首页|分布式模糊聚类微动法铁路路基岩溶地球物理探测:以皖赣铁路宁国改线工程为例

分布式模糊聚类微动法铁路路基岩溶地球物理探测:以皖赣铁路宁国改线工程为例

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微动勘探法可探查铁路路基地下岩溶、裂隙通道等不良地质体的发育位置,针对反演成果中土、岩体分界面模糊不清,异常位置及边界不准确等问题,采用分布式模糊聚类算法分析反演数据.系统回顾了微动勘探法和分布式模糊聚类算法基本原理,以皖赣铁路宁国改线某区间既有铁路路基岩溶勘察为例,开展分布式模糊聚类微动勘探进行地层分层、溶洞自动划分.将分布式模糊聚类法分析前后的反演数据同时与钻探揭露结果对比发现,分布式模糊聚类算法可对分界面、异常区域进行自动有效划定,可更加准确地识别地质异常体.说明该方法可较大程度提高微动反演数据的准确率,为铁路路基工程的设计和施工提供参考.
Karst Geophysical Detection of Railway Subgrade by Distributed Fuzzy Clustering of Microtremor Method:Taking the Ningguo Rerouting Project of Anhui-Jiangxi Railway as an Example
The microtremor survey method was used to detect the development position of unfavorable geological bodies such as karst and fissure channels under the railway road base.As to the problems in the inversion results,such as blurred interface between soil and rock mass,inaccurate location and boundary of anomalies,etc.,the distributed fuzzy clustering algorithm was used to analyze the inversion data.The basic principles of the microtremor survey method and the distributed fuzzy clustering algorithm was systematically reviewed.Taking the karst survey of the existing railway subgrade in a certain section of the Anhui-Jiangxi Railway Ningguo Line as an example,the distributed fuzzy clustering of microtremor exploration was carried out for stratum stratification and karst cavern automatic division.The inversion data before and after distributed fuzzy clustering analysis were compared with the drilling results,it is found that the distributed fuzzy clustering algorithm can automatically and effectively delineate the interface and abnormal areas,and more accurately identify geological anomalies.It shows that this method can greatly improve the accuracy of microtremor inversion data,and provide reference for the design and construction of railway subgrade engineering.

railway subgradeKarstgeophysical prospectingmicrotremor survey methoddistributed fuzzy clustering

王其合、苏本玉、王国林

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中铁上海设计院集团有限公司,上海 200070

中国矿业大学资源与地球科学学院,徐州 221116

铁路路基 岩溶 物探 微动勘探法 分布式模糊聚类

中国铁建股份有限公司科技重大专项国家自然科学基金

2021-A0242274179

2024

科学技术与工程
中国技术经济学会

科学技术与工程

CSTPCD北大核心
影响因子:0.338
ISSN:1671-1815
年,卷(期):2024.24(3)
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