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Dr. Xue-Fan Wang is Associate Professor at China University of Geosciences (Beijing), China. He obtained Bachelor and Master degrees in Geological Engineering from Nanjing University, China. He obtained PhD in Geotechnical Engineering at The University of Hong Kong (HKU), China. He was the recipient of Young Elite Scientists Sponsorship Program by CAST. His research has been funded by multiple national grants, and the outcomes have been applied to major infrastructure projects. His main expertise includes intelligent monitoring techniques for various applications in geological engineering and civil engineering. His research interests cover drilling process monitoring (DPM), measurement while drilling (MWD), distributed fiber optical sensing, digitalization, and artificial intelligence.
Outline
Stratigraphic interface characterization and strength parameter assessment of geomaterials constitute fundamental research priorities in geological and geotechnical engineering. While measurement while drilling (MWD) and drilling process monitoring (DPM) have emerged as critical techniques for acquiring real-time drilling parameters, inherent limitations in data interpretation persist. The critical challenge of random fluctuations in MWD-derived penetration rate measurements exhibits poor correlation with the stratified homogeneity characteristics of geological formations. Such discrepancies undermine the reliability of stratigraphic classification and mechanical property analysis. Through systematic comparison of MWD and DPM datasets combined with quantitative parameter evaluation, this investigation reveals significant methodological distinctions in data acquisition accuracy. Machine learning-enhanced analysis employing Support Vector Machine (SVM) algorithms demonstrates that DPM-derived parameters provide superior stratigraphic identification capabilities. Our findings indicate that DPM implementations achieve 20.57 % and 38.01 % higher resolution in interface detection along two drill-holes compared to the conventional MWD approaches. This improvement allows for better prediction of stratigraphic profiles and more precise guidance in subsequent geological and geotechnical engineering practices.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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