High resolution conductivity mapping using regional AEM survey and machine learning.

Brief description

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; 1. 0-50cm depth interval 0_50cm_median.tif; 0_50_upper.tif; 0_50_lower.tif 2. 9-11m depth interval 9_11m_median.tif; 9_11m_upper.tif; 9_11m_lower.tif 3. 22-27m depth interval 22_27_median.tif; 22_27_upper.tif; 22_27_lower.tif 4. 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

Lineage

Maintenance and Update Frequency: notPlanned

Statement: The conductivity grids are derived from a covariate machine learning approach that establishers predictive correlations between the AEM conductivity depth estimates and a suite of environmental raster's. The conductivity depth estimates are derived from the regional (20km line spaced) AusAEM survey data (Ley-cooper and Brodie 2020) . 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). The environmental raster or covariates include terrain derivatives, gamma radiometric, satellite imagery (basest earth) and climate surfaces. The gradient boosted tree algorithm was use to generate the prediction models. The training cross-validation step used label clusters or Kgroups to minimise over-fitting. The median of the models provided the conductivity prediction and the upper and lower percentiles (95th and 5th) provides a measure model uncertainty. Grids show conductivity (S/m) in log 10 units. Ley-Cooper A. Y. & Brodie R. C., 2020. AusAEM: Imaging the nearsurface from the World’s Largest Airborne Electromagnetic Survey. In: Czarnota K., et al. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia. http://dx.doi.org/10.11636/134528. Brodie R. C. & Richardson M., 2015. Open source software for 1D airborne electromagnetic inversion. ASEG Extended Abstracts 2015:1–3. https://doi.org/10.1071/ASEG2015ab197

Notes

Purpose
Provide high resolution conductivity depth predictions to support soil mapping, geological mapping, natural resource assessment, groundwater assessment and mineral exploration.