This service provides Estimates of Geological and Geophysical Surfaces (EGGS). The data comes from cover thickness models based on magnetic, airborne electromagnetic and borehole measurements of the depth of stratigraphic and chronostratigraphic surfaces and boundaries.

The Murray Basin is a saucer-shaped basin with flat-lying Cenozoic sediments up to approximately 600 m thickness (Brown and Stephenson, 1991). Constraints on the thickness of the Murray Basin have been compiled from: drillholes, reflection seismic profile interpretations, refraction seismic profiles and depth to magnetic basement estimates (Target_type.pdf). Target depths were sourced from Geoscience Australia, the national Groundwater Information System database (Http://www.bom.gov.au/water/groundwater/ngis/), the Geological Survey of Victoria (http://earthresources.vic.gov.au/earth-resources/geology-of-victoria/geological-survey-of-victoria) and the Geological Survey of South Australia (http://www.minerals.statedevelopment.sa.gov.au/geoscience/geological_survey). In addition, some of the magnetic depth estimates used data from McLean (2010). To constrain the thickness of Cenozoic cover where sediments were either absent or very thin we generated shallow-depth values in areas with post-Cenozoic geology and high topographic relief. In all, 5436 depth estimates were compiled (Target_depths.xlsx). The input datasets have been used to generate two predictive models of the thickness of Cenozoic sediments within the Murray Basin. The first model uses kriging of the depth estimates to generate a gridded surface using a local-area linear variogram model as a means of interpolating between constraints (Murray_Basin_kriging_Cenozoic_thickness.pdf; Murray_Basin_krig.tif -floating value grid). The second model uses a machine-learning approach where correlations between 17 supplementary datasets and 5436 depth estimates are used to derive a predictive model. We used a supervised learning algorithm known as Gaussian Process (GP) to generate the integrated predictive model. Gaussian Process is a non-parametric probabilistic approach to learning. It uses kernel functions to measure the similarity between points and predict values not seen from the training data (see Read_Me_GP.rtf). The supplementary datasets used in the model are listed in Table 1 and model variable settings can be found in read_me.rtf (Murray_Basin_GP_Cenozoic_thickness.pdf; Murray_Basin_GP_model.tif -floating value grid). Both approaches delineate the overall structure, geometry and thickness of the Murray Basin. The advantage of the machine learning approach is that it learns relationships between the depth and supplementary datasets which allow predictions in areas with limited constraints. References Brown, C. M. and Stephenson, A. E., 1991, Geology of the Murray Basin, southeastern Australia, Canberra, Bureau of Mineral Resources Bulletin 235, 430 p. McLean, M.A., 2010. Depth to Palaeozoic basement of the Gold Undercover region from borehole and magnetic data. GeoScience Victoria Gold Undercover Report 21. Department of Primary Industries, Victoria. Table 1. Supplementary input datasets used in predictive estimation of Murray Basin thickness, utilising a machine learning method Covariates* Description 1 Latitude Gridded latitude values 2 Longitude Gridded longitude values 3 Elevation Terrain elevation – 90m shuttle DEM 4 Distance from bedrock Euclidean distance from outcrop geology units older than Cenozoic 5 Gravity Terrain and isostatic corrected Bouguer gravity 6 Gravity 1228 Upward continued gravity at 1228 metres 7 Gravity 2407 Upward continued gravity at 2407 metres 8 Gravity 6605 Upward continued gravity at 6605 metres 9 Gravity 18124 Upward continued gravity at 18124 metres 10 Gravity 35524 Upward continued gravity at 35524 metres 11 Gravity 49734 Upward continued gravity at 49734 metres 12 Gravity 97479 Upward continued gravity at 97479 metres 13 Gravity – 1k Isostatically corrected gravity subtracted from upward continued gravity at 1000 metres 14 Magnetics 5km Upward continued magnetic anomaly grid at 5 km 15 Magnetic 10km Upward continued magnetic anomaly grid at 10 km 16 Magnetic 5-10km Upward continued 5km magnetic anomaly grid subtracted from upward continued 10 km magnetic anomaly grid 17 Magnetic basement Depth to magnetic basement using the tilt method. *Primary datasets including gravity, magnetics and surface geology sourced from Geoscience Australia http://www.ga.gov.au/data-pubs/maps Elevation dataset used the 3 second (~90m) Shuttle Radar Topography Mission (SRTM) digital elevation model. https://pid.geoscience.gov.au/dataset/ga/72760.

