3bca0f44-5994-4aa3-bb80-17c1aa24d21a urn:uuid eng UTF-8 nonGeographicDataset nonGeographicDataset 2025-10-16T23:56:50 creation ISO 19115 2016 High resolution conductivity mapping using regional AEM survey and machine learning. 3bca0f44-5994-4aa3-bb80-17c1aa24d21a The AEM method measures regolith and rocks' bulk subsurface electrical conductivity, typically to a depth of several hundred meters. AEM survey data is widely used in Australia for mineral exploration (i.e. mapping undercover and detection of mineralisation), groundwater assessment (i.e. hydro-stratigraphy and water quality) and natural resource management (i.e. salinity assessment). Geoscience Australia (GA) has flown Large regional AEM surveys over Northern Australia, including Queensland, Northern Territory and Western Australia. The surveys were flown nominally at 20-kilometre line spacing, using the airborne electromagnetic systems that have signed technical deeds of staging with GA to ensure they can be modelled quantitatively. Geoscience Australia commissioned the survey as part of the Exploring for the Future (EFTF) program. The EFTF program is led by Geoscience Australia (GA), in collaboration with the Geological Surveys of the Northern Territory, Queensland, South Australia and Western Australia, and is investigating the potential mineral, energy and groundwater resources in northern Australia and South Australia. We have used a machine learning modelling approach that establishes predictive relationships between the inverted flight-line modelled conductivity with a suite of national environmental and geological covariates. These covariates include terrain derivatives, gamma-ray radiometric, geological maps, climate derived surfaces and satellite imagery. Conductivity-depth values were derived from a single model using GA's deterministic 1D smooth-30-layer layered-earth-inversion algorithm. (Brodie and Richardson 2015). Three conductivity depth interval predictions are generated to interpolate the actual modelled conductivity data, which is 20km apart. These depth slices include a 0-50cm, 9-11m and 22-27m depth prediction. Each depth interval was modelled and individually optimised using the gradient boosted tree algorithm. The training cross-validation step used label clusters or groups to minimise over-fitting. Many hundreds of conductivity models are generated (i.e. ensemble modelling). Here we use the median of the models as the conductivity prediction and the upper and lower percentiles (95th and 5th) to measure model uncertainty. Grids show conductivity (S/m) in log 10 units. Reported out-of-sample r-squares for each interval in order of increasing depth are 0.74, 0.64, and 0.67. A decline in model performance with increasing depth was expected due to the decrease in suitable covariates at greater depths. Modelled conductivities seem to be consistent with the geological, regolith, geomorphological, and climate processes in the study area. The conductivity grids are at the resolution of the covariates, which have a nominal pixel size of 85 meters. Datasets in this data package include; 0-50cm depth interval 0_50cm_median.tif; 0_50_upper.tif; 0_50_lower.tif 9-11m depth interval 9_11m_median.tif; 9_11m_upper.tif; 9_11m_lower.tif 22-27m depth interval 22_27_median.tif; 22_27_upper.tif; 22_27_lower.tif Covariate shift; Cov_shift.tif (higher values = great shift in covariates) Reference: Ross C Brodie & Murray Richardson (2015) Open Source Software for 1D Airborne Electromagnetic Inversion, ASEG Extended Abstracts, 2015:1, 1-3, DOI: 10.1071/ ASEG2015ab197 {"type": "Polygon", "coordinates": [[[112.0, -24.0], [154.0, -24.0], [154.0, -9.0], [112.0, -9.0], [112.0, -24.0]]]} 112.0 154.0 -24.0 -9.0 AEM AGRICULTURE, LAND AND FARM MANAGEMENT Conductivity EARTH SCIENCES ENVIRONMENTAL SCIENCES Published_External Regolith / soils SOIL SCIENCES geophysics machine learning Download 0-50cm (tif) [5.6 GB] documentDigital Download 0-50cm (tif) [5.6 GB] ZIP Download 9-11m (tif) [5.6 GB] documentDigital Download 9-11m (tif) [5.6 GB] ZIP Download 22-27m (tif) [5.2 GB] documentDigital Download 22-27m (tif) [5.2 GB] ZIP Download covariate shift (tif) [1.7 GB] documentDigital Download covariate shift (tif) [1.7 GB] ZIP Machine Learning Models WMS documentDigital Machine Learning Models WMS WMS Machine Learning Models WCS documentDigital Machine Learning Models WCS WCS