Velocity estimation by the common-reflection-surface (CRS) method: Using ground-penetrating radar data

Geophysics ◽  
2005 ◽  
Vol 70 (6) ◽  
pp. B43-B52 ◽  
Author(s):  
Hervé Perroud ◽  
Martin Tygel
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Data ◽  
Velocity Estimation ◽  
Agricultural Activities ◽  
Water Conductivity ◽  
Common Reflection Surface ◽  
The Common ◽  
The Time Domain ◽  
Ground Penetrating

In this paper, we describe the use of the common-reflection-surface (CRS) method to estimate velocities from ground-penetrating radar (GPR) data. Applied to multicoverage data, the CRS method provides, as one of its outputs, the time-domain rms velocity map, which is then converted to depth by the familiar Dix algorithm. Combination of the obtained depth-converted velocity map with electrical resistivity in-situ measurements enables us to estimate both water content and water conductivity. These quantities are essential to delineate infiltration of contaminants from the surface after industrial or agricultural activities. The method was applied to GPR data and compared with the classical NMO approach. The results show that the CRS method provides a physically more meaningful velocity field, thus improving the potential of GPR as an investigation tool for environmental studies.

scholarly journals Researching migration methods, entropy and energy diagram to process ground penetrating radar data

2017 ◽  
Vol 17 (4B) ◽  
pp. 167-174
Author(s):  
Van Nguyen Thanh ◽  
Thuan Van Nguyen ◽  
Trung Hoai Dang ◽  
Triet Minh Vo ◽  
Lieu Nguyen Nhu Vo
Keyword(s):  
Electromagnetic Wave ◽  
Wave Velocity ◽  
Ground Penetrating Radar ◽  
Radar Data ◽  
Maximum Energy ◽  
Velocity Estimation ◽  
Data Migration ◽  
Minimum Entropy ◽  
Energy Diagram ◽  
Ground Penetrating

Electromagnetic wave velocity is the most important parameter in processing ground penetrating radar data. Migration algorithm which heavily depends on wave velocity is used to concentrate scattered signals back to their correct locations. Depending wave velocity in urban area is not easy task by using traditional methods (i.e., common midpoint). We suggest using entropy and energy diagram as standard for achieving suitable velocity estimation. The results of one numerical model and areal data indicate that migrated section using accurate velocity has minimum entropy or maximum energy. From the interpretation, size and depth of anomalies are reliably identified.

Analysis of ground-probing radar data: predictive deconvolution

1991 ◽  
Vol 28 (1) ◽  
pp. 134-139 ◽  
Author(s):  
P. T. Lafleche ◽  
J. P. Todoeschuck ◽  
O. G. Jensen ◽  
A. S. Judge
Keyword(s):  
Ground Penetrating Radar ◽  
Seismic Data ◽  
Prediction Error ◽  
Radar Data ◽  
Digital Data ◽  
Data Sets ◽  
Predictive Deconvolution ◽  
The Common ◽  
Ground Penetrating ◽  
Processing Techniques

Recent advances in ground-probing radar instrumentation have allowed the collection of large volumes of digital data. Such data sets are amenable to modern data-processing techniques both to increase geological resolution and to enhance data presentation. The close similarity between ground-radar data and seismic data suggests that processing techniques that have been used in the seismic industry could be applied to radar data. As an example, a ground probing radar profile is deconvolved using the common prediction-error filter, which assumes a white power spectrum for the reflections, and a filter that assumes a spectrum proportional to spatial frequency. With the prediction-error filter we find three of four buried pipes which are not visible in the undeconvolved section; all four are found with the second filter. Key words: ground-penetrating radar, deconvolution, scaling geology, frozen-core dams, permafrost, containment dams, mill waste, Contwoyto Lake.

scholarly journals Combination of Kirchhoff migration method and the energy diagram in the process of ground penetrating radar data

