| 基于机器学习的海上风电场声学地层勘探技术原位试验研究 |
| In-Situ Experimental Research on Acoustic Stratigraphic Exploration for Offshore Wind Famms Based on Machine Learning |
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| DOI：10.3969/j.issn.1003-2029.2026.03.009 |
| 中文关键词: 海上风电场 声学地层勘探技术 机器学习 钻探 原位试验 |
| 英文关键词:offshore wind farm acoustic stratigraphic exploration technology machine learning drilling in situ testing |
| 基金项目:中国博士后科学基金资助项目（2022M721046）；河南省科技攻关项目（252102320308）；河南省高等学校重点科研项目（25B560003）；中国电建集团西北勘测设计研究院有限公司平台支撑项目（XBY-PTKJ-2022-4）；河南省研究生教育改革与质量提升工程项目（YJS2025XQC36） |
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| 中文摘要: |
| 在海上风电快速发展的背景下，准确、高效的工程地质勘探对保障项目安全具有重要意义。本文以福建某海上风电场为例，开展原位声学地层勘探，结合深度学习与无监督聚类的智能解译方法对勘探数据进行分层，并与原位钻探揭示的实际地层信息进行对比。结果表明:声学地层勘探技术可较清晰、连续地揭示海底主要地层结构。在25个对比数据点中，绝对误差小于1.0m的有12个，占比为48.0%;绝对误差小于4.0m的有19个，占比为76.0%，表明该方法在多数测点的宏观地层划分中具有一定可靠性，同时可识别局部地质异常区域。研究成果可为声学地层勘探技术在海上风电项目中的应用提供参考，并为福建海域风机基础选型提供基础数据。 |
| 英文摘要: |
| Against the backdrop of the rapid development of offshore wind power, accurate and efficient engineering geological site investigations are important for ensuring project safety. Taking an offshore wind farm in Fujian Province as a case study, this research conducted in situ acoustic stratigraphic profiling. An intelligent interpretation approach, integrating deep learning with unsupervised clustering, was employed to stratify the exploration data. The interpreted results were subsequently correlated with the actual stratigraphic profiles revealed by in situ borehole drilling. The findings show that acoustic stratigraphic exploration technology can delineate the primary sub-seafloor stratigraphic structures relatively clearly and continuously. Among the 25 comparison data points, 12 had absolute errors of less than 1.0 m, accounting for 48.0%, and 19 had absolute errors of less than 4.0 m, accounting for 76.0%. These results indicate that the method has a certain level of reliability for macroscopic stratigraphic classification at most measurement points and can identify localized geological anomalies. These findings provide references for the application of acoustic stratigraphic profiling in offshore wind power projects and offer basic data for wind turbine foundation selection in the Fujian sea area. |
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