摘要
水工洞室内基础地质现象在一定程度上可以反映地质灾害的发生,其分类与识别对于了解洞室内结构面分布状况、围岩性质以及指导下一步洞室勘探有重要意义。对水工洞室内地质现象和地质结构的分类识别分析通常采用手动作业的方式,但传统分析方法费时费力,难以实现分析过程的自动化。因此,采用多种深度学习模型和机器学习模型对水工洞室内基础地质现象图像进行分析,通过运用不同深度学习模型,并将Softmax分类器、随机森林及支持向量机应用于基础地质现象分类,对比并选择性能较好的模型进行耦合,可建立较优的水工洞室基础地质现象图像智能识别模型,在一定程度上实现了洞室内基础地质现象的自动识别分析,减少了地质工程师的工作量。
Basic geological phenomena in a hydraulic cavern can be a reflection of the occurrence of geological disaster.An efficient classification and identification of such information are crucial to understanding the distribution of structural planes in the cavern and the properties of its surrounding rocks to guide its further exploration.This task usually relies on manual work,but such traditional methods are time-consuming and labor-intensive at a low level of automated analysis.In this work,we use a variety of deep learning models and machine learning models to analyze the images of basic geological observations of hydraulic cavern foundation;and the cavern phenomena are classified through applying different deep learning models and the methods of Softmax classifier,random forest,and support vector machine.By comparing and selecting models with better performance for coupling,we developed a satisfactory image recognition model for the cavern geological phenomena,achieving automatic identification and analysis of the caverns,so that the workload of geological engineers is reduced significantly.
作者
李明超
赵文超
张野
任秋兵
李明泽
LI Mingchao;ZHAO Wenchao;ZHANG Ye;REN Qiubing;LI Mingze(State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300350,China;Bei Fang Investigation,Design&Research Corporation Limited,Tianjin 300222,China;State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area,Xi'an University of Technology,Xi'an 710048,China)
出处
《水力发电学报》
CSCD
北大核心
2023年第4期93-103,共11页
Journal of Hydroelectric Engineering
基金
国家自然科学基金面上项目(51879185,52179139)
湖北省水电工程施工与管理重点实验室开放基金(2020KSD06)。
关键词
水工洞室
地质图像
深度学习
机器学习
智能分类
hydraulic cavern
geological image
deep learning
machine learning
intelligent classification
