<div>Near-surface magnetizations are ubiquitous across many areas of Australia and complicate reliable estimation of depth to deeper magnetizations. We have selected four test areas in which we use equivalent source dipoles to represent and quantify the near-surface magnetizations. We present a synthetic modelling study that demonstrates that field variations from the near-surface magnetizations substantially degrade estimation of depth to a magnetization 500 metres below the modelled sensor elevation and that these problems persist even for anomalies with significantly higher amplitudes. However, preferential attenuation of the fields from near surface magnetizations by upward continuation proved quite effective in improving estimation of depth to those magnetizations.</div> This Abstract was submitted/presented at the 2023 Australasian Exploration Geoscience Conference (AEGC) 13-18 March (https://2023.aegc.com.au/)

<div>Around the world the Earth's crust is blanketed to various extents by sedimentary cover. For continental regions, knowledge of the distribution and thickness of sediments is crucial for a wide range of applications including seismic hazard, resource potential, and our ability to constrain the deeper crustal geology. Excellent constraints on the sedimentary thickness can be obtained from borehole drilling or active seismic surveys. However, these approaches are expensive and impractical in remote continental interiors such as central Australia. </div><div><br></div><div>Recently, a method for estimating the sedimentary thickness using passive seismic data, the collection of which is relatively simple and low-cost, was developed and applied to seismic stations in South Australia. This method uses receiver functions, specifically the time delay of the \P{}-to-\S{} converted phase generated at the sediment-basement interface, relative to the direct-P arrival, to generate a first order estimate of the thickness of sedimentary cover. In this work we expand the analysis to the vast array of over 1500 seismic stations across Australia, covering an entire continent and numerous sedimentary basins that span the entire range from Precambrian to present-day. We compare with an established yet separate method to estimate the sedimentary thickness, which utilises the autocorrelation of the radial receiver functions to ascertain the two-way travel-time of shear waves reverberating in a sedimentary layer.</div><div><br></div><div>Across the Australian continent the new results clearly match the broad pattern of expected sedimentation based on the various geological provinces. Furthermore we are able to delineate the boundaries of many sedimentary features, such as the Eucla and Murray Basins, which are Cenozoic, and the boundary between the Karumba Basin and the mineral rich Mount Isa Province. The signal is found to diminish for older Proterozoic basins, likely due to compaction and metamorphism of the sediments over time. Finally, a comparison with measurements of sedimentary thickness from local boreholes allows for a straightforward predictive relationship between the delay time and the cover thickness to be defined. This offers future widespread potential, providing a simple and cheap way to characterise the sedimentary thickness in under-explored areas from passive seismic data. </div><div><br></div><div>This study and some of the data used are funded and supported by the Australian Government's Exploring for the Future program led by Geoscience Australia.</div> <b>Citation:</b> Augustin Marignier, Caroline M Eakin, Babak Hejrani, Shubham Agrawal, Rakib Hassan, Sediment thickness across Australia from passive seismic methods, <i>Geophysical Journal International</i>, Volume 237, Issue 2, May 2024, Pages 849–861, <a href="https://doi.org/10.1093/gji/ggae070">https://doi.org/10.1093/gji/ggae070</a>

