Product catalogue
High resolution conductivity mapping using regional AEM survey and machine learning.
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
Simple
Identification info
- Date (Creation)
- 2022-05-10T05:03:36
- Date (Publication)
- 2022-06-27T00:44:17
- Date (Revision)
- 2022-06-27T04:59:15
- Date (Revision)
- 2022-08-02T05:25:10
- Edition
-
2.0.0
- Citation identifier
- Geoscience Australia Persistent Identifier/https://pid.geoscience.gov.au/dataset/ga/146163
- Citation identifier
- Digital Object Identifier/https://dx.doi.org/10.26186/146163
- Cited responsible party
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Role Organisation / Individual Name Details Owner Commonwealth of Australia (Geoscience Australia)
Voice Author Wilford, J.
MEG Internal Contact Co-author LeyCooper, Y.
MEG Internal Contact Co-author Basak, S.
PSCD Internal Contact Co-author Czarnota, K.
MEG Internal Contact Publisher Commonwealth of Australia (Geoscience Australia)
Voice
- Purpose
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Provide high resolution conductivity depth predictions to support soil mapping, geological mapping, natural resource assessment, groundwater assessment and mineral exploration.
- Point of contact
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Role Organisation / Individual Name Details Resource provider Minerals, Energy and Groundwater Division
External Contact Point of contact Commonwealth of Australia (Geoscience Australia)
Voice Point of contact LeyCooper, Y.
MEG Internal Contact
- Topic category
-
- Geoscientific information
Extent
))
Temporal extent
- Time period
- 2018-05-01
- Maintenance and update frequency
- Not planned
Resource format
- Title
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Product data repository: Various Formats
- Website
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Data Store directory containing the digital product files
Data Store directory containing one or more files, possibly in a variety of formats, accessible to Geoscience Australia staff only for internal purposes
- Keywords
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AEM
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- theme.ANZRC Fields of Research.rdf
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SOIL SCIENCES
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ENVIRONMENTAL SCIENCES
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AGRICULTURE, LAND AND FARM MANAGEMENT
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EARTH SCIENCES
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- Discipline
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geophysics
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- Theme
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Conductivity
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- Keywords
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machine learning
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- Theme
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Regolith / soils
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- Keywords
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Published_External
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Resource constraints
- Title
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Creative Commons Attribution 4.0 International Licence
- Alternate title
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CC-BY
- Edition
-
4.0
- Access constraints
- License
- Use constraints
- License
Resource constraints
- Title
-
Australian Government Security ClassificationSystem
- Edition date
- 2018-11-01T00:00:00
- Classification
- Unclassified
Associated resource
- Association Type
- Operated on by
- Title
-
Machine Learning Models WMS
- Citation identifier
- 146039
- Citation identifier
- 958f3685-d7c8-4874-8601-8a4bfd84c676
Associated resource
- Association Type
- Operated on by
- Title
-
Machine Learning Models WCS
- Citation identifier
- 146040
- Citation identifier
- 26cc9c00-dc16-4b6a-8c37-a624b17cb547
- Language
- English
- Character encoding
- UTF8
Distribution Information
- Distributor contact
-
Role Organisation / Individual Name Details Distributor Commonwealth of Australia (Geoscience Australia)
Voice
- OnLine resource
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Download 0-50cm (tif) [5.6 GB]
Download 0-50cm (tif) [5.6 GB]
- Distribution format
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tif
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- OnLine resource
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Download 9-11m (tif) [5.6 GB]
Download 9-11m (tif) [5.6 GB]
- Distribution format
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tif
- File decompression technique
-
unzip
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- OnLine resource
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Download 22-27m (tif) [5.2 GB]
Download 22-27m (tif) [5.2 GB]
- Distribution format
-
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tif
- File decompression technique
-
unzip
-
- OnLine resource
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Download covariate shift (tif) [1.7 GB]
Download covariate shift (tif) [1.7 GB]
- Distribution format
-
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tif
- File decompression technique
-
unzip
-
- OnLine resource
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Machine Learning Models WMS
Machine Learning Models WMS
- Distribution format
-
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OGC:WMS
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- OnLine resource
-
Machine Learning Models WCS
Machine Learning Models WCS
- Distribution format
-
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OGC:WCS
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Resource lineage
- 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
- Hierarchy level
- Dataset
- Description
-
Please refer to the lineage section.
Metadata constraints
- Title
-
Australian Government Security ClassificationSystem
- Edition date
- 2018-11-01T00:00:00
- Classification
- Unclassified
Metadata
- Metadata identifier
-
urn:uuid/8df7fe67-1d3d-4881-8cf5-fa00f141ebd1
- Title
-
GeoNetwork UUID
- Language
- English
- Character encoding
- UTF8
- Contact
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Role Organisation / Individual Name Details Point of contact Geoscience Australia - Client Services
Voice Point of contact LeyCooper, Y.
MEG Internal Contact
Type of resource
- Resource scope
- Dataset
Alternative metadata reference
- Title
-
Geoscience Australia - short identifier for metadata record with
uuid
- Citation identifier
- eCatId/146163
- Date info (Revision)
- 2018-05-01T11:20:35
- Date info (Creation)
- 2018-05-01T11:20:35
Metadata standard
- Title
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AU/NZS ISO 19115-1:2014
Metadata standard
- Title
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ISO 19115-1:2014
Metadata standard
- Title
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ISO 19115-3
- Title
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Geoscience Australia Community Metadata Profile of ISO 19115-1:2014
- Edition
-
Version 2.0, September 2018
- Citation identifier
- https://pid.geoscience.gov.au/dataset/ga/122551