Brief description
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.
To enable a sustainable and responsible use of the Earth's subsurface environment, a quantified knowledge of the geological composition and structure of the subsurface is an economic imperative to inform decision-making. Geoscience Australia developed a start-to-end open-source methodology ranging from data acquisition, interpretation and storage to data modelling, to create a national seamless chronostratigraphic framework and predict depth and spatial extent of potential resources (Bonnardot et al., 2020; 2024).
This data package contains a layered depth to sedimentary cover model and associated constraints, that was generated in the Darling-Curnamona-Delamerian (DCD) region (between 27.6‒39⁰ S of latitude and 137.7‒144⁰ E of longitude) to characterise depth and thickness of key stratigraphic sequences, e.g. Cenozoic, Mesozoic, Paleozoic and Neoproterozoic.
The layered cover model integrates the interpretation of depth estimates from stratigraphic logs (Vizy and Rollet, 2024), surface and layered geology, depth to magnetic source estimates (Foss et al., 2024; Hope et al., 2024), and airborne electromagnetic data (Wong et al., 2023) that were consistently stored in a data repository (Estimates of Geophysical and Geological Surfaces, EGGS database). Geoscience Australia’s (GA) Estimates of Geological and Geophysical Surfaces (EGGS) database (Matthews et al., 2020) is the national repository for standardised depth estimate points, where all points are attributed with stratigraphic information populated from the Australian Stratigraphic Units Database (ASUD).
Two sets of depth surfaces were generated using different approaches: 1) interpolation of 4 depth surfaces, e.g. base of Cenozoic, Mesozoic, Paleozoic and Neoproterozoic were generated using the implicit interpolator LoopStructural (Grose et al., 2021) from the open-source Loop 3D modelling platform (loop3d.org) (see Bonnardot et al., 2024 for the methodology) and 2) machine learning algorithm, UncoverML (Wilford et al., 2020) was used to model the depth of the Cenozoic surface. Machine learning allows to learn relationship between datasets and therefore, can provide higher resolution in areas of sparse data points distribution.
The data package includes:
- Depth estimates data point compiled and used for gridding each surface, for the Base Cenozoic, Base Mesozoic, Base Paleozoic and Base Neoproterozoic (Figure 1),
- Four regional depth surface grids generated with LoopStructural for the Base Cenozoic, Base Mesozoic, Base Paleozoic and Base Neoproterozoic (Figure 2).
- One regional depth surface grid generated with UncoverML for the Base Cenozoic.
- Four regional isochore grids generated for the thickness of the Cenozoic, Mesozoic, Paleozoic, Neoproterozoic.
Lineage
Maintenance and Update Frequency: asNeeded
Statement: Data used to generate the surfaces was compiled from multiple sources: 1) horizon interpretation of open file AusAEM airborne electromagnetic (Wong et al., 2023), 2) boreholes markers interpretation extracted from the Estimates of Geophysical and Geological Surfaces (EGGS) database (Mathews et al., 2020) and from the Australian Boreholes Stratigraphic Units Compilation (ABSUC) (Vizy and Rollet, 2024), 4) outcropping geology extracted from the GA’s 1:1M scale surface geology (Raymond et al., 2012) and 5) depth to magnetic source estimates with inferred chronostratigraphic surface attributes derived from solid geology maps (Foss et la., 2024; Hope et al., 2024; Sanchez et al., 2024).
Two sets of depth surfaces were generated using different approaches: 1) interpolation of 4 depth surfaces, e.g. base of Cenozoic, Mesozoic, Paleozoic and Neoproterozoic were generated using the implicit interpolator LoopStructural (Grose et al., 2021) from the open-source Loop 3D modelling platform (loop3d.org) and 2) machine learning algorithm, UncoverML, (Wilford et al., 2020) was used to model the depth of the Cenozoic surface.
The description of the input datasets used to construct these surfaces are in Table 1.
All depth points stored in the EGGS database are available from the Exploring for the Future Portal, at https://portal.ga.gov.au/persona/eftf
Notes
PurposeCharacterising the geological properties and architecture of the subsurface is an economic imperative to effectively manage competing uses of the underground space and mitigate exploration risks under cover though data-driven decision-making. Geoscience Australia developed a start-to-end open-source methodology ranging from data acquisition, interpretation and storage to data modelling, to create a national seamless chronostratigraphic framework and predict depth and spatial extent of potential resources. Central to Geoscience Australia’s subsurface modelling workflow, the Estimates of Geological and Geophysical Surfaces (EGGS) database was designed to help address data variability found in large datasets compilation (Mathews et al., 2020). The EGGS database stores depth estimates consistently compiled and standardised from various sources. Depth estimates in EGGS comply with a set of standards and provide an internally consistent and format agnostic baseline suitable to generate models using various digital applications. For example, each depth estimate is labelled as ‘BASE’ if they characterise the base of a geological era, or ‘WITHIN”, if the depth measurement characterises a stratigraphic unit within a geological era. The depth of a geological interface is also characterised by the overlying and underlying stratigraphic units, where the stratigraphic information adheres to the Australian Stratigraphic Units Database (ASUD; Geoscience Australia and Australian Stratigraphy Commission, 2024). This open-source workflow provides the ability to tailor the layered cover model to any subsurface mapping requirements, such as understanding geological sequences to guide exploration strategies under cover, assess resources potential and their economic viability, mapping groundwater resources and support competing land-use management.
Created: 10 07 2024
Issued: 11 02 2025
text: westlimit=137.70; southlimit=39.00; eastlimit=144.00; northlimit=27.60; projection=GDA94 / MGA zone 54 / projected (EPSG: 28354)
Subjects
AusAEM |
Australia’s Resources Framework |
Cenozoic |
Cover model |
Cover thickness |
Depth to basement |
EFTF – Exploring for the Future |
Estimates of Geological and Geophysical Database (EGGS) |
Exploring for the Future (EFTF) |
Geology not elsewhere classified |
Loop 3D |
Mesozoic |
Neoproterozoic |
New South Wales |
Paleozoic |
Published_External |
South Australia |
UncoverML |
Victoria |
geoscientificInformation |
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Other Information
Download the data (zip) [130.2 MB]
uri :
https://d28rz98at9flks.cloudfront.net/149732/149732_00_1.zip
Download the metadata statement (pdf) [892.0 KB]
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https://d28rz98at9flks.cloudfront.net/149732/149732_01_1.pdf
Identifiers
- DOI : 10.26186/149732
- URI : pid.geoscience.gov.au/dataset/ga/149732
- global : 55ce79b0-98c0-4033-834f-31f9a8017c72
