The use of neural networks in the interpretation of ground penetrating radar

1993 ◽  
Author(s):  
D. V. Minior ◽  
S. Smith
Keyword(s):  
Neural Networks ◽  
Ground Penetrating Radar ◽  
Ground Penetrating

scholarly journals Neural Networks as a Tool for Georadar Data Processing

2015 ◽  
Vol 25 (4) ◽  
pp. 955-960 ◽  
Author(s):  
Piotr Szymczyk ◽  
Sylwia Tomecka-Suchoń ◽  
Magdalena Szymczyk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Data Processing ◽  
Ground Penetrating Radar ◽  
Feature Vector ◽  
Special Kind ◽  
Automatic Classification ◽  
Geologic Structure ◽  
Ground Penetrating

Abstract In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.

scholarly journals Exploration of Pipelines from the use of GPR Data by Neural Network

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3711-3715
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Ground Penetrating Radar ◽  
Buried Pipes ◽  
Adaptive Neural Networks ◽  
The World ◽  
Systematic Training ◽  
Ground Penetrating

Noticing about the buried pipes is a important issue, In many regions of the world. In spite of the fact that several techniques are there. This literature is used to find out the underground pipes automatically that provides accuracy execution is underway. Which gave amazing results Achieved by the deep learning of the different discoveries found in this article offer a pipeline to detect anti-personnel pipes Adaptive Neural Networks ( applied to the Ground Penetrating Radar (GPR). The proposed algorithm is suitable to recognize if the scanning format has been received. The acquisition of GPR has a track of anti-personnel pipes. The validity of the said system is made on a real GPR receipt, although systematic training can be done to have relied upon data generated by achievements. Based on the results 95% of the accuracy of detection got achieved without testing acquisition of pipes.

scholarly journals Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xisto L. Travassos ◽  
Sérgio L. Avila ◽  
Nathan Ida
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ground Penetrating Radar ◽  
Data Interpretation ◽  
Machine Learning Techniques ◽  
Computational Techniques ◽  
Data Detection ◽  
Artificial Neural ◽  
Ground Penetrating

Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.

scholarly journals Inversion of Ground Penetrating Radar Data Based on Neural Networks

2018 ◽  
Vol 10 (5) ◽  
pp. 730 ◽  
Author(s):  
Tao Liu ◽  
Yi Su ◽  
Chunlin Huang
Keyword(s):  
Neural Networks ◽  
Ground Penetrating Radar ◽  
Radar Data ◽  
Ground Penetrating
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