<div>Finding new mineral deposits hidden beneath the sedimentary cover of Australia has become a national priority, given the country’s economic dependence on natural resources and urgent demand for critical minerals for a sustainable future. A fundamental first step in finding new deposits is to characterise the depth of sedimentary cover. Excellent constraints on the sedimentary thickness can be obtained from borehole drilling or active seismic surveys. However, these approaches are expensive and impractical in the remote regions of Australia. With over three quarters of the continent being covered in sedimentary and unconsolidated material, this poses a significant challenge to exploration.</div><div><br></div><div>Recently, a method for estimating the sedimentary thickness using passive seismic data, the collection of which is relatively simple and low-cost, was developed and applied to seismic stations in South Australia. The method uses receiver functions, specifically the delay time of the P-to-S converted phase generated at the interface of the sedimentary basement, relative to the direct-P arrival, to generate a first order estimate of the thickness of sedimentary cover. In this work we apply the same method to the vast array of seismic stations across Australia, using data from broadband stations in both permanent and temporary networks.&nbsp;We also investigate using the two-way traveltime of shear waves, obtained from the autocorrelation of radial receiver functions, as a related yet separate estimate of sedimentary thickness.&nbsp;</div><div><br></div><div>From the new receiver function delay time and autocorrelation results we are able to identify many features, such as the relatively young Cenozoic Eucla and Murray Basins. Older Proterozoic regions show little signal, likely due to the strong compaction of sediments.&nbsp;A comparison with measurements of sedimentary thickness from local boreholes gives a straightforward predictive relationship between the delay time and the cover thickness, offering a simple and cheap way to characterise the sedimentary thickness in unexplored areas from passive seismic data. This study and some of the data used are funded and supported by the Australian Government's Exploring for the Future program led by Geoscience Australia. Abstract to be submitted to/presented at the American Geophysical Union (AGU) Fall Meeting 2023 (AGU23) - https://www.agu.org/fall-meeting

<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. This work contributes to building a better understanding of the Australian continent, whilst giving the Australian public the tools they need to help them make informed decisions in their areas of interest.</div><div><br></div><div>As part of the Australia's Resources Framework Project, in the Exploring for the Future Program, Geoscience Australia and CSIRO undertook a magnetic source depth study across four areas, with the objectives of generating cover model constraints from magnetic modelling to expand national coverage, and to improve our subsurface understanding of these areas. During this study, 2005 magnetic estimates of depth to the top of magnetization were generated, with solutions derived using a consistent methodology (targeted magnetic inversion modelling, or TMIM; also known as ‘sweet-spot’ modelling). The methodology for these estimates are detailed in a summary report by Foss et al (2024), and is available for download through Geoscience Australia’s enterprise catalogue (https://pid.geoscience.gov.au/dataset/ga/149239). </div><div><br></div><div>The new points were generated over four areas: 1) the western part of Tasmania that is the southernmost extension of the Darling-Curnamona-Delamerian (DCD) project area; 2) northeastern Queensland; 3) the Officer Basin area of western South Australia and southeastern West Australia; and 4) the Eastern Resources Corridor (ERC), covering eastern South Australia, southwest Queensland, western New South Wales and western Victoria. These depth estimates have been released, together with a summary report detailing the data and methodology used to generate the results, through Geoscience Australia's product catalogue (ecat) at https://pid.geoscience.gov.au/dataset/ga/149239.</div><div><br></div><div>This supplementary data release contains the chronostratigraphic attribution of the new TMIM magnetic depth estimates, which range in depth from at surface to 13,294 m below ground. To ensure that the interpretations took into account the local geological features, the magnetic depth estimates were integrated and interpreted with other geological and geophysical datasets, including borehole stratigraphic logs, potential fields images, surface and solid geology maps, and airborne electromagnetic interpretations (where available). </div><div><br></div><div>Each depth-solution is interpretively ascribed to either a chronostratigraphic boundary with the stratigraphic units above and below the depth estimate, or the stratigraphic unit that the depth estimate occurs within, populated from the Australian Stratigraphic Units Database (ASUD). Stratigraphic attribution adds value and informs users of the depth to certain stratigraphic units in their areas of interest. Each solution is accompanied by confidence estimates. The depth estimate points are formatted for compliance with Geoscience Australia’s (GA) Estimates of Geological and Geophysical Surfaces (EGGS) database, the national repository for standardised depth estimate points. </div><div><br></div><div>Results from these interpretations provided some support to stratigraphic drillhole targeting, as part of the Delamerian Margins NSW National Drilling Initiative campaign, a collaboration between GA’s EFTF program, the MinEx CRC National Drilling Initiative and the Geological Survey of New South Wales. The magnetic depth-estimate solutions produced within this study provide important depth constraints in data-poor areas. These data help to construct a better understanding of the 3D geometry of the Australian continent and aid in cover thickness modelling activities. The availability of the depth-estimate solutions via the EGGS database through Geoscience Australia’s Portal creates enduring value to the public.</div>

