are more important than bathymetry-related variables for sand predictive modelling; (2) the inclusion of highly correlated predictors can improve predictive accuracy; (3) the rank orders of averaged variable importance (AVI) and accuracy contribution change with input predictors for RF and are not necessarily matched; (4) a knowledge-informed AVI method (KIAVI2) is recommended for RF; (5) the hybrid methods and their averaging can significantly improve predictive accuracy and are recommended; (6) relationships between sand and predictors are non-linear; and (7) variable selection methods for GBM need further study. Accuracy-improved predictions of sand content are generated at high resolution, which provide important baseline information for environmental management and conservation. <b>Citation:</b> Li, J.; Siwabessy, J.; Huang, Z.; Nichol, S. Developing an Optimal Spatial Predictive Model for Seabed Sand Content Using Machine Learning, Geostatistics, and Their Hybrid Methods. <i>Geosciences</i> 2019, 9, 180. https://doi.org/10.3390/geosciences9040180

<div><strong>Purpose</strong></div><div>This package comprises a set of 86 thematic grids (rasters) derived from national coverages of gravity and magnetic survey data. These datasets provide valuable information about the distribution of geological features, physical property variations, and the composition of the Earth's crust. All grids have been resampled to the same cell size, map extent, and projection to allow them to be integrated into predictive mapping and modelling workflows using machine learning. Users can download individual grids or the whole grid package. </div><div>&nbsp;</div><div><strong>Input Data</strong></div><div>The following Australian national datasets were used:</div><div>1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2019 Australian National Gravity Grids: Free Air Anomaly, Complete Bouguer Anomaly, De-trended Global Isostatic Residual, 400 m cell size (Lane <em>et al</em>., 2020).</div><div>2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Total Magnetic Intensity (TMI) Grid of Australia 2019 - seventh edition Enhanced Products Package (Morse, 2020).</div><div><br></div><div><strong>Processing</strong></div><div>All processing of the national grids were undertaken using Intrepid software. The following was performed on the input data:</div><div>1.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The grids were reprojected from GDA94 geodetic to Australian Albers (EPSG 3577). </div><div>2.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;The grids were aligned to the same grid cell registration point and interpolated to fit within an 80 m cell size using a cubic spline method to ensure that the cell locations for all images are common.</div><div>3.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Various Fast Fourier Transforms (FFT) were applied to each grid (see ‘Grids_for_Machine_Learning_dataset_notes.pdf’). </div><div>&nbsp;</div><div><strong>Metadata (all grids)</strong></div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Datum: GDA94</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Projection: Australian Albers (EPSG 3577)</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Cell size: 80 m</div><div>·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;File format: GeoTiff (.tif)</div> <b>Data is available on request from clientservices@ga.gov.au - Quote eCat# 149130</b>

<div><strong>Output Type: </strong>Exploring for the Future Extended Abstract</div><div><br></div><div><strong>Short Abstract: </strong>Airborne electromagnetic surveys are widely used in Australia for mineral exploration, groundwater assessment (i.e. hydro-stratigraphy and water quality) and natural resource management (i.e. salinity assessment). In the last decade, regional surveys have been acquired covering approximately two thirds of the continent and resulting in a large volume of data to interpret. To address this challenge, we have developed a machine learning workflow to assist with the interpretation of AEM conductivity depth sections.</div><div>‘AEM assist’ is an open-source machine learning algorithm that allows the user to interpret AEM sections from drillhole observations and/or interpreted segments along the conductivity depth section. AEM assist finds predictive relationships between the training observations (drillhole and/or interpreted sections) and the conductivity value which also includes the first vertical derivative of the conductivity. Due to the non-uniqueness of the conductivity response, we have also built in a suite of supplementary covariates or features to help improve the model prediction. These features include terrain indices, gamma radiometric, surface weathering intensity, distance proxies (e.g., distance from rocks of a known age), climate indices, gravity, and magnetic derivatives. We have built the AEM assist into a national mapping framework to facilitate model interpretation and training anywhere in Australia. Although local training of sections is recommended the national framework provides an opportunity to train a model in one region and predict into another area given similar geological and landscape histories. The AEM assist has the potential to speed up the interpretation of AEM flightline sections with statistical models of interpretation uncertainty. AEM assist can be used to provide a first pass interpretation of a survey area that can later be revised by the domain expert. A feature of AEM assist is that it systematically integrates many datasets that would otherwise be difficult to do from traditional methods.</div><div><br></div><div><strong>Citation:</strong> Basak S., Wilford J., Wong S.C.T., Ley-Cooper Y. & Ray A., 2024. AEM assist - a national predictive machine learning framework for airborne electromagnetic interpretation and extrapolation. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts. Geoscience Australia, Canberra, https://doi.org/10.26186/149495</div>