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.
Maintenance and Update Frequency: asNeeded
Statement: Data used to generate the surfaces were compiled from: 1) horizon interpretation of open file AusAEM1 airborne electromagnetic (Wong et al., 2020), 2) South Nicholson seismic reflection surveys acquired as part of the Exploring for the Future (EFTF) program (Carr et al., 2020), 3) boreholes markers interpretation extracted from the Estimates of Geophysical and Geological Surfaces (EGGS) database (Matthews et al., 2020), 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 (Meixner et al., 2016; Stewart et al., 2020).
Interpretation of the AusAEM1 AEM survey provides semi-continuous depth measurements throughout the region. Where possible, the depths to the bases of Cenozoic, Mesozoic, Paleozoic and Neoproterozoic have been interpreted, and the overlying and underlying units have been documented, which potentially demonstrates if the contacts are conformable or unconformable (Wong et al., 2020). Interpretation of 2D reflection seismic data (Carr et al., 2020) are based on South Nicholson Survey lines 17GA-SN1, 17GA-SN2, 17GA-SN3, 17GA-SN4 and 17GA-SN5 (Fomin et al., 2020). Seismic horizon xyz data were exported from seismic interpretation software in depth domain (PSDM). Stratigraphic borehole depth interpretations have been compiled including data from existing stratigraphic, mineral, coal, coal seam gas, geothermal, groundwater, petroleum and water boreholes across Queensland and the Northern Territory (Matthews et al., 2020). Depth to magnetic top estimates have been assessed using magnetic Spectral, Naudy (Automag) and Targeted Magnetic Inversion Modelling (TMIM) methods (Czarnota et al., 2016). All points, have been geologically attributed using updated solid geology maps and/or GA’s provinces database (Stewart et al., 2013; Stewart et al., 2020).
When interpreting the depth estimates, we record at each data point the names of the surface as well as those of the overlying and underlying stratigraphic units (http://www.ga.gov.au/data-pubs/datastandards/stratigraphic-units) for each point in the database. This enables unconformities that span large time intervals to be identified even if the surface name does not capture the age span appropriately. We also capture minimum thickness constraints on stratigraphic units, such as the depth of drill holes that terminate within stratigraphic units and the depth to magnetic bodies located beneath unconformities (Bonnardot et al., 2020; Matthews et al., 2020).
The confidence level in the depth estimates from the different datasets is variable due to the method used to image the subsurface: boreholes and outcropping geology are generally well constrained and considered to be of high confidence, while airborne electromagnetic, seismic reflection and depth to magnetic sources are derived from geophysical inferences and are generally considered to be of lower confidence. Uncertainties in the depth estimates are also subject to the interpreter and the level of resolution in the interpretation (Bonnardot et al., 2020; Matthews et al., 2020).
The description of the input datasets used to construct these surfaces are in Table 1.
All depth points stored in the EGGS database and data interpretation for AusAEM1 airborne electromagnetic and seismic reflection surveys are available from the Exploring for the Future Portal, at https://portal.ga.gov.au/persona/eftf.
The grids have been created using a standard interpolation minimum-curvature algorithm with the tension set to zero, a slope estimation type, a bicubic interpolation method, a cell size of 4000 m and a smoothed filter Barlett7 operator. All the interpolated surfaces are in the following projection:
WGS 84 / Australian Centre for Remote Sensing Lambert (EPSG 4462)
Geographic CRS: WGS 84 (EPSG 4326)
Projection name: Australian Centre for Remote Sensing Lambert Conformal Projection (EPSG 4460)
Projection type: Lambert Conic Conformal (2SP)
Delivery of pre-competitve data to the public as part of EFTF program. This data package provides a framework to assess depth and spatial extent of potential resources, including minerals, hydrocarbons and groundwater, as well as an input to economic models of the viability of potential resource development.