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.

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.

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.

A critical analysis on the uncertainty computation in ground-penetrating radar-retrieved dry snow parameters

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. H39-H49
Author(s):  
Federico Di Paolo ◽  
Barbara Cosciotti ◽  
Sebastian E. Lauro ◽  
Elisabetta Mattei ◽  
Elena Pettinelli
Keyword(s):  
Wave Velocity ◽  
Ground Penetrating Radar ◽  
Snow Water Equivalent ◽  
Radar Data ◽  
Published Data ◽  
Physical Parameters ◽  
Large Area ◽  
Essential Information ◽  
Two Parameters ◽  
Ground Penetrating

The use of the ground-penetrating-radar (GPR) technique to estimate snow parameters such as thickness, density, and snow water equivalent (SWE) is particularly promising because it allows for surveying a large area in a relatively short amount of time. However, this application requires an accurate evaluation of the physical parameters retrieved from the radar measurements, which requires estimating each quantity involved in the computation along with its associated uncertainty. Conversely, the uncertainties are rarely reported in GPR snow studies, even if they represent essential information for data comparisons with other techniques such as the snow rod or snow pit methods. Snow parameters can be estimated from radar data as follows: The snow thickness can be computed from two-way traveltime if the snow average wave velocity is known; the snow density can be estimated from wave velocity using an appropriate mixing formula, and SWE can be computed once these two parameters have been calculated. Starting from published data, we have estimated the accuracy achievable by computing the overall uncertainty for each GPR-retrieved snow parameter and evaluated the influence of the different sources of uncertainties. The computation was made for three antenna frequencies (250, 500, and 1000 MHz) and various snow depths (0–5 m). We find that for snow thicknesses of less than 3 m, the main contribution to the uncertainties associated with snow parameters is given by the uncertainty on two-way traveltime estimation, especially for low antenna frequencies. However, for thicker snow depths, other factors such as the uncertainty on the antenna separation affect the overall accuracy and cannot be neglected. Our studies highlight the importance of the uncertaintiy assessment and suggest a rigorous way for their computation in the field of quantitative geophysics.

Recognizing surface scattering in ground‐penetrating radar data

Geophysics ◽  
1995 ◽  
Vol 60 (5) ◽  
pp. 1378-1385 ◽  
Author(s):  
Jingsheng Sun ◽  
Roger A. Young
Keyword(s):  
Wave Velocity ◽  
Ground Penetrating Radar ◽  
Radar Data ◽  
Relative Strength ◽  
Surface Scattering ◽  
Geological Interpretation ◽  
Vertical Scale ◽  
Survey Line ◽  
Ground Penetrating ◽  
Ground Wave

Ground‐penetrating radar (GPR) data may show strong noise events as a result of scattering by surface objects on the ground or above the survey line. The relative strength of these events can be large in comparison to reflections from geologic features, because radar signals in the ground attenuate exponentially whereas signals that travel in the air attenuate geometrically. Migration of GPR field data from clastic and carbonate sequences in central Oklahoma distinguishes between scattered events and geologic events because the former are focused at the air‐wave velocity, while the latter are focused at the ground‐wave velocity. Forward modeling using locations of scatterers derived from migration confirms the presence of scattered events, and common midpoint (CMP) gathers are helpful in identifying surface scattering. Scattered events displayed at a horizontal/vertical scale of 1:1 are easily mistaken for subhorizontal, geologic reflections. Methods of recognizing scattered events and removing them, if possible, are therefore crucial to correct geological interpretation of GPR data.

The Research of the Ground Penetrating Radar Wave Velocity Estimation Method Based on QR Decomposition

2014 ◽  
Vol 522-524 ◽  
pp. 1197-1201 ◽  
Author(s):  
Xu Qiao ◽  
Wan Jun Ji ◽  
Kai Zhu ◽  
Feng Yang
Keyword(s):  
Real Time ◽  
Wave Velocity ◽  
Ground Penetrating Radar ◽  
Estimation Method ◽  
Time Estimation ◽  
Spatial Location ◽  
Qr Decomposition ◽  
Velocity Estimation ◽  
Ground Penetrating ◽  
Radar Wave

Ground penetrating radar (GPR) is a kind of geophysical instruments, which has been widely applied in geology, engineering, resource, environment, military etc. The method mentioned in this paper based on QR decomposition, by solving equations to find the estimated value of the radar wave velocity. This method delivers the features of real-time, accurate, and the accuracy required is not high. It is suitable for real time estimation of GPR detection. By estimating the wave velocity, we can identify the spatial location of underground target. This method provides a convenient way for the detection of underground target.

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