Enhancing Ground Penetrating Radar (GPR) Data Analysis Utilizing Machine Learning
Ground Penetrating Radar is a non-destructive geophysical technique that utilizes radio waves to generate images of the Earth's subsurface to point out the location of buried evidence. In this paper, it is used to identify structures and types of seismic images of a real oil and gas field. Thi...
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| Format: | Article |
| Language: | Arabic |
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Mustansiriyah University/College of Engineering
2025-05-01
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| Series: | Journal of Engineering and Sustainable Development |
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| Online Access: | https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/2509 |
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| author | Mohanad Shehab Musab T.S. Al-Kaltakchi Ammar Dukhan Wai Lok Woo |
| author_facet | Mohanad Shehab Musab T.S. Al-Kaltakchi Ammar Dukhan Wai Lok Woo |
| author_sort | Mohanad Shehab |
| collection | DOAJ |
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Ground Penetrating Radar is a non-destructive geophysical technique that utilizes radio waves to generate images of the Earth's subsurface to point out the location of buried evidence. In this paper, it is used to identify structures and types of seismic images of a real oil and gas field. This work employs GPR with 500MHz to permit the EMW to penetrate deep and to provide a good resolution for images generated. Gray-Level Co-Occurrence Matrix and Wavelet feature extractor approaches are mixed to extract 48 selected features. Subsequently, preprocessing techniques are utilized to improve GPR data analysis and interpretation, including refining data, imputing the missing values, normalizing all data, and splitting them into 70% for the training and 30% for the testing phases. Finally, various machine learning techniques are employed to classify the collected images using models like Decision Trees,agged trees, Naive Bayes, Artificial Neural Networks, Quadratic Discriminant Analysis, Support Vector Machines, and K-nearest neighbors. The performance metrics of all the machine learning approaches are worthy, and the proposed KNN can achieve an accuracy of 98.169%, 14 seconds of training time, and less than a few seconds of testing time.
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| format | Article |
| id | doaj-art-3d38c36dd2954cc2b740b56e06949390 |
| institution | OA Journals |
| issn | 2520-0917 2520-0925 |
| language | Arabic |
| publishDate | 2025-05-01 |
| publisher | Mustansiriyah University/College of Engineering |
| record_format | Article |
| series | Journal of Engineering and Sustainable Development |
| spelling | doaj-art-3d38c36dd2954cc2b740b56e069493902025-08-20T02:19:51ZaraMustansiriyah University/College of EngineeringJournal of Engineering and Sustainable Development2520-09172520-09252025-05-0129310.31272/jeasd.2509Enhancing Ground Penetrating Radar (GPR) Data Analysis Utilizing Machine LearningMohanad Shehab0https://orcid.org/0000-0002-2941-0762Musab T.S. Al-Kaltakchi 1https://orcid.org/0000-0001-5542-9144Ammar Dukhan 2https://orcid.org/0000-0002-1783-0911Wai Lok Woo3https://orcid.org/0000-0002-8698-7605Electrical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, IraqElectrical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, IraqElectrical Engineering Department, College of Engineering, Mustansiriyah University, Baghdad, IraqDepartment of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK Ground Penetrating Radar is a non-destructive geophysical technique that utilizes radio waves to generate images of the Earth's subsurface to point out the location of buried evidence. In this paper, it is used to identify structures and types of seismic images of a real oil and gas field. This work employs GPR with 500MHz to permit the EMW to penetrate deep and to provide a good resolution for images generated. Gray-Level Co-Occurrence Matrix and Wavelet feature extractor approaches are mixed to extract 48 selected features. Subsequently, preprocessing techniques are utilized to improve GPR data analysis and interpretation, including refining data, imputing the missing values, normalizing all data, and splitting them into 70% for the training and 30% for the testing phases. Finally, various machine learning techniques are employed to classify the collected images using models like Decision Trees,agged trees, Naive Bayes, Artificial Neural Networks, Quadratic Discriminant Analysis, Support Vector Machines, and K-nearest neighbors. The performance metrics of all the machine learning approaches are worthy, and the proposed KNN can achieve an accuracy of 98.169%, 14 seconds of training time, and less than a few seconds of testing time. https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/2509AccuracyClassifiersGround Penetrating RadarSeismic ImagesReal Oil and Gas Field |
| spellingShingle | Mohanad Shehab Musab T.S. Al-Kaltakchi Ammar Dukhan Wai Lok Woo Enhancing Ground Penetrating Radar (GPR) Data Analysis Utilizing Machine Learning Journal of Engineering and Sustainable Development Accuracy Classifiers Ground Penetrating Radar Seismic Images Real Oil and Gas Field |
| title | Enhancing Ground Penetrating Radar (GPR) Data Analysis Utilizing Machine Learning |
| title_full | Enhancing Ground Penetrating Radar (GPR) Data Analysis Utilizing Machine Learning |
| title_fullStr | Enhancing Ground Penetrating Radar (GPR) Data Analysis Utilizing Machine Learning |
| title_full_unstemmed | Enhancing Ground Penetrating Radar (GPR) Data Analysis Utilizing Machine Learning |
| title_short | Enhancing Ground Penetrating Radar (GPR) Data Analysis Utilizing Machine Learning |
| title_sort | enhancing ground penetrating radar gpr data analysis utilizing machine learning |
| topic | Accuracy Classifiers Ground Penetrating Radar Seismic Images Real Oil and Gas Field |
| url | https://jeasd.uomustansiriyah.edu.iq/index.php/jeasd/article/view/2509 |
| work_keys_str_mv | AT mohanadshehab enhancinggroundpenetratingradargprdataanalysisutilizingmachinelearning AT musabtsalkaltakchi enhancinggroundpenetratingradargprdataanalysisutilizingmachinelearning AT ammardukhan enhancinggroundpenetratingradargprdataanalysisutilizingmachinelearning AT wailokwoo enhancinggroundpenetratingradargprdataanalysisutilizingmachinelearning |