Multi-frequency and multi-attribute GPR data fusion based on 2-D wavelet transform

被引：29
作者：
Lu, Guoze [1 ]
Zhao, Wenke [1 ,2 ]
Forte, Emanuele [3 ]
Tian, Gang [1 ]
Li, Yong [2 ]
Pipan, Michele [3 ]
机构：
[1] Zhejiang Univ, Sch Earth Sci, Hangzhou 310007, Peoples R China
[2] Chinese Acad Geol Sci, Inst Geophys & Geochem Explorat, Key Lab Geophys Electromagnet Probing Technol, Minist Nat Resources, Langfang 065000, Peoples R China
[3] Univ Trieste, Dept Math & Geosci, I-34126 Trieste, Italy
来源：
MEASUREMENT | 2020年 / 166卷
基金：
中国国家自然科学基金;
关键词：
GPR data fusion; Multi-frequency; Multi-attribute; Wavelet transform; ATTRIBUTES; VISUALIZATION; FAULT;
D O I：
10.1016/j.measurement.2020.108243
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
High frequency GPR signals offer high resolution while low frequency GPR signals offer greater depth of penetration. Effective fusion of multiple frequencies can combine the advantages of both. In addition, GPR attribute analysis can improve subsurface imaging, but a single attribute can only partly highlight details of different physical and geometrical properties of subsurface potential targets. In order to overcome these challenges, we implement an advanced multi-frequency and multi-attribute GPR data fusion approach based on 2-D wavelet transform utilizing a dynamic fusion weight scheme derived from edge detection algorithm, which is tested on data from a small glacier in the north-eastern Alps by 250 & 500 MHz central frequency antennas. Besides, information entropy and spatial frequency are developed as quantitative evaluation parameters to analyze the fusion outcomes. The results demonstrate that the proposed approach can enhance the efficiency and scope of GPR data interpretation in an automatic and objective way. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数：6
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