LVQ神经网络在探地雷达成果解译中的应用

Application of an LVQ Neural Network to Ground Penetrating Radar Result Interpretations

  • 摘要: 探地雷达作为短距离预报隧道掌子面前方地质情况的方法之一,已在国内外诸多隧道工程的施工中被成功地运用。探测获得的雷达图像一般由具有经验的工程技术人员进行解译,目前没有简单实用的解译方法和软件。文章在现场经验解译的基础上,提出了雷达图像标准化方案,运用图形边缘检测算子处理技术对图形特征向量进行提取,发现了特征向量与含水量之间的关系;运用LVQ神经网络在模式识别中的优势,经过训练和仿真验证了LVQ神经网络在探地雷达成果图解译中的可行性,并结合C#编程语言编制了图形识别软件,在实际应用中取得了很好的效果。

     

    Abstract: As one of the short-distance prediction methods for geological conditions in front of the working face, ground penetrating radar has been successfully applied to many tunnel projects at home and abroad. Since no simple and practical software or method for geological interpretation are available, the acquired radar images are often interpreted by experienced engineers. In this paper, a standardized interpretation scheme for radar images is proposed based on field interpretation experience, the relationship between eigenvectors and water content is discovered using a graphic eigenvector extracted with the graphic edge detection operator (Sobel Operator), the feasibility of using an LVQ neural network in the result interpretation of ground penetrating radar is verified by training and simulating with MATLAB software based on its advantages in pattern recognition, and image recognition software is programmed in C# language with good interpretation results achieved in its practical application.

     

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