Reliable and efficient magnetic data inversion for resource detection using a hybrid bat algorithm

Accurate estimation of subsurface parameters is a critical objective in geophysical exploration. This study introduces an innovative hybrid algorithm integrating the Bat Algorithm (BA) with the Fourth Horizontal Gradient (FHG) to optimize the estimation of geometric parameters (depth, amplitude coefficient, shape factor, source origin, and magnetization angle) from magnetic field data. The FHG enhances the resolution of magnetic anomalies by mitigating regional field effects, while the BA efficiently navigates the parameter space to accurately delineate subsurface structures modeled as simplified geometric. This approach prioritize geometric parameters to define the spatial configuration of magnetic sources, assuming constant petrophysical properties (e.g., magnetization intensity, susceptibility contrast) to simplify the inversion process. The algorithm's robustness and effectiveness were extensively evaluated using synthetic magnetic datasets under both noise-free and with 10% Gaussian noise conditions. The findings demonstrate the method's capability to achieve accurate parameter estimation even in noisy environments. The proposed approach was further assessed using real magnetic profile data acquired from the Faro Mine Complex in Yukon, Canada. The estimated subsurface parameters closely match with well data and prior studies, emphasizing the algorithm's practical effectiveness. This integrated approach significantly advances the interpretation of magnetic datasets by improving both the accuracy and resolution of subsurface parameters estimation within the framework of idealized geometric models.