A hyperbolic summation method to focus B-scan ground penetrating radar images: An experimental study with a stepped frequency system

2007 ◽  
Vol 49 (3) ◽  
pp. 671-676 ◽  
Author(s):  
Caner Ozdemir ◽  
Sevket Demirci ◽  
Enes Yigit ◽  
Adnan Kavak
Keyword(s):  
Experimental Study ◽  
Ground Penetrating Radar ◽  
Summation Method ◽  
Radar Images ◽  
Stepped Frequency ◽  
Ground Penetrating

scholarly journals FOCUSING GROUND PENETRATING RADAR IMAGES WITH VERTICAL OFFSET FILTERING

2011 ◽  
Vol 19 ◽  
pp. 183-195 ◽  
Author(s):  
Allen Benter ◽  
Wayne Moore ◽  
Michael Antolovich
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Images ◽  
Ground Penetrating ◽  
Offset Filtering

Seismic reflection and ground‐penetrating radar imaging of a shallow aquifer

Geophysics ◽  
1998 ◽  
Vol 63 (4) ◽  
pp. 1310-1317 ◽  
Author(s):  
Steven J. Cardimona ◽  
William P. Clement ◽  
Katharine Kadinsky‐Cade
Keyword(s):  
Water Table ◽  
Ground Penetrating Radar ◽  
Low Frequency ◽  
Shallow Aquifer ◽  
Cone Penetrometer ◽  
Gradual Change ◽  
Cone Penetration ◽  
Geotechnical Data ◽  
Radar Images ◽  
Ground Penetrating

In 1995 and 1996, researchers associated with the US Air Force’s Phillips and Armstrong Laboratories took part in an extensive geophysical site characterization of the Groundwater Remediation Field Laboratory located at Dover Air Force Base, Dover, Delaware. This field experiment offered an opportunity to compare shallow‐reflection profiling using seismic compressional sources and low‐frequency ground‐penetrating radar to image a shallow, unconfined aquifer. The main target within the aquifer was the sand‐clay interface defining the top of the underlying aquitard at 10 to 14 m depth. Although the water table in a well near the site was 8 m deep, cone penetration geotechnical data taken across the field do not reveal a distinct water table. Instead, cone penetration tests show a gradual change in electrical properties that we interpret as a thick zone of partial saturation. Comparing the seismic and radar data and using the geotechnical data as ground truth, we have associated the deepest coherent event in both reflection data sets with the sand‐clay aquitard boundary. Cone penetrometer data show the presence of a thin lens of clays and silts at about 4 m depth in the north part of the field. This shallow clay is not imaged clearly in the low‐frequency radar profiles. However, the seismic data do image the clay lens. Cone penetrometer data detail a clear change in the soil classification related to the underlying clay aquitard at the same position where the nonintrusive geophysical measurements show a change in image character. Corresponding features in the seismic and radar images are similar along profiles from common survey lines, and results of joint interpretation are consistent with information from geotechnical data across the site.

scholarly journals A Method for Cavity Scale Estimation Based on Ground-Penetrating Radar (GPR) Explorations: An Experimental Study

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Jeong-Jun Park ◽  
Yoonseok Chung ◽  
Gigwon Hong
Keyword(s):  
Experimental Study ◽  
Ground Penetrating Radar ◽  
Empirical Formula ◽  
Soil Depth ◽  
Additional Experiment ◽  
Cross Sectional ◽  
Scale Estimation ◽  
Cavity Collapse ◽  
Ground Penetrating

This study described the results of experiments comparing the cavity scales obtained from the GPR exploration with the direct excavation of the identified cavity scales. The first experiment was carried out on the actual roadway, and the additional experiment was carried out on the mock-up site to prevent the cavity collapse under the ground. It was confirmed that the soil depth of the predicted cavity and the identified cavity was similar, but the predicted cavity scales by GPR exploration overestimated the longitudinal and cross-sectional widths compared with the identified cavity scales. Based on the correlation between the cavity scales predicted by GPR exploration and the cavity scales identified in the mock-up test, an empirical formula for estimating the cavity scales was proposed.

scholarly journals Object Detection in Ground-Penetrating Radar Images Using a Deep Convolutional Neural Network and Image Set Preparation by Migration

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Kazuya Ishitsuka ◽  
Shinichiro Iso ◽  
Kyosuke Onishi ◽  
Toshifumi Matsuoka
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ground Penetrating Radar ◽  
Deep Convolutional Neural Network ◽  
Training Data ◽  
Neural Network Approach ◽  
Radar Images ◽  
Image Set ◽  
Ground Penetrating ◽  
Better Than

Ground-penetrating radar allows the acquisition of many images for investigation of the pavement interior and shallow geological structures. Accordingly, an efficient methodology of detecting objects, such as pipes, reinforcing steel bars, and internal voids, in ground-penetrating radar images is an emerging technology. In this paper, we propose using a deep convolutional neural network to detect characteristic hyperbolic signatures from embedded objects. As a first step, we developed a migration-based method to collect many training data and created 53510 categorized images. We then examined the accuracy of the deep convolutional neural network in detecting the signatures. The accuracy of the classification was 0.945 (94.5%)–0.979 (97.9%) when using several thousands of training images and was much better than the accuracy of the conventional neural network approach. Our results demonstrate the effectiveness of the deep convolutional neural network in detecting characteristic events in ground-penetrating radar images.

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