Regularized ensemble Kalman inversion for robust and efficient gravity data modeling to identify mineral and ore...

Area of Science:

• Geophysics
• Geophysical Exploration
• Inverse Problems

Background:

• Modeling mineral and ore bodies from gravity anomalies is challenging due to the ill-posed nature of inverse problems, especially with noisy or sparse data.
• Existing inversion methods often require extensive parameter tuning and can produce unstable solutions.

Purpose of the Study:

• To propose a regularized ensemble Kalman inversion (EKI) framework to enhance numerical stability and uncertainty quantification in geophysical exploration.
• To improve the modeling of mineral and ore bodies from gravity anomalies.

Main Methods:

• Developed a regularized ensemble Kalman inversion (EKI) framework incorporating Tikhonov regularization.
• Utilized ensemble statistics for efficient uncertainty quantification.
• Performed controlled numerical experiments and benchmarked against metaheuristic algorithms (PSO, VFSA, BA).

Main Results:

• The regularized EKI framework demonstrated improved numerical stability and reduced sensitivity to ensemble degeneracy.
• Optimal balance between convergence stability and model resolution achieved with an ensemble size larger than [Formula: see text] and moderate regularization.
• Superior computational efficiency and stable convergence compared to established metaheuristic algorithms.
• Stable and geologically consistent results obtained for synthetic and real gravity data (chromite, Pb-Zn, sulphide, Cu-Au deposits).

Conclusions:

• The regularized EKI framework is a robust and efficient tool for mineral exploration.
• This method effectively mitigates mining risks and supports strategic decision-making.
• The approach provides stable, geologically consistent models validated by prior interpretations and drilling data.