2015 ◽  
Vol 18 (4) ◽  
pp. 42-50
Author(s):  
Van Thanh Nguyen ◽  
Thuan Van Nguyen ◽  
Trung Hoai Dang
Keyword(s):  
Ground Penetrating Radar ◽  
High Reliability ◽  
Theoretical Models ◽  
Radar Data ◽  
Velocity Estimation ◽  
Energy Diagram ◽  
Subsurface Structures ◽  
Kirchhoff Migration ◽  
Migration Method ◽  
Ground Penetrating

Kirchhoff migration in ground penetrating radar (GPR) has been the technique of collapsing diffraction events on unmigrated records to points, thus moving reflection events to their proper locations and creating a true image of subsurface structures. Today, the scope of Kirchhoff migration has been broadened and is a tool for electromagnetic wave velocity estimation. To optimize this algorithm, we propose using the energy diagram as a criterion of looking for the correct propagation velocity. Using theoretical models, we demonstrated that the calculated velocities were the same as the root mean square ones up to the top of objects. The results verified on field data showed that improved sections could be obtained and the size as well as depth of anomalies were determined with high reliability.

Deep learning-based automated underground cavity detection using three-dimensional ground penetrating radar

2019 ◽  
Vol 19 (1) ◽  
pp. 173-185 ◽  
Author(s):  
Man-Sung Kang ◽  
Namgyu Kim ◽  
Jong Jae Lee ◽  
Yun-Kyu An
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ground Penetrating Radar ◽  
Three Dimensional ◽  
Radar Data ◽  
Underground Cavity ◽  
Deep Convolutional Neural Networks ◽  
Cavity Detection ◽  
Ground Penetrating

Three-dimensional ground penetrating radar data are often ambiguous and complex to interpret when attempting to detect only underground cavities because ground penetrating radar reflections from various underground objects can appear like those from cavities. In this study, we tackle the issue of ambiguity by proposing a system based on deep convolutional neural networks, which is capable of autonomous underground cavity detection beneath urban roads using three-dimensional ground penetrating radar data. First, a basis pursuit-based background filtering algorithm is developed to enhance the visibility of underground objects. The deep convolutional neural network is then established and applied to automatically classify underground objects using the filtered three-dimensional ground penetrating radar data as represented by three types of images: A-, B-, and C-scans. In this study, we utilize a novel two-dimensional grid image consisting of several B- and C-scan images. Cavity, pipe, manhole, and intact features extracted from in situ three-dimensional ground penetrating radar data are used to train the convolutional neural network. The proposed technique is experimentally validated using real three-dimensional ground penetrating radar data obtained from urban roads in Seoul, South Korea.

scholarly journals Applying reflection tomography in the postmigration domain to multifold ground-penetrating radar data

Geophysics ◽  
2006 ◽  
Vol 71 (1) ◽  
pp. K1-K8 ◽  
Author(s):  
John H. Bradford
Keyword(s):  
Ground Penetrating Radar ◽  
Accurate Method ◽  
Radar Data ◽  
Velocity Estimation ◽  
Lateral Velocity ◽  
Velocity Models ◽  
Contaminated Waste ◽  
Ground Penetrating ◽  
Migration Pathways ◽  
Reflection Tomography

Acquisition and processing of multifold ground-penetrating radar (GPR) data enable detailed measurements of lateral velocity variability. The velocities constrain interpretation of subsurface materials and lead to significant improvement in image accuracy when coupled with prestack depth migration (PSDM). Reflection tomography in the postmigration domain was introduced in the early 1990s for velocity estimation in seismic reflection. This robust, accurate method is directly applicable in multifold GPR imaging. At a contaminated waste facility within the U.S. Department of Energy's Hanford site in Washington, the method is used to identify significant lateral and vertical velocity heterogeneity associated with infilled waste pits. Using both the PSDM images and velocity models in interpretation, a paleochannel system that underlies the site and likely forms contaminant migration pathways is identified.

Reconstructing Properties of Subsurface from Ground-Penetrating Radar Data

PIERS Online ◽  
2006 ◽  
Vol 2 (6) ◽  
pp. 567-572
Author(s):  
Hui Zhou ◽  
Dongling Qiu ◽  
Takashi Takenaka
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Data ◽  
Ground Penetrating
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