Near Surface Velocity Estimation Using GPR Data: Investigations by Numerical Simulation, and Experimental Approach with AVO Response
"> Figure 1
<p>GPR data processing flow chart, containing the complete processing and analysis steps.</p> "> Figure 2
<p>(<b>a</b>) Numerical model display of four mediums with <b>ϵ<sub>r</sub></b> representing relative permittivity of free space and <b>σ</b> representing the electrical conductivity of different mediums. (<b>b</b>) Graphical representation of the subsurface mathematical model; red and blue lines represent conductivity and relative permittivity, respectively.</p> "> Figure 3
<p>Velocity spectrum analysis of the GPR experimental data: (<b>a</b>) The simulated velocity model of subsurface materials; (<b>b</b>) the corresponding velocity amplitude spectrum with maximum velocity amplitude at 14ns is also shown in the figure; (<b>c</b>) the corresponding velocity amplitude curve with shift caused by the introduction of the next interface, with maximum velocity spectrum at 15 ns.</p> "> Figure 4
<p>(<b>a</b>) Pre stack CMP gathers of GPR data while R1 and R2 indicate the two reflectors, respectively. (<b>b</b>) Post stack CMP gathers of GPR data; and R1, R2 represent the reflector 1 and reflector 2, respