This image is a greyscale image of the Total Magnetic Intensity (TMI) Anomaly Image of Australia with Variable Reduction to Pole (VRTP). Total magnetic intensity (TMI) data measures variations in the intensity of the Earth magnetic filed caused by the contrasting content of rock-forming minerals in the Earth crust. Magnetic anomalies can be either positive (field stronger than normal) or negative (field weaker) depending on the susceptibility of the rock. A variable reduction to Pole is aimed at locating magnetic anomalies exactly above their source bodies and without any distortion. The image is created from the 2019 variable reduction to Pole of the TMI grid with a grid cell size of ~3 seconds of arc (approximately 80 m). This image only includes airborne-derived TMI data for onshore and near-offshore continental areas. The image provides a better interpretation of the magnetic data by giving an accurate location of magnetic source bodies.

Total magnetic intensity (TMI) data measures variations in the intensity of the Earth magnetic field caused by the contrasting content of rock-forming minerals in the Earth crust. Magnetic anomalies can be either positive (field stronger than normal) or negative (field weaker) depending on the susceptibility of the rock. The 2019 Total magnetic Intensity (TMI) grid of Australia has a grid cell size of ~3 seconds of arc (approximately 80 m). This grid only includes airborne-derived TMI data for onshore and near-offshore continental areas. Since the sixth edition was released in 2015, data from 234 new surveys have been added to the database, acquired mainly by the State and Territory Geological Surveys. The new grid was derived from a re-levelling of the national magnetic grid database. The survey grids were levelled to each other, and to the Australia Wide Airborne Geophysical Survey (AWAGS), which serves as a baseline to constrain long wavelengths in the final grid. It is estimated that 33 500 000 line-kilometres of survey data were acquired to produce the 2019 grid data, about 2 000 000 line-kilometres more than for the previous edition.

The filtered uranium grid is a derivative of the 2019 radiometric or gamma-ray grid of Australia. The radiometric, or gamma-ray spectrometric method, measures the natural variations in the gamma-rays detected near the Earth's surface as the result of the natural radioactive decay of potassium (K), uranium (U) and thorium (Th). The data are collected on airborne geophysical surveys conducted by Commonwealth, State and Northern Territory Governments and the private sector. The 2019 filtered uranium grid was derived by seamlessly merging over 600 airborne gamma-ray spectrometric surveys. The final grid has a cell size of about 100m (0.001 degrees) and shows uranium element concentrations of the Australia region.

The filtered thorium grid is a derivative of the 2019 radiometric or gamma-ray grid of Australia. The radiometric, or gamma-ray spectrometric method, measures the natural variations in the gamma-rays detected near the Earth's surface as the result of the natural radioactive decay of potassium (K), uranium (U) and thorium (Th). The data are collected on airborne geophysical surveys conducted by Commonwealth, State and Northern Territory Governments and the private sector. The 2019 filtered thorium grid was derived by seamlessly merging over 600 airborne gamma-ray spectrometric surveys. The final grid has a cell size of about 100m (0.001 degrees) and shows thorium element concentrations of the Australia region.

This image is a greyscale image of the Total Magnetic Intensity of Australia. Total magnetic intensity (TMI) data measures variations in the intensity of the Earth magnetic filed caused by the contrasting content of rock-forming minerals in the Earth crust. Magnetic anomalies can be either positive (field stronger than normal) or negative (field weaker) depending on the susceptibility of the rock. The image is created from the 2019 TMI grid with a grid cell size of ~3 seconds of arc (approximately 80 m). This image only includes airborne-derived TMI data for onshore and near-offshore continental areas. The image shows the magnetic response of subsurface features with contrasting magnetic susceptibilities. The image can also be used to locate structural features such as dykes.

The thorium over potassium grid is a derivative of the 2019 radiometric or gamma-ray grid of Australia. The radiometric, or gamma-ray spectrometric method, measures the natural variations in the gamma-rays detected near the Earth's surface as the result of the natural radioactive decay of potassium (K), uranium (U) and thorium (Th). The data are collected on airborne geophysical surveys conducted by Commonwealth, State and Northern Territory Governments and the private sector. The 2019 thorium over potassium was derived by seamlessly merging over 600 airborne gamma-ray spectrometric surveys. The final grid has a cell size of about 100m (0.001 degrees) and is derived from the filtered thorium and potassium grids.

The unfiltered terrestrial dose rate grid is a derivative of the 2019 radiometric or gamma-ray grid of Australia, which is a merge of over 600 individual gamma-ray spectrometric surveys. The radiometric, or gamma-ray spectrometric method, measures the natural variations in the gamma-rays detected near the Earth's surface as the result of the natural radioactive decay of potassium (K), uranium (U) and thorium (Th). The data are collected on airborne geophysical surveys conducted by Commonwealth, State and Northern Territory Governments and the private sector. The unfiltered terrestrial dose rate grid is derived as a linear combination of the unfiltered K, U and Th grids, and has a cell size of about 100m (0.001 degrees).

The unfiltered thorium grid is a derivative of the 2019 radiometric or gamma-ray grid of Australia which is a merge of over 600 individual gamma-ray spectrometric surveys. The radiometric, or gamma-ray spectrometric method, measures the natural variations in the gamma-rays detected near the Earth's surface as the result of the natural radioactive decay of potassium (K), uranium (U) and thorium (Th). The data are collected on airborne geophysical surveys conducted by Commonwealth, State and Northern Territory Governments and the private sector. The 2019 unfiltered thorium grid has a cell size of about 100 m (0.001 degrees) and shows thorium element concentrations of the Australia region.

The uranium squared over thorium grid is a derivative of the 2019 radiometric or gamma-ray grid of Australia. The radiometric, or gamma-ray spectrometric method, measures the natural variations in the gamma-rays detected near the Earth's surface as the result of the natural radioactive decay of potassium (K), uranium (U) and thorium (Th). The data are collected on airborne geophysical surveys conducted by Commonwealth, State and Northern Territory Governments and the private sector. The 2019 uranium squared over thorium was derived by seamlessly merging over 600 airborne gamma-ray spectrometric surveys. The final grid has a cell size of about 100m (0.001 degrees) and is derived from the filtered uranium and thorium grids.

The complete infilled K, eTh and eU grids are based on the Radiometric Map of Australia (radmapv4) 2019 (Poudjom Djomani and Minty, 2019a, b, c) with gaps in coverage infilled using environmental correlation machine learning prediction. The radiometric, or gamma-ray spectrometric method, measures the natural variations in the gamma-rays detected near the Earth's surface as the result of the natural radioactive decay of potassium (K), uranium (U) and thorium (Th). However because Uranium and Thorium abundances are calculated by measuring gamma emission associated with their daughter radionuclides they are typically expressed as equivalent eU and eTh. The 2019 radiometric grid is compiled from airborne geophysical surveys conducted by Commonwealth, State and Northern Territory Governments and the private sector. Over 600 airborne gamma-ray spectrometric surveys were merged and gridded to a cell size of approximately 100m (0.001 degrees) to produce the Radiometric Map of Australia (radmapv4) 2019. Gamma-rays emitted from the surface mainly relate to the mineralogy and geochemistry of the bedrock and weathered materials or regolith. To infill gaps in the national gamma-ray grid (radmapv4 -2019) we have compiled a set of national covariates or predictive datasets that capture landscape processes, regolith and geology that are likely correlated to the distribution of K, eTh and eU at the surface. These datasets include satellite imagery (to map surface mineralogy and vegetation), terrain attributes (e.g. slope, relief), gravity (Lane et al, 2020) and surface geology. A boosted regression tree algorithm called XGBoost (open-source software library for gradient boosting machine learning) was used to train relationships between airborne estimates of K, eTh and eU with the covariate datasets. The training set used the Australia Wide Airborne Geophysical Survey (AWAGS) (Milligan et al., 2009). Local model predictions were generated for gaps in the 2019 version of the national grid by clipping subsets of the AWAGS survey lines and in places extracting additional training survey sites from nearby surveys. The strength of the correlations between the training observation and the covariates were highest in semi-arid areas with decreasing correlations from K through to eTh and eU. Modelled grids of K, eTh and eU were merged with the Radiometric Map of Australia (radmapv4 -2019) using the grid merge module in Intrepid Geophysics software. The first step was to scale the modelled dataset to the reference dataset, then apply a DC shift. The second step was to surface adjust the grid, which computes a two dimensional surface calculated from the differences in its value between the reference grid, it then fits a difference surface with the largest mean signal value and reiterates this process until the difference is within a pre-defined threshold. The third step is to merge the modelled dataset with the Radiometric Map of Australia (radmapv4) 2019, using a feathering process where measured radiometric values are ranked higher over the modelled data. The complete infill radiometric grids have been generated for regolith (including soils) and geological mapping and can be used as a seamless dataset for predictive modelling using machine learning. The product can be seen as an interim dataset until the gaps are filled in through new airborne survey acquisition. It is important to recognise that the infill grids are based on correlations between airborne flight-line estimates of the radioelements and covariate thematic datasets. Responses and patterns observed within these gap areas are therefore not reflecting measurements using the airborne spectrometry. Equally, the covariate approach should not be expected to confidently identify gamma-ray ‘outliers’ or anomalies that have been used in other geophysical survey approaches. Lane, R. J. L., Wynne, P. E., Poudjom Djomani, Y. H., Stratford, W. R., Barretto, J. A., and Caratori Tontini, F., 2020, 2019 Australian National Gravity Grids: Geoscience Australia, eCat Reference Number 133023, https://pid.geoscience.gov.au/dataset/ga/133023 Milligan, P., Minty, B., Richardson, M and Franklin, R. 2009 The Australia-Wide Airborne Geophysical Survey - accurate continental magnetic coverage, ASEG, Extended Abstracts, 2009:1, 1-9 Poudjom Djomani, Y., Minty, B.R.S. 2019a. Radiometric Grid of Australia (Radmap) v4 2019 unfiltered pct potassium. Geoscience Australia, eCat reference number 131978. http://dx.doi.org/10.26186/5dd4a7851e8db Poudjom Djomani Y., Minty, B.R.S. 2019b. Radiometric Grid of Australia (Radmap) v4 2019 unfiltered ppm thorium. Geoscience Australia, ecat reference number 131988. http://dx.doi.org/10.26186/5dd4a821a334d Poudjom Djomani, Y., Minty, B.R.S. 2019c. Radiometric Grid of Australia (Radmap) v4 2019 filtered ppm uranium. Geoscience Australia, eCat reference number 131974. http://dx.doi.org/10.26186/5dd48ee78c980