Maximising the extraction of geological information from geophysical datasets using machine learning

Machine learning and 3D modeling techniques were applied to a high-resolution magneticradiometric dataset collected over the Fletchers Awl copper-gold project in Central Queensland to map the 2D and 3D geology of the project area. Two supervised machine learning methods (Random Forests, Support Vector Machines) were used, both requiring training data to classify the input data into lithologies. After tuning the input data, training points, and model parameters, these methods produced maps that closely resembled the government geology map. An unsupervised method (ISO Cluster Classification) was also tested, which segments the input maps into classes without the use of training data. This produced a reasonable approximation of the mapped geology, but distinct units were grouped together. We found that the best use for the machine learning outputs was as guides for the more traditional geological interpretation of the geophysics data that was undertaken. The interpretation was extended into 3D using wireframing and implicit modeling. A magnetic inversion, drilling data, and gravity maps were also incorporated to guide the 3D mapping. The resulting 2D and 3D maps are great improvements on the existing mapping and have improved the understanding of the mineral system model for the area, allowing more effective exploration targeting.

[OgonnaEmenaha]

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