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Automatic classification of metallic targets using pattern recognition of GPR reflection: a study in the IAG-USP Test Site, Sao Paulo (Brazil)

机译:使用GPR反射模式识别的金属目标自动分类:IAG-USP测试网站的研究,圣保罗(巴西)

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In this work, a methodology to automatically classify of metal targets using pattern recognition techniques on GPR reflection data is presented. The methodology consists of designing a multilayer perceptron (MLP) classifier based on features extracted from the targets in the subsoil, and then using it to classify hyperbolas diffraction indicating their position and depth. The classification of reflections allows a high resolution reconstruction of the subsurface with reduced computing time. The system was developed in MATLAB and applied to GPR data obtained at IAG-USP test site, located in the city of Sao Paulo, Brazil, where metallic drums were studied under controlled field conditions. This site contains different targets of variable sizes buried under different depths and it served as a model for the computational experiment. The results indicate that the automatic classification of the metallic targets in the subsoil is efficient, contributing for the reduction of the ambiguities in the geophysical data interpretation, besides having application on the subsoil mapping of utilities.
机译:在这项工作中,提出了一种在GPR反射数据上使用模式识别技术自动分类金属目标的方法。该方法包括基于从子内的目标中提取的特征来设计多层的Perceptron(MLP)分类器,然后使用它来分类标题的双曲线衍射指示其位置和深度。反射的分类允许具有减少计算时间的地下的高分辨率重建。该系统在MATLAB中开发,应用于位于巴西圣保罗市的IAG-USP测试场所获得的GPR数据,其中在受控现场条件下研究了金属鼓。该站点包含在不同深度下掩埋的不同目标的不同目标,并且它作为计算实验的模型。结果表明,除了在公用事业的底层映射中存在施加地球物理数据解释中的含糊不清,底层中金属靶标的自动分类是有效的,有助于降低地球物理数据解释中的模糊性。

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