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.

<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

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.