<div>Alkaline igneous and related rocks are recognised as a significant source of the critical minerals essential for Australia’s transition to net-zero. Understanding these small but economically significant group of poorly mapped rocks is essential for identifying their resource potential. The Australian Alkaline Rocks Atlas aims to capture all known occurrences of these volumetrically minor, but important, igneous rocks in a national compilation, to aid understanding of their composition, distribution and age at the continental scale. The Atlas, comprises five, stand-alone data packages covering the Archean, Proterozoic, Paleozoic, Mesozoic and Cenozoic eras. Each data package includes a GIS database and detailed accompanying report that informs alkaline rock nomenclature, classification procedures, individual units and their grouping into alkaline provinces based on common age, characteristics and inferred genesis. The Alkaline Rocks Atlas will form a foundation for more expansive research on related mineral systems and their corresponding economic potential being undertaken as part of the EFTF program. To illustrate the use of the Alkaline Rocks Atlas, a mineral potential assessment using a subset of the Atlas has been undertaken for carbonatite-related rare earth element mineral systems that aims to support mineral exploration and land-use decision making that aims to support mineral exploration and land-use decision making.</div>

<div>The Exploring for the Future program, led by Geoscience Australia, was a $225 million Australian Government investment over 8 years, focused on revealing Australia’s mineral, energy, and groundwater potential by characterising geology.&nbsp;&nbsp;This report provides an overview of activities, results, achievements and impacts from the Exploring for the Future program, with a particular focus on the last four years (2020-2024). &nbsp;</div>

The Exploring for the Future program Showcase 2024 was held on 13-16 August 2024. Day 2 - 14th August talks included: <b>Session 1 - Architecture of the Australian Tectonic Plate</b> <a href="https://youtu.be/a8jzTdNdwfk?si=OWNlVR-FLDhF1GVM">AusArray: Australian lithosphere imaging from top to bottom</a> - Dr Alexei Gorbatov <a href="https://youtu.be/j5ox8Ke5n6M?si=YkfDno2xmZXueS1b">AusLAMP: Mapping lithospheric architecture and reducing exploration space in Australia</a> - Jingming Duan <a href="https://youtu.be/qZ6wjzx_dNc?si=NjDEzvqyEeM24-E8">Constraining the thermomechanical and geochemical architecture of the Australian mantle: Using combined analyses of xenolith inventories and seismic tomography</a> - Dr Mark Hoggard <b>Session 2 - Quantitative characterisation of Australia's surface and near surface</b> <a href="https://youtu.be/nPfa_j3_dos?si=mktfIJWXeLElIOK4">AusAEM: The national coverage and sharpening near surface imaging</a> - Dr Anandaroop Ray <a href="https://youtu.be/SU6ak98JvAw?si=DQPovulHa4poqcm0">Unlocking the surface geochemistry of Australia</a> - Phil Main <a href="https://youtu.be/Xtm45CT6e-s?si=JHU7J-ktgVrbj1Ke">Spotlight on the Heavy Mineral Map of Australia</a> - Dr Alex Walker <b>Session 3 – Maps of Australian geology like never before</b> <a href="https://youtu.be/aRISb1YYigU?si=3byJbqW0qRTqCB8-">An Isotopic Atlas of Australia: Extra dimensions to national maps</a> - Dr Geoff Fraser <a href="https://youtu.be/khSy-WAkw-w?si=F-Y67FX3jXN5zZaz">First continental layered geological map of Australia</a> - Dr Guillaume Sanchez <a href="https://youtu.be/Z3GlCJepLK4?si=k_tbaKdmxGBmoSro">An integrated 3D layered cover modelling approach: Towards open-source data and methodologies for national-scale cover modelling</a> - Sebastian Wong View or download the <a href="https://dx.doi.org/10.26186/149800">Exploring for the Future - An overview of Australia’s transformational geoscience program</a> publication. View or download the <a href="https://dx.doi.org/10.26186/149743">Exploring for the Future - Australia's transformational geoscience program</a> publication. You can access full session and Q&A recordings from YouTube here: 2024 Showcase Day 2 - Session 1 - <a href="https://www.youtube.com/watch?v=EHBsq0-pC8c">Architecture of the Australian Tectonic Plate</a> 2024 Showcase Day 2 - Session 2 - <a href="https://youtube.com/watch?v=xih4lbDk-1A">Quantitative characterisation of Australia's surface and near surface</a> 2024 Showcase Day 2 - Session 3 - <a href="https://www.youtube.com/watch?v=qeTLc1K-Cds">Maps of Australian geology like never before</a>

