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

Area of Science:

• Geophysics
• Geophysical Exploration
• Computational Geophysics

Background:

• Accurate subsurface parameter estimation is crucial for geophysical exploration.
• Interpreting magnetic field data requires robust methods to delineate subsurface structures.
• Existing methods may struggle with noise and regional field effects.

Purpose of the Study:

• To develop and validate a novel hybrid algorithm for optimizing the estimation of geometric parameters from magnetic data.
• To integrate the Bat Algorithm (BA) with the Fourth Horizontal Gradient (FHG) for enhanced geophysical inversion.
• To improve the accuracy and resolution of subsurface structure interpretation using magnetic field data.

Main Methods:

• Hybrid algorithm combining the Bat Algorithm (BA) and Fourth Horizontal Gradient (FHG).
• FHG used to enhance magnetic anomaly resolution and mitigate regional field effects.
• BA employed for efficient navigation of parameter space in optimizing geometric parameters (depth, amplitude, shape, origin, magnetization angle).
• Algorithm tested on synthetic datasets (noise-free and 10% Gaussian noise) and real magnetic profile data.

Main Results:

• The hybrid BA-FHG algorithm accurately estimates subsurface geometric parameters.
• The method demonstrates robustness and effectiveness in noisy environments.
• Validation with real magnetic data from the Faro Mine Complex shows close agreement with well data and prior studies.
• Improved accuracy and resolution in subsurface parameter estimation were achieved.

Conclusions:

• The integrated BA-FHG approach offers a significant advancement in magnetic dataset interpretation.
• The algorithm provides reliable subsurface parameter estimation for idealized geometric models.
• This method enhances the practical effectiveness of geophysical exploration by improving inversion accuracy and resolution.