Recognition method of typical anomalies during karst tunnel construction using GPR attributes and Gaussian processes
Recognition method of typical anomalies during karst tunnel construction using GPR attributes and Gaussian processes
复制标题
基于探地雷达属性和高斯过程的岩溶隧道施工典型异常识别方法
DOI：
10.1007/s12517-020-05782-0
复制
发表时间：
2020-08-11
影响因子：
--
通讯作者：
Wu, Heng
中科院分区：
文献类型：
--
作者：
Liu, Maomao;Liu, Zonghui;Wu, Heng
Ground penetrating radar (GPR) is a geophysical exploration technique used in a wide range of applications such as polar exploration, hydrogeological surveys, and archeological prospection. In recent years, it has been applied to tunnel construction for forecasting geological anomalies in front of the tunnel face. However, the application of GPR, forecasting geological anomalies in karst tunnels, has always been restricted by the problem of ambiguity. In the paper, based on the GPR investigation of highway tunnels in the karst areas in Guangxi, China, several GPR data of typical karst geological anomalies were selected as the analysis objective. The purpose of this study is twofold: to analyze and summarize the GPR attribute characteristics of typical geological anomalies by studies of laboratory tests and to introduce a new method for identifying different types of karst geological anomalies automatically in karst tunnel construction. Firstly, the attribute analysis technology of GPR is introduced, and the propagation law of electromagnetic waves in karst geological anomalies is studied from three perspectives: the time domain, frequency domain, and time-frequency domain by using physical laboratory simulations. Then, an intelligent identification model for typical karst geological anomalies is established in accordance with a Gaussian process (GP). Results from the Gaussian classification of field cases indicate that the type of typical karst geological anomalies can be effectively identified via comparing model prediction probability. The findings of this research allow for quantitative description of the GPR interpretation of typical karst geological anomalies in tunnel advanced prediction.
