Murray Basin Cenozoic thickness

The Murray Basin is a saucer-shaped basin with flat-lying Cenozoic sediments up to approximately 600 m thickness (Brown and Stephenson, 1991). Constraints on the thickness of the Murray Basin have been compiled from: drillholes, reflection seismic profile interpretations, refraction seismic profiles and depth to magnetic basement estimates (Target_type.pdf). Target depths were sourced from Geoscience Australia, the national Groundwater Information System database (Http://www.bom.gov.au/water/groundwater/ngis/), the Geological Survey of Victoria (http://earthresources.vic.gov.au/earth-resources/geology-of-victoria/geological-survey-of-victoria) and the Geological Survey of South Australia (http://www.minerals.statedevelopment.sa.gov.au/geoscience/geological_survey). In addition, some of the magnetic depth estimates used data from McLean (2010). To constrain the thickness of Cenozoic cover where sediments were either absent or very thin we generated shallow-depth values in areas with post-Cenozoic geology and high topographic relief. In all, 5436 depth estimates were compiled (Target_depths.xlsx). The input datasets have been used to generate two predictive models of the thickness of Cenozoic sediments within the Murray Basin. The first model uses kriging of the depth estimates to generate a gridded surface using a local-area linear variogram model as a means of interpolating between constraints (Murray_Basin_kriging_Cenozoic_thickness.pdf; Murray_Basin_krig.tif -floating value grid). The second model uses a machine-learning approach where correlations between 17 supplementary datasets and 5436 depth estimates are used to derive a predictive model. We used a supervised learning algorithm known as Gaussian Process (GP) to generate the integrated predictive model. Gaussian Process is a non-parametric probabilistic approach to learning. It uses kernel functions to measure the similarity between points and predict values not seen from the training data (see Read_Me_GP.rtf). The supplementary datasets used in the model are listed in Table 1 and model variable settings can be found in read_me.rtf (Murray_Basin_GP_Cenozoic_thickness.pdf; Murray_Basin_GP_model.tif -floating value grid). Both approaches delineate the overall structure, geometry and thickness of the Murray Basin. The advantage of the machine learning approach is that it learns relationships between the depth and supplementary datasets which allow predictions in areas with limited constraints.ReferencesBrown, C. M. and Stephenson, A. E., 1991, Geology of the Murray Basin, southeastern Australia, Canberra, Bureau of Mineral Resources Bulletin 235, 430 p.McLean, M.A., 2010. Depth to Palaeozoic basement of the Gold Undercover region from borehole and magnetic data. GeoScience Victoria Gold Undercover Report 21. Department of Primary Industries, Victoria.Table 1. Supplementary input datasets used in predictive estimation of Murray Basin thickness, utilising a machine learning method    Covariates*    Description1    Latitude    Gridded latitude values2    Longitude    Gridded longitude values3    Elevation    Terrain elevation – 90m shuttle DEM4    Distance from bedrock    Euclidean distance from outcrop geology units older than Cenozoic5    Gravity     Terrain and isostatic corrected Bouguer gravity6    Gravity 1228    Upward continued gravity at 1228 metres7    Gravity 2407    Upward continued gravity at 2407 metres8    Gravity 6605    Upward continued gravity at 6605 metres9    Gravity 18124    Upward continued gravity at 18124 metres10    Gravity 35524    Upward continued gravity at 35524 metres11    Gravity 49734    Upward continued gravity at 49734 metres12    Gravity 97479    Upward continued gravity at 97479 metres13    Gravity – 1k     Isostatically corrected gravity subtracted from upward continued gravity at 1000 metres14    Magnetics 5km    Upward continued magnetic anomaly grid at 5 km15    Magnetic 10km    Upward continued magnetic anomaly grid at 10 km16    Magnetic 5-10km    Upward continued 5km magnetic anomaly grid subtracted from upward continued 10 km magnetic anomaly grid17    Magnetic basement    Depth to magnetic basement using the tilt method. *Primary datasets including gravity, magnetics and surface geology sourced from Geoscience Australia http://www.ga.gov.au/data-pubs/mapsElevation dataset used the 3 second (~90m) Shuttle Radar Topography Mission (SRTM) digital elevation model. https://pid.geoscience.gov.au/dataset/ga/72760.