<div>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.&nbsp;</div><div>To enable a sustainable and responsible use of the Earth's subsurface environment,<strong> </strong>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<strong> </strong>open-source methodology ranging from data acquisition, interpretation and storage<strong> </strong>to data modelling, to create a national seamless chronostratigraphic framework and predict depth and spatial extent of potential resources (Bonnardot et al., 2020; 2024).&nbsp;&nbsp;</div><div>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.&nbsp;</div><div>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).&nbsp;</div><div>&nbsp;</div><div>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.&nbsp;</div><div>&nbsp;</div><div>The data package includes:&nbsp;</div><div>- Depth estimates data point compiled and used for gridding each surface, for the Base Cenozoic, Base Mesozoic, Base Paleozoic and Base Neoproterozoic (Figure 1),&nbsp;</div><div>- Four regional depth surface grids generated with LoopStructural for the Base Cenozoic, Base Mesozoic, Base Paleozoic and Base Neoproterozoic (Figure 2).&nbsp;</div><div>- One regional depth surface grid generated with UncoverML for the Base Cenozoic.&nbsp;&nbsp;</div><div>- Four regional isochore grids generated for the thickness of the Cenozoic, Mesozoic, Paleozoic, Neoproterozoic.&nbsp;</div><div><br></div>