Airborne electromagnetic data generated by the AusAEM Survey are shown to map mineral deposit host rocks and regional geological features within the AusAEM Survey area. We have developed new functionality in Geoscience Australia’s sample-by-sample layered earth inversion algorithm, allowing inversion of the magnitude of the combined vector sum of the X- and Z-components of TEMPEST AEM data. This functionality improves the clarity of inverted interpretation products by reducing the degree of along-line incoherency inherent to stitched 1D inversions. The new inversion approach improves the interpretability of sub-horizontal conductors, allowing better mapping of geological features under cover. Examples of geological mapping by the AusAEM survey highlight the utility of wide line spacing, regional AEM surveying to improve geological, mineral systems and groundwater resource understanding in the regions flanking outcropping mineral deposit host rocks in northern Australia. Presented at the 2019 Australasian Exploration Geoscience Conference

<div><strong>Output Type: </strong>Exploring for the Future Extended Abstract</div><div><br></div><div><strong>Short Abstract: </strong>Most geological mapping either over-estimates the amount of bedrock exposed at the surface or can miss local bedrock exposures in geological units describing cover materials (i.e. alluvium and colluvium). A machine learning, Convolutional Neural Network (CNN) has been applied to detect outcrops (exposed bedrock at the earth’s surface) and areas of very shallow cover over bedrock (i.e. sub-crop) at one meter resolution. We used a multi-feature training dataset consisting of sites associated with urban areas, roads, outcrops, waterbodies, soil (includes bare soil and soil covered by green and dry vegetation), trees, and shadows. Even though we were only interested in mapping outcrop, a multi-criteria label set significantly improved overall accuracy of the model. The explanatory variables or covariates included high-resolution satellite imagery, Sentinel-2 imagery, and terrain derivatives. The modelling approach was tested over an area in central West NSW, Australia. Labels were split into 80% for training and 20% for out-of-sample validation. Spatial K-groups were used in the training set to minimize auto-spatial correlation between neighbouring points and reduce the potential for overfitting. Two CNN model architectures were evaluated: Simple-Net and UNet. The Simple-Net structure consists of 2D Convolution layer and flatten layer, whereas the UNet architecture includes a mixture of 2D convolution layer, max pooling, up sampling and flatten layer. These models were tested with and without the use of high-resolution imagery. The UNet model incorporating high resolution imagery gave the best results (accuracy of 0.841 and an F1 score of 0.814), compared with Simple-Net (accuracy of 0.823 and an F1 score of 0.786). However, the Simple-Net’s model without the incorporation of high-resolution imagery was a slight improvement over the UNet architecture and due to the lack of national coverage for high-resolution imagery, the Simple-Net model offers better scalability. The detection of outcrop/sub-crop has broad application in improving the spatial explicitness of existing geological maps, improving sample detection and interpretation of litho-stratigraphy and geochemistry. High spatial resolution of outcrop/sub-crop also has implications in the agricultural and civil engineering sectors, ecology and in understanding surface and near-surface hydrological systems. After this proof-of-concept phase we plan to up-scale the approach nationally using a more representative labels and national covariates.</div><div><br></div><div><strong>Citation: </strong>Du, Z., Wilford, J. &amp; Roberts, D., 2024. Find a rock or a rock nearby using Convolution Neural Networks. In: Czarnota, K. (ed.) Exploring for the Future: Extended Abstracts. Geoscience Australia, Canberra. https://doi.org/10.26186/149631</div>