Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression
Analyzing geological profiles is of great importance for various applications such as natural resource management, environmental assessment, and mining engineering projects. This study presents a novel geostatistical approach for subsurface geological profile interpolation using a fractional kriging...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/8/12/717 |
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| author | Qile Ding Yiren Wang Yu Zheng Fengyang Wang Shudong Zhou Donghui Pan Yuchun Xiong Yi Zhang |
| author_facet | Qile Ding Yiren Wang Yu Zheng Fengyang Wang Shudong Zhou Donghui Pan Yuchun Xiong Yi Zhang |
| author_sort | Qile Ding |
| collection | DOAJ |
| description | Analyzing geological profiles is of great importance for various applications such as natural resource management, environmental assessment, and mining engineering projects. This study presents a novel geostatistical approach for subsurface geological profile interpolation using a fractional kriging method enhanced by random forest regression. Using bedrock elevation data from 49 boreholes in a study area in southeast China, we first use random forest regression to predict and optimize variogram parameters. We then use the fractional kriging method to interpolate the data and analyze the variability. We also compare the proposed model with traditional methods, including linear regression, K-nearest neighbors, ordinary kriging, and fractional kriging, using cross-validation metrics. The results indicate that the proposed model reduces prediction errors and enhances spatial prediction reliability compared to other models. The MSE of the proposed model is 25% lower than that of ordinary kriging and 10% lower than that of fractional kriging. In addition, the execution time of the proposed model is slightly higher than other models. The findings suggest that the proposed model effectively captures complex subsurface spatial relationships, offering a reliable and precise solution for performing spatial interpolation tasks. |
| format | Article |
| id | doaj-art-404ce177de6d4cf7bcfa411c36860cb5 |
| institution | DOAJ |
| issn | 2504-3110 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj-art-404ce177de6d4cf7bcfa411c36860cb52025-08-20T02:53:35ZengMDPI AGFractal and Fractional2504-31102024-12-0181271710.3390/fractalfract8120717Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest RegressionQile Ding0Yiren Wang1Yu Zheng2Fengyang Wang3Shudong Zhou4Donghui Pan5Yuchun Xiong6Yi Zhang7School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, ChinaSchool of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, ChinaSchool of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, ChinaSchool of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, ChinaDongguan Institute of Building Research Co., Ltd., Dongguan 523809, ChinaSchool of Environment and Civil Engineering, Dongguan University of Technology, Dongguan 523808, ChinaGuangdong Building Material Research Institute Co., Ltd., Guangzhou 510160, ChinaDongguan Institute of Building Research Co., Ltd., Dongguan 523809, ChinaAnalyzing geological profiles is of great importance for various applications such as natural resource management, environmental assessment, and mining engineering projects. This study presents a novel geostatistical approach for subsurface geological profile interpolation using a fractional kriging method enhanced by random forest regression. Using bedrock elevation data from 49 boreholes in a study area in southeast China, we first use random forest regression to predict and optimize variogram parameters. We then use the fractional kriging method to interpolate the data and analyze the variability. We also compare the proposed model with traditional methods, including linear regression, K-nearest neighbors, ordinary kriging, and fractional kriging, using cross-validation metrics. The results indicate that the proposed model reduces prediction errors and enhances spatial prediction reliability compared to other models. The MSE of the proposed model is 25% lower than that of ordinary kriging and 10% lower than that of fractional kriging. In addition, the execution time of the proposed model is slightly higher than other models. The findings suggest that the proposed model effectively captures complex subsurface spatial relationships, offering a reliable and precise solution for performing spatial interpolation tasks.https://www.mdpi.com/2504-3110/8/12/717interpolationfractional kriging methodboreholebedrock elevationvariogram |
| spellingShingle | Qile Ding Yiren Wang Yu Zheng Fengyang Wang Shudong Zhou Donghui Pan Yuchun Xiong Yi Zhang Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression Fractal and Fractional interpolation fractional kriging method borehole bedrock elevation variogram |
| title | Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression |
| title_full | Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression |
| title_fullStr | Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression |
| title_full_unstemmed | Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression |
| title_short | Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression |
| title_sort | subsurface geological profile interpolation using a fractional kriging method enhanced by random forest regression |
| topic | interpolation fractional kriging method borehole bedrock elevation variogram |
| url | https://www.mdpi.com/2504-3110/8/12/717 |
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