<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

To meet the increasing demand for natural resources globally, industry faces the challenge of exploring new frontier areas that lie deeper undercover. Here, we present an approach to, and initial results of, modelling the depth of four key chronostratigraphic packages that obscure or host mineral, energy and groundwater resources. Our models are underpinned by the compilation and integration of ~200 000 estimates of the depth of these interfaces. Estimates are derived from interpretations of newly acquired airborne electromagnetic and seismic reflection data, along with boreholes, surface and solid geology, and depth to magnetic source investigations. Our curated estimates are stored in a consistent subsurface data repository. We use interpolation and machine learning algorithms to predict the distribution of these four packages away from the control points. Specifically, we focus on modelling the distribution of the base of Cenozoic-, Mesozoic-, Paleozoic- and Neoproterozoic-age stratigraphic units across an area of ~1.5 million km2 spanning the Queensland and Northern Territory border. Our repeatable and updatable approach to mapping these surfaces, together with the underlying datasets and resulting models, provides a semi-national geometric framework for resource assessment and exploration. <b>Citation:</b> Bonnardot, M.-A., Wilford, J., Rollet, N., Moushall, B., Czarnota, K., Wong, S.C.T. and Nicoll, M.G., 2020. Mapping the cover in northern Australia: towards a unified national 3D geological model. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

Effective mineral, energy and groundwater resource management and exploration rely on accurate geological maps. While geological maps of the surface exist and increase in resolution, maps of the subsurface are sparse, and the underpinning geological and geophysical constraints are disordered or non-existent. The Estimates of Geological and Geophysical Surfaces (EGGS) database seeks to enable robust subsurface geological mapping by establishing an ordered collection of precious geological and geophysical interpretations of the subsurface. EGGS stores the depth to geological boundaries derived from boreholes as well as interpretations of depth to magnetic top assessments, airborne electromagnetics inversions and reflection seismic profiles. Since geological interpretation is iterative, links to geophysical datasets and processing streams used to image the subsurface are stored. These metadata allow interpretations to be readily associated with the datasets from which they are derived and re-examined. The geological basis for the interpretation is also recorded. Stratigraphic consistency is maintained by linking each interpretation to the Australian Stratigraphic Units Database. As part of the Exploring for the Future program, >170 000 points were entered into the EGGS database. These points underpin construction of cover thickness models and economic fairway assessments. <b>Citation:</b> Mathews, E.J., Czarnota, K., Meixner, A.J., Bonnardot, M.-A., Curtis, C., Wilford, J., Nicoll, M.G., Wong, S.C.T., Thorose, M. and Ley-Cooper, Y., 2020. Putting all your EGGS in one basket: the Estimates of Geological and Geophysical Surfaces database. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

Building on newly acquired airborne electromagnetic and seismic reflection data during the Exploring for the Future (EFTF) program, Geoscience Australia (GA) generated a cover model across the Northern Territory and Queensland, in the Tennant Creek – Mount Isa (TISA) area (Figure 1; between 13.5 and 24.5⁰ S of latitude and 131.5 and 145⁰ E of longitude) (Bonnardot et al., 2020). The cover model provides depth estimates to chronostratigraphic layers, including: Base Cenozoic, Base Mesozoic, Base Paleozoic and Base Neoproterozoic. The depth estimates are based on the interpretation, compilation and integration of borehole, solid geology, reflection seismic, and airborne electromagnetic data, as well as depth to magnetic source estimates. These depth estimates in metres below the surface (relative to the Australian Height Datum) are consistently stored as points in the Estimates of Geophysical and Geological Surfaces (EGGS) database (Matthews et al., 2020). The data points compiled in this data package were extracted from the EGGS database. Preferred depth estimates were selected to ensure regional data consistency and aid the gridding. Two sets of cover depth surfaces (Bonnardot et al., 2020) were generated using different approaches to map megasequence boundaries associated with the Era unconformities: 1) Standard interpolation using a minimum-curvature gridding algorithm that provides minimum misfit where data points exist, and 2) Machine learning approach (Uncover-ML, Wilford et al., 2020) that allows to learn about relationships between datasets and therefore can provide better depth estimates in areas of sparse data points distribution and assess uncertainties. This data package includes the depth estimates data points compiled and used for gridding each surface, for the Base Cenozoic, Base Mesozoic, Base Paleozoic and Base Neoproterozoic (Figure 1). To provide indicative trends between the depth data points, regional interpolated depth surface grids are also provided for the Base Cenozoic, Base Mesozoic, Base Paleozoic and Base Neoproterozoic. The grids were generated with a standard interpolation algorithm, i.e. minimum-curvature interpolation method. Refined gridding method will be necessary to take into account uncertainties between the various datasets and variable distances between the points. These surfaces provide a framework to assess the depth and possible spatial extent of resources, including basin-hosted mineral resources, basement-hosted mineral resources, hydrocarbons and groundwater, as well as an input to economic models of the viability of potential resource development.

