上海交通大学学报

上海交通大学学报

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基于改进的图卷积网络算法的孔间地层界线预测分析（网络首发）

  

  1. 1.浙江大学滨海和城市岩土工程研究中心；2.香港理工大学土木及环境工程学系；3.中铁第四勘察设计院集团有限公司；4.浙大城市学院工程学院；5.华东交通大学江西省岩土工程基础设施安全与控制重点实验室；6.江西省地下空间技术开发工程研究中心
  • 基金资助:
    国家自然科学基金-高铁联合基金(U1934208)资助项目

Novel Graph Convolutional Network for predicting geological profiles

  1. (1. Research Center of Coastal and Urban Geotechnical Engineering, Zhejiang University, Hangzhou 310058, China; 2. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China; 3. China Railway SIYUAN Survey and Design Group Co., Ltd., Hangzhou 310000, China; 4. School of Engineering, Hangzhou City University, Hangzhou 310015, China; 5. Jiangxi Key Laboratory of Infrastructure Safety Control in Geotechnical Engineering, East China Jiaotong University, Nanchang 330013, China; 6. Engineering Research & Development Centre for Underground Technology of Jiangxi Province, Nanchang 330013, China)

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摘要： 将图卷积网络算法应用于地层界面预测，并对图卷积网络进行了改进，提出了一种改进的图卷积网络算法，使其能够仅通过场地内不等距且稀疏的钻孔来预测场地的孔间地层界线，并提出了边界准确率来更好的判断预测的地层界线的准确性。将本方法应用于实际地质剖面案例中，并与现有的马尔可夫随机场方法和IC-XGBoost方法进行比较，结果表明，本方法在全局准确率和边界准确率上均有所提高，说明该方法能较为准确的预测孔间地层界线。将该方法实际应用于杭州某基坑的孔间地层界面预测，其中验证钻孔的准确率均高于0.8。讨论了钻孔间距大小和间距是否相等对预测结果的影响，结果表明钻孔间距越小，预测出的地质剖面边界越准确，但钻孔间距的缩小和预测准确率的提高并不是简单的线性关系，预测准确率的提高取决于增加的钻孔提供的土层空间信息；当钻孔数量相同时，钻孔不等间距对结果的影响取决于钻孔的位置所提供的土层空间信息，钻孔提供的土层空间信息越多，预测越准确。

关键词: 图卷积神经网络, 稀疏钻孔, 非等间距, 地层界面, 边界准确率

Abstract: The graph convolutional network (GCN) algorithm is applied to geological profile prediction, and an improved graph convolutional network algorithm is proposed through modifications. This algorithm can flexibly establish connection graphs based on unequal and sparse boreholes within the site, enabling the prediction of the two-dimensional geological profile of the site. A boundary accuracy rate is proposed to better judge the accuracy of the predicted geological profile. This method is applied to actual geological profile cases and compared with the existing IC-XGBoost method. The results show that this method improves the global accuracy and the boundary accuracy compared with other methods which indicates its accuracy in predicting two-dimensional geological profiles. Finally, the influence of the number of boreholes and unequal spacing on the prediction results is discussed. The results indicate that the more boreholes there are, the more accurate the predicted geological profile boundary will be. However, the increase in the number of boreholes and the improvement in prediction accuracy are not a simple linear relationship. The improvement in prediction accuracy depends on the soil layer spatial information provided by the additional boreholes. When the number of boreholes is the same, the influence of unequal spacing of boreholes on the results depends on the soil layer spatial information provided by the locations of the boreholes. The more soil layer spatial information provided by the boreholes, the more accurate the prediction will be. Finally, this improved GCN method applied in Hangzhou real case to predict the geological profile. This method works well in real case that all the measurement accuracy of validated boreholes above 0.8.

Key words: Graph convolutional neural network, Sparse boreholes, Unregular, Stratigraphic interface, Boundary accuracy

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