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

Abstract

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.

Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data

“…Furthermore, geologic structure mapping serves as a reference for inferring surface geological structure from geophysical survey data. In comparison to traditional methods, machine learning approaches offer enhanced efficiency, accuracy, and greater objectivity [16][17][18][19][20][21][22][23][24].…”

“…By doing so, the geological epochs within this series of sample data can be compared to ensure that the model accurately captures the sequential nature of geological epochs. The geological epoch order loss is calculated as described in Equation (23).…”