Authors / CoAuthors
Wilford, J. | LeyCooper, Y. | Basak, S. | Czarnota, K.
Abstract
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.
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document
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146380
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Cnr Jerrabomberra Ave and Hindmarsh Dr GPO Box 378
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- ( Discipline )
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- remote sensing
- ( Discipline )
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- geophysics
- theme.ANZRC Fields of Research.rdf
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- EARTH SCIENCES
- ( Discipline )
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- AEM
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- Conductivity
- ( Discipline )
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- machine learning
- ( Project )
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- Exploring for the Future
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- EFTF
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- Australia's Resources Framework
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- Published_External
Publication Date
2022-03-10T23:42:05
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High resolution conductivity mapping using regional AEM survey and machine learning- Extended abstract
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This publication was produced as part of Geoscience Australia's Exploring for the Future Program. It was presented at the Exploring for the Future 2022 Showcase.
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[-22.00, -9.0069, 111.3601, 154.9084]
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