<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>This data package contains interpretations of airborne electromagnetic (AEM) conductivity sections in the Exploring for the Future (EFTF) program’s Eastern Resources Corridor (ERC) study area, in south eastern Australia. Conductivity sections from 3 AEM surveys were interpreted to provide a continuous interpretation across the study area – the EFTF AusAEM ERC (Ley-Cooper, 2021), the Frome Embayment TEMPEST (Costelloe et al., 2012) and the MinEx CRC Mundi (Brodie, 2021) AEM surveys. Selected lines from the Frome Embayment TEMPEST and MinEx CRC Mundi surveys were chosen for interpretation to align with the 20&nbsp;km line-spaced EFTF AusAEM ERC survey (Figure 1).</div><div>The aim of this study was to interpret the AEM conductivity sections to develop a regional understanding of the near-surface stratigraphy and structural architecture. To ensure that the interpretations took into account the local geological features, the AEM conductivity sections were integrated and interpreted with other geological and geophysical datasets, such as boreholes, potential fields, surface and basement geology maps, and seismic interpretations. This approach provides a near-surface fundamental regional geological framework to support more detailed investigations. </div><div>This study interpreted between the ground surface and 500&nbsp;m depth along almost 30,000 line kilometres of nominally 20&nbsp;km line-spaced AEM conductivity sections, across an area of approximately 550,000&nbsp;km2. These interpretations delineate the geo-electrical features that correspond to major chronostratigraphic boundaries, and capture detailed stratigraphic information associated with these boundaries. These interpretations produced approximately 170,000 depth estimate points or approximately 9,100 3D line segments, each attributed with high-quality geometric, stratigraphic, and ancillary data. 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>Results from these interpretations provided 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 interpretations have applications in a wide range of disciplines, such as mineral, energy and groundwater resource exploration, environmental management, subsurface mapping, tectonic evolution studies, and cover thickness, prospectivity, and economic modelling. It is anticipated that these interpretations will benefit government, industry and academia with interest in the geology of the ERC region.</div>

It is increasingly recognised that, to maintain a sustainable pipeline of mineral resources in Australia, future discoveries will need to be made in areas obscured by more recent cover sequences. A major challenge to mineral exploration in covered frontiers is identifying new prospective fairways, and understanding and mapping important metallogenic processes at a range of scales to enable more effective targeting of exploration. Here, we present evidence for a completely buried corridor of interpreted high prospectivity—the East Tennant region—based on synthesis and integration of a diverse range of geoscientific datasets. Key indicators of the region’s potential include lithospheric-scale architecture, elevated electrical conductivity in the crust and mantle, and modelled and demonstrated hydrothermal alteration in the near surface. Multiscale geophysical surveys show evidence for crustal-scale fluid flow along major structures, connecting the mantle with the surface. Although few geological constraints exist in this region, examination of legacy drillcore and geochronology results demonstrates a similar history to rocks known to host mineralisation across the North Australian Craton. These results provide tantalising indications that the under-explored East Tennant region has significant potential to host major mineral systems. <b>Citation: </b>Schofield, A., Clark, A., Doublier, M.P., Murr, J., Skirrow, R., Goodwin, J., Cross, A. J., Pitt, L., Duan, J., Jiang, W., Wynne, P., O’Rourke, A., Czarnota, K., and I. C. Roach., 2020. Data integration for greenfields exploration: an example from the East Tennant region, Northern Territory. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

Water, energy and mineral resources are vital for Australia’s economic prosperity and sustainable development. However, continued supply of these resources cannot be taken for granted. It is widely accepted that the frontier of exploration now lies beneath the Earth’s surface, making characterisation of the subsurface a unifying challenge. Between 2016 and 2020, the $100.5 million Exploring for the Future program focused on addressing this challenge across northern Australia in order to better define resource potential and boost investment. The program applied a multiscale systems approach to resource assessment based on characterisation of the Australian plate from the surface down to its base, underpinned by methodological advances. The unprecedented scale and diversity of new data collected have resulted in many world-first achievements and breakthrough insights through integrated systems science. Through this multi-agency effort, new continental-scale datasets are emerging to further enhance Australia’s world-leading coverage. The program has identified prospective regions for a wide range of resources and pioneered approaches to exploration undercover that can be applied elsewhere. The outcomes so far include extensive tenement uptake for minerals and energy exploration in covered terranes, and development of informed land-management policy. Here, we summarise the key scientific achievements of the program by reviewing the main themes and interrelationships of 62 contributions, which together constitute the Exploring for the Future: extended abstracts volume. <b>Citation:</b> Czarnota, K., Roach, I.C., Abbott, S.T., Haynes, M.W., Kositcin, N., Ray, A. and Slatter, E., 2020. Exploring for the Future: advancing the search for groundwater, energy and mineral resources. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.