A comparative study of interpolation methods for the development of ore distribution maps
Abstract Mining projects require precise knowledge about tonnage and quality of ore reserves for planning and decision-making. This is hard to establish as exploration operations, which are costly, time-consuming and require an accurate description of the deposit. Geologists use deterministic and ge...
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2025-01-01
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| Series: | Discover Geoscience |
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| Online Access: | https://doi.org/10.1007/s44288-025-00108-7 |
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| author | Mahinaz M. Shawky |
| author_facet | Mahinaz M. Shawky |
| author_sort | Mahinaz M. Shawky |
| collection | DOAJ |
| description | Abstract Mining projects require precise knowledge about tonnage and quality of ore reserves for planning and decision-making. This is hard to establish as exploration operations, which are costly, time-consuming and require an accurate description of the deposit. Geologists use deterministic and geostatistical methods as interpolation tools to generate a continuous (or prediction) ore distribution map from known sampled data. A comparative study between four different geostatistical interpolation techniques: Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK) and Empirical Bayesian Kriging (EBK); and four deterministic techniques: Inverse Distance Weighting (IDW), Global Polynomial Interpolation (GPI), Radial Basis Function (RBF), and Local Polynomial Interpolation (LPI) were applied. The 95% confidence level was measured and the effectiveness of each interpolation method was assessed through cross-validation for the construction of the corresponding maps displaying the distribution of ore on the surface with the use of GIS. The output methods are ranked to perform a comprehensive analysis using the error statistics methods: Mean Error (ME), Root Mean Square Error (RMSE), Mean Standardized Error (MSE) and Root Mean Square Standardized Error (RMSSE). The results show that GPI, EBK and Kriging methods perform the best results while IDW has good statistical analysis factors but it came in the tail of the rank. Therefore, there is no appropriate interpolation method accurate for all cases, each method must be statistically evaluated before each application and essentially based on real data. |
| format | Article |
| id | doaj-art-bd4de5250118449eb24e9a914b5201ef |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
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| series | Discover Geoscience |
| spelling | doaj-art-bd4de5250118449eb24e9a914b5201ef2025-01-19T12:13:21ZengSpringerDiscover Geoscience2948-15892025-01-013112210.1007/s44288-025-00108-7A comparative study of interpolation methods for the development of ore distribution mapsMahinaz M. Shawky0Computer and Information Sciences, Nuclear Materials AuthorityAbstract Mining projects require precise knowledge about tonnage and quality of ore reserves for planning and decision-making. This is hard to establish as exploration operations, which are costly, time-consuming and require an accurate description of the deposit. Geologists use deterministic and geostatistical methods as interpolation tools to generate a continuous (or prediction) ore distribution map from known sampled data. A comparative study between four different geostatistical interpolation techniques: Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK) and Empirical Bayesian Kriging (EBK); and four deterministic techniques: Inverse Distance Weighting (IDW), Global Polynomial Interpolation (GPI), Radial Basis Function (RBF), and Local Polynomial Interpolation (LPI) were applied. The 95% confidence level was measured and the effectiveness of each interpolation method was assessed through cross-validation for the construction of the corresponding maps displaying the distribution of ore on the surface with the use of GIS. The output methods are ranked to perform a comprehensive analysis using the error statistics methods: Mean Error (ME), Root Mean Square Error (RMSE), Mean Standardized Error (MSE) and Root Mean Square Standardized Error (RMSSE). The results show that GPI, EBK and Kriging methods perform the best results while IDW has good statistical analysis factors but it came in the tail of the rank. Therefore, there is no appropriate interpolation method accurate for all cases, each method must be statistically evaluated before each application and essentially based on real data.https://doi.org/10.1007/s44288-025-00108-7Image processingDistribution mapSpatial interpolation techniquesKrigingConfidence level |
| spellingShingle | Mahinaz M. Shawky A comparative study of interpolation methods for the development of ore distribution maps Discover Geoscience Image processing Distribution map Spatial interpolation techniques Kriging Confidence level |
| title | A comparative study of interpolation methods for the development of ore distribution maps |
| title_full | A comparative study of interpolation methods for the development of ore distribution maps |
| title_fullStr | A comparative study of interpolation methods for the development of ore distribution maps |
| title_full_unstemmed | A comparative study of interpolation methods for the development of ore distribution maps |
| title_short | A comparative study of interpolation methods for the development of ore distribution maps |
| title_sort | comparative study of interpolation methods for the development of ore distribution maps |
| topic | Image processing Distribution map Spatial interpolation techniques Kriging Confidence level |
| url | https://doi.org/10.1007/s44288-025-00108-7 |
| work_keys_str_mv | AT mahinazmshawky acomparativestudyofinterpolationmethodsforthedevelopmentoforedistributionmaps AT mahinazmshawky comparativestudyofinterpolationmethodsforthedevelopmentoforedistributionmaps |