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AEM Assist: A national machine learning tool for airborne electromagnetic...
Output Type: Exploring for the Future Extended Abstract Short Abstract: Airborne electromagnetic surveys are widely used in Australia for mineral exploration, groundwater... -
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State-of-the-art analysis of geochemical data for mineral exploration
Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments, and regolith are typically carried out at continental, regional and local scales. The... -
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High resolution conductivity mapping using regional AEM survey and machine learning
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... -
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National Gravity and Magnetic Derivatives Customised for Data Analysis and...
Purpose This package comprises a set of 86 thematic grids (rasters) derived from national coverages of gravity and magnetic survey data. These datasets provide valuable... -
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Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials...
Disruptions to the global supply chains of critical raw materials (CRM) have the potential to delay or increase the cost of the renewable energy transition. However, for some... -
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National surface and near-surface conductivity grids
A national compilation of airborne electromagnetic (AEM) conductivity–depth models from AusAEM (Ley-Cooper et al. 2020) survey line data and other surveys (see reference list in... -
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Developing optimal spatial predictive model for seabed sand content using...
Seabed sediment predictions at regional and national scales in Australia are mainly based on bathymetry related variables due to the lack of backscatter related data. In this... -
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Digital soil mapping of lithium in Australia
With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses... -
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Find a rock or a rock nearby using Convolution Neural Networks
Output Type: Exploring for the Future Extended Abstract Short Abstract: Most geological mapping either over-estimates the amount of bedrock exposed at the surface or can miss... -
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Uncover-ML: a machine learning pipeline for geoscience data analysis.
The geosciences are a data-rich domain where Earth materials and processes are analysed from local to global scales. However, often we only have discrete measurements at... -
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Surficial and deep earth material prediction from geochemical compositions -...
Prediction of true classes of surficial and deep earth materials using multivariate geospatial data is a common challenge for geoscience modellers. Most geological processes... -
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Unearthing Australia’s subsurface secrets - An integrated FAIR modelling approach
Output Type: Exploring for the Future Extended Abstract Short Abstract: To enable a sustainable and responsible use of the Earth's subsurface environment and accelerate... -
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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... -
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Predictive grids of major oxide concentrations in surface rock and regolith...
Major oxides provide valuable information about the composition, origin, and properties of rocks and regolith. Analysing major oxides contributes significantly to understanding... -
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Petrophysical interpretation and reservoir characterisation on Proterozoic...
The Proterozoic succession in the NDI Carrara 1 drill hole, Northern Territory, consists predominantly of tight shales, siltstones, and calcareous clastic rocks. As part of...
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