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Proceedings Paper

An interpretation model of GPR point data in tunnel geological prediction
Author(s): Yu-yao He; Bao-qi Li; Yuan-shu Guo; Teng-na Wang; Ya Zhu
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Paper Abstract

GPR (Ground Penetrating Radar) point data plays an absolutely necessary role in the tunnel geological prediction. However, the research work on the GPR point data is very little and the results does not meet the actual requirements of the project. In this paper, a GPR point data interpretation model which is based on WD (Wigner distribution) and deep CNN (convolutional neural network) is proposed. Firstly, the GPR point data is transformed by WD to get the map of time-frequency joint distribution; Secondly, the joint distribution maps are classified by deep CNN. The approximate location of geological target is determined by observing the time frequency map in parallel; Finally, the GPR point data is interpreted according to the classification results and position information from the map. The simulation results show that classification accuracy of the test dataset (include 1200 GPR point data) is 91.83% at the 200 iteration. Our model has the advantages of high accuracy and fast training speed, and can provide a scientific basis for the development of tunnel construction and excavation plan.

Paper Details

Date Published: 8 February 2017
PDF: 6 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102252J (8 February 2017); doi: 10.1117/12.2266226
Show Author Affiliations
Yu-yao He, Northwestern Polytechnical Univ. (China)
Bao-qi Li, Northwestern Polytechnical Univ. (China)
Yuan-shu Guo, Chang'an Univ. (China)
Teng-na Wang, Chang'an Univ. (China)
Ya Zhu, Northwestern Polytechnical Univ. (China)

Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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