<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>Raster datasets of inferred magnesium number for the bulk lithospheric mantle across the Australian continent. The magnesium number is an elemental ratio, defined by Mg / (Mg + Fe), which correlates to the relative enrichment or depletion in incompatible elements. Oxide concentrations are inferred in from thermo-chemical inverse modelling of Rayleigh phase velocities, surface heat flow, geoid anomalies, and topography. The magnesium number rasters summarise the results of a Markov-chain Monte Carlo sampling of the posterior model space from an ensemble of plausible candidate models. Model release 'FR23' is developed using primary-mode Rayleigh phase velocity grids adapted from Fishwick & Rawlinson (2012; "3-D structure of the Australian lithosphere from evolving seismic datasets"). Model release 'KY22' is developed using the primary-mode Rayleigh phase velocity grids of Yoshizawa (2014; "Radially anisotropic 3-D shear wave structure of the Australian lithosphere and asthenosphere from multi-mode surface waves"). All models are products of the Exploring for the Future program.</div>

<div>As part of the first phase (2016-2020) of the Exploring for the Future (EFTF) program, Geoscience Australia deployed 119 broad band seismic stations in northern Australia. This deployment was part of the Australian Passive Seismic Array (AusArray) Project. Data from these stations were used to image the seismic structure using various techniques, including ambient noise tomography (ANT). The first ANT model (Hejrani et al, 2020) was focused on a narrow range of frequencies and used the Hawkins and Sambridge (2019) approach to estimate dispersion curves. This new approach starts from the original work by Aki (1957) to estimate phase velocity in the frequency domain, and then takes a step further to ensure a smooth curve is achieved. In Hejrani et al., (2022), using minimum Signal-to-Noise-Ratio (SNR) threshold of 2, about 4,000 data points (out of 7,000+) were used to generate surface wave velocity maps at a resolution of 1 degree at four frequencies (sensitive to different depths). This model was subsequently updated in September 2021 by using all 7,000+ data points (no SNR threshold) of phase velocity measurements across AusArray year one to provide a 0.25 degree resolution model. All dispersion curves regardless of their quality were utilized. A number of artefacts were identified in that model, which motivated further investigations. During 2022, I developed a new automated and scalable approach to estimate dispersion curves, which was completed in December 2022. This new method starts from the original idea by Aki (1957), but takes a different approach to stabilize the dispersion curves and to avoid cycle skipping. </div><div>This record represents the preferred 2D velocity models for AusArray year one data based on the newly estimated dispersion curves and a comparison with previous models and interpretations; is an update from Hejrani et al. (2020) and should be read in conjunction. Work is currently under way to invert these 2D surface wave models to obtain 3D velocity models for the crust and mantle. Such 3D velocity models would be suitable for joint interpretations with other data such active seismic, gravity and magnetic. The code will be made publicly available at the conclusion of EFTF.</div>