Cover
Type of resources
Keywords
Publication year
Service types
Topics
-
Geoscience Australia, in partnership with State and Territory Geological Surveys and research organisations, has applied the magnetotelluric (MT) method to image the resistivity structure of the Australian continent over the last decade. Data have been acquired at nearly 5000 stations through the collaborative national AusLAMP survey and regional MT surveys. The data provide valuable information for multi-disciplinary interpretations that incorporate various datasets. Most of these MT data have been released to the public. To date, AusLAMP has been completed ~30% of the national coverage. Data have been acquired at nearly 1000 stations. This pre-competitive dataset will be an essential input to Geoscience Australia’s Exploring for the Future program as well as a valuable resource for researchers to reconstruct the tectonic evolution of the Australian continent. The regional MT surveys have been undertaken across potential mineral/energy provinces and greenfields areas in the Australia continent. A number of regional surveys have been completed recently. The MT data from the poorly understood Southern Thomson Orogen and Coompana region have improved understanding of cover thickness, sub-surface geology, and crustal architecture. The data reduce the uncertainty associated with intersecting the targeted stratigraphy for the pre-competitive stratigraphic drilling program. Comparison with drill-hole information indicates that the technique is capable of identifying major stratigraphic structures and providing cover thickness estimates with reasonable accuracy in regions where there is little surface outcrop and thick cover sequences. The MT data from the Mount Isa inlier in northern Australia provide new insights into basement architecture, the crustal architecture and resource potential in this region. The data reveal some crustal-scale conductivity anomalies which correspond to known major crustal boundaries and faults. Those faults and boundaries are considered the primary factors in the partitioning of mineralisation in the region, with some conductors in the upper crust coinciding with known mineral deposits. Presented at the 24th Electromagnetic Induction Workshop (EMIW) 13-20 August 2018, Helsingør Denmark (https://emiw2018.emiw.org/)
-
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.
-
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.
-
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.
-
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.
-
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.
-
The Exploring for the Future program Virtual Roadshow was held on 7 July and 14-17 July 2020. The Minerals session of the roadshow was held on 14 July 2020 and consisted of the following presentations: Introduction - Richard Blewett Preamble - Karol Kzarnota Surface & Basins or Cover - Marie-Aude Bonnardot Crust - Kathryn Waltenberg Mantle - Marcus Haynes Zinc on the edge: New insights into sediment-hosted base metals mineral system - David Huston Scale reduction targeting for Iron-Oxide-Copper-Gold in Tennant Creek and Mt Isa - Anthony Schofield and Andrew Clark Economic Fairways and Wrap-up - Karol Czarnota
-
<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>
-
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.
-
<div>The interpretation of AusAEM airborne electromagnetic (AEM) survey conductivity sections in the Canning Basin region delineates the geo-electrical features that correspond to major chronostratigraphic boundaries, and captures detailed stratigraphic information associated with these boundaries. This interpretation forms part of an assessment of the underground hydrogen storage potential of salt features in the Canning Basin region based on integration and interpretation of AEM and other geological and geophysical datasets. A main aim of this work was to interpret the AEM to develop a regional understanding of the near-surface stratigraphy and structural geology. This regional geological framework was complimented by the identification and assessment of possible near-surface salt-related structures, as underground salt bodies have been identified as potential underground hydrogen storage sites. This study interpreted over 20,000 line kilometres of 20 km nominally line-spaced AusAEM conductivity sections, covering an area approximately 450,000 km2 to a depth of approximately 500 m in northwest Western Australia. These 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 interpretation produced approximately 110,000 depth estimate points or 4,000 3D line segments, each attributed with high-quality geometric, stratigraphic, and ancillary data. The depth estimate points are formatted for Geoscience Australia’s Estimates of Geological and Geophysical Surfaces database, the national repository for formatted depth estimate points. Despite these interpretations being collected to support exploration of salt features for hydrogen storage, they are also intended for use in a wide range of other disciplines, such as mineral, energy and groundwater resource exploration, environmental management, subsurface mapping, tectonic evolution studies, and cover thickness, prospectivity, and economic modelling. Therefore, these interpretations will benefit government, industry and academia interested in the geology of the Canning Basin region.</div>