“…Their thickness varies from a few millimeters to a few centimeters along magnetite cracks. The most prevalent iron mineral in the deposit is magnetite; although, due to supergene and oxidation processes, local magnetite has been martitized along fractures and grain boundaries [ 43 , [89] , [90] , [91] , [92] , [93] , [94] ]. Fig.…”
“…Their thickness varies from a few millimeters to a few centimeters along magnetite cracks. The most prevalent iron mineral in the deposit is magnetite; although, due to supergene and oxidation processes, local magnetite has been martitized along fractures and grain boundaries [ 43 , [89] , [90] , [91] , [92] , [93] , [94] ]. Fig.…”
“…They vary in thickness from a few millimeters to a few centimeters along cracks of magnetite. Magnetite is the most dominant iron mineral in the deposit; although, due to the effects of oxidation and supergene processes, local magnetite has been martitized along grain boundaries and fractures [46,[94][95][96][97][98][99].…”
“…With the advancement of computer technology, semi‐automatic survey‐based geological mapping has progressed from concept (Yu et al., 2012) to practice (Piippo et al., 2022). Machine learning algorithms (MLAs) offer a promising route towards automation.…”
We present an automated approach for inferring surface geological structures from geophysical survey data. Our method employs machine learning, using mapped geological structures as labels and filtered geophysical surveys as reference maps. We compared the performance of the eight main machine learning algorithms and their 32 branches. Applied to the Geological Survey of Victoria's database for the Bendigo Zone, following an appropriate choice of geological features, the 3‐class classification model using subspace K‐nearest neighbour methods achieves a stable and validated 92% accuracy in around 1 min. The fault‐only classification model achieves a stable and validated 97% accuracy in around 6 min. This shows that geological structural features on the surface may be inferred from between one and three of the following geophysical data types: gravity, airborne total magnetic intensity and first vertical derivative of total magnetic intensity. It shows the prospect of machine learning in geological research and suggests that geophysical data combined with machine learning may be useful and efficient in determining the existence of geological structural features.
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