A Machine learning approach for the magnetic data interpretation of 2-D dipping dike

2-D dipping dike model is often used in the magnetic anomaly interpretations of mineral exploration and regional geodynamic studies. However, the conventional interpretation techniques used for modeling the dike parameters are quite challenging and time-consuming. In this study, a fast and efficient inversion algorithm based on machine learning (ML) techniques such as K-Nearest Neighbors (KNN), Random Forest (RF), and XGBoost is developed to interpret the magnetic anomalies produced by the 2-D dike body. The model parameters estimated from these methods include the depth to the top of the dike (z), half-width (d), Amplitude coefficient (K), index angle (alpha), and origin (x(0)). Initially, ML models are trained with optimized hyper-parameters on simulated datasets, and their performance is evaluated using Mean absolute error (MAE), Root means squared error (RMSE), and Squared correlation (R2). The applicability of the ML algorithms was demonstrated on the synthetic data, including the effect of noise and nearby geological structures. The results obtained for synthetic data showed good agreement with the true model parameters. On the noise-free synthetic data, XGBoost better predicts the model parameters of dike than KNN and RF. In comparison, its performance decreases with increasing the percentage of noise and geological complexity. Further, the validity of the ML algorithms was also tested on the four field examples: (i) Mundiyawas-Khera Copper deposit, Alwar Basin, (ii) Pranhita-Godavari (P-G) basin, India, (iii) Pima Copper deposit of Arizona, USA, and (iv) Iron deposit, Western Gansu province China. The obtained results also agree well with the previous studies and drill-hole data.