Inferring geological structural features from geophysical and geological mapping data using machine learning algorithms

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. We used machine learning algorithms (MLAs) to automatically interpret filtered geophysical survey data from the Bendigo Zone, Victoria, Australia. After training on the geophysical survey data, the MLAs successfully inferred the presence of geological boundaries, such as faults, on the surface. We compared the performance of the main types of MLA and found that a subspace K ‐nearest neighbour algorithm provided an accurate and stable solution, in a timeframe that compares well with human analysis.