Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping
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 combine...
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KeAi Communications Co. Ltd.
2025-06-01
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| Series: | Artificial Intelligence in Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666544125000206 |
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| author | André William Boroh Alpha Baster Kenfack Fokem Martin Luther Mfenjou Firmin Dimitry Hamat Fritz Mbounja Besseme |
| author_facet | André William Boroh Alpha Baster Kenfack Fokem Martin Luther Mfenjou Firmin Dimitry Hamat Fritz Mbounja Besseme |
| author_sort | André William Boroh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bc00d6e829034bbb903891472598f92b |
| institution | OA Journals |
| issn | 2666-5441 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Geosciences |
| spelling | doaj-art-bc00d6e829034bbb903891472598f92b2025-08-20T02:22:05ZengKeAi Communications Co. Ltd.Artificial Intelligence in Geosciences2666-54412025-06-016110012410.1016/j.aiig.2025.100124Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mappingAndré William Boroh0Alpha Baster Kenfack Fokem1Martin Luther Mfenjou2Firmin Dimitry Hamat3Fritz Mbounja Besseme4Department of Mining Geology, School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box: 115, Meiganga, Cameroon; Corresponding author.Ministry of Mines and Technological Industries, Yaoundé, CameroonDepartment of Mathematics and Computer Sciences, School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box: 115, Meiganga, CameroonDepartment of Earth Sciences, Faculty of Sciences, University of Ngaoundere, CameroonDepartment of Mining Geology, School of Geology and Mining Engineering, University of Ngaoundere, P.O. Box: 115, Meiganga, CameroonThe 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.http://www.sciencedirect.com/science/article/pii/S2666544125000206Variogram modellingGenetic algorithm (GA)Machine learningGravity dataMineral exploration |
| spellingShingle | André William Boroh Alpha Baster Kenfack Fokem Martin Luther Mfenjou Firmin Dimitry Hamat Fritz Mbounja Besseme Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping Artificial Intelligence in Geosciences Variogram modelling Genetic algorithm (GA) Machine learning Gravity data Mineral exploration |
| title | Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping |
| title_full | Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping |
| title_fullStr | Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping |
| title_full_unstemmed | Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping |
| title_short | Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping |
| title_sort | variogram modelling optimisation using genetic algorithm and machine learning linear regression application for sequential gaussian simulations mapping |
| topic | Variogram modelling Genetic algorithm (GA) Machine learning Gravity data Mineral exploration |
| url | http://www.sciencedirect.com/science/article/pii/S2666544125000206 |
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