Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping

Abstract

The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms (GA) with machine learning-based linear regression, aiming to improve the accuracy and efficiency of geostatistical analysis, particularly in mineral exploration. The study combines GA and machine learning to optimise variogram parameters, including range, sill, and nugget, by minimising the root mean square error (RMSE) and maximising the coefficient of determination (R²). The experimental variograms were computed and modelled using theoretical models, followed by optimisation via evolutionary algorithms. The method was applied to gravity data from the Ngoura-Batouri-Kette mining district in Eastern Cameroon, covering 141 data points. Sequential Gaussian Simulations (SGS) were employed for predictive mapping to validate simulated results against true values. Key findings show variograms with ranges between 24.71 km and 49.77 km, optimised RMSE and R² values of 11.21 mGal² and 0.969, respectively, after 42 generations of GA optimisation. Predictive mapping using SGS demonstrated that simulated values closely matched true values, with the simulated mean at 21.75 mGal compared to the true mean of 25.16 mGal, and variances of 465.70 mGal² and 555.28 mGal², respectively. The results confirmed spatial variability and anisotropies in the N170-N210 directions, consistent with prior studies. This work presents a novel integration of GA and machine learning for variogram modelling, offering an automated, efficient approach to parameter estimation. The methodology significantly enhances predictive geostatistical models, contributing to the advancement of mineral exploration and improving the precision and speed of decision-making in the petroleum and mining industries.