Magnetotelluric (MT) data allow geoscientists to investigate the link between mineralisation and lithospheric-scale features and processes. In particular, the highly conductive structures imaged by MT data appear to map the pathways of large-scale palaeo-fluid migration, which is an important element of several mineral systems. New data were collected as part of the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP) under Geoscience Australia Exploring for the Future (EFTF) program in northern Australian. We use this dataset to demonstrate that the MT method is a valuable tool for mapping lithospheric-scale features and for selecting prospective areas for mineral exploration. Our results image a number of major conductive structures at depths up to ~200 km or deeper in the survey region, for example, the Carpentaria Conductivity Anomaly in east of Mount Isa; and the Tanami Conductive Anomaly along the Willowra Suture Zone. These significant anomalies are lithospheric- scale highly conductive structures, and show spatial correlations with major suture zones and known mineral deposits. These results provide important first-order information for lithospheric architecture and possible large footprint of mineral systems. Large-scale crustal/mantle conductivity anomalies mapping fluid pathways associated with major sutures/faults may have implications for mineral potential. These results provide evidence that some mineralisation occurs at the gradient of or over highly conductive structures at lower crustal and lithospheric mantle depths. These observations provide a powerful means of highlighting greenfields for mineral exploration in under-explored and covered regions.

This service delivers airborne electromagnetics (AEM) derived conductivity grids for depth intervals representing the top 22 layers from AEM modelling in the West Musgrave region (https://dx.doi.org/10.26186/147969). The grids were generated from the AEM conductivity models released as part of the Western Resource Corridor AusAEM survey (https://dx.doi.org/10.26186/147688), the Earaheedy and Desert Strip AusAEM survey (https://pid.geoscience.gov.au/dataset/ga/145265) and several industry surveys (https://dx.doi.org/10.26186/146278) from the West Musgraves region. The AEM conductivity models resolve important subsurface features for assessing the groundwater system including lithological boundaries, palaeovalleys and hydrostatigraphy.

This service delivers airborne electromagnetics (AEM) derived conductivity grids for depth intervals representing the top 22 layers from AEM modelling in the West Musgrave region (https://dx.doi.org/10.26186/147969). The grids were generated from the AEM conductivity models released as part of the Western Resource Corridor AusAEM survey (https://dx.doi.org/10.26186/147688), the Earaheedy and Desert Strip AusAEM survey (https://pid.geoscience.gov.au/dataset/ga/145265) and several industry surveys (https://dx.doi.org/10.26186/146278) from the West Musgraves region. The AEM conductivity models resolve important subsurface features for assessing the groundwater system including lithological boundaries, palaeovalleys and hydrostatigraphy.

From June 23rd to November 4th 2016 Geotech Ltd. carried out a helicopter-borne geophysical survey over part of East Isa in Queensland (figure 1). Operations were based at Cloncurry, Queensland. The traverse lines were flown in an east to west (N 90° E azimuth) direction with 2km and 2.5km traverse line spacings, with three Tie lines flown perpendicular to the traverse lines. During the survey the helicopter was maintained at a mean altitude of 76 metres above the ground with an average survey speed of 90 km/hour. This allowed for an actual average EM Transmitter-receiver loop terrain clearance of 38 metres and a magnetic sensor clearance of 68 metres. The principal geophysical sensors included a versatile time domain electromagnetic (VTEMTMPlus) full receiver-waveform system, and a caesium magnetometer. Ancillary equipment included a GPS navigation system, laser and radar altimeters, and inclinometer. A total of 15697 line-kilometres of geophysical data were acquired during the survey. The electromagnetic system is a Geotech Time Domain EM (VTEMplus) with full receiver-waveform streamed data recording at 192 kHz. The "full waveform VTEM system" uses the streamed half-cycle recording of transmitter current and receiver voltage waveforms to obtain a complete system response calibration throughout the entire survey flight. The VTEM transmitter loop and Z-component receiver coils are in a concentric-coplanar configuration and their axes are nominally vertical. An X-component receiver coil is also installed in the centre of the transmitter loop, with its axis nominally horizontal and in the flight line direction. The receiver coils measure the dB/dt response, and a B-Field response is calculated during the data processing. In-field data quality assurance and preliminary processing were carried out on a daily basis during the acquisition phase. Preliminary and final data processing, including generation of final digital data products were undertaken from the office of Geotech Ltd. in Aurora, Ontario. A set of Conductivity Depth Images (CDI) were generated using EM Flow version 3.3, developed by Encom Technologies Pty Ltd. A total of forty-five (45) dB/dt Z component channels, starting from channel 4 (21 µsec) to channel 48 (10667 µsec), were used for the CDI calculation. An averaged waveform at the receiver was used for the calculation since it was consistent for the majority of the flights with minor deviation from the average. Digital data includes all electromagnetic and magnetic data, conductivity imaging products, mulitplots plus ancillary data including the waveform.

This service delivers airborne electromagnetics (AEM) derived conductivity grids for depth intervals representing the top 22 layers from AEM modelling in the West Musgrave region (https://dx.doi.org/10.26186/147969). The grids were generated from the AEM conductivity models released as part of the Western Resource Corridor AusAEM survey (https://dx.doi.org/10.26186/147688), the Earaheedy and Desert Strip AusAEM survey (https://pid.geoscience.gov.au/dataset/ga/145265) and several industry surveys (https://dx.doi.org/10.26186/146278) from the West Musgraves region. The AEM conductivity models resolve important subsurface features for assessing the groundwater system including lithological boundaries, palaeovalleys and hydrostatigraphy.

Improvements in discovery and management of minerals, energy and groundwater resources are spurred along by advancements in surface and subsurface imaging of the Earth. Over the last half decade Australia has led the world in the collection of regionally extensive airborne electromagnetic (AEM) data coverage, which provides new constraints on subsurface conductivity structure. Inferring geology and hydrology from conductivity is non-trivial as the conductivity response of earth materials is non-unique, but careful calibration and interpretation does provide significant insights into the subsurface. To date utility of this new data is limited by its spatial extent. The AusAEM survey provides conductivity constraints every 12.5 m along flight lines with no constraints across vast areas between flight lines spaced 20 km apart. Here we provide a means to infer the conductivity between flight lines as an interim measure before infill surveys can be undertaken. We use a gradient boosted tree machine learning algorithm to discover relationships between AEM conductivity models across northern Australia and other national data coverages for three depth ranges: 0–0.5 m, 9–11 m and 22–27 m. The predictive power of our models decreases with depth but they are nevertheless consistent with our knowledge of geological, landscape evolution and climatic processes and an improvement on standard interpolation methods such as kriging. Our models provide a novel complementary methodology to gridding/interpolating from AEM conductivity alone for use by the mining, energy and natural resource management sectors. <b>Citation: </b>Wilford J., Ley-Cooper Y., Basak S., & Czarnota K., 2022. High resolution conductivity mapping using regional AEM survey and machine learning. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, https://dx.doi.org/10.26186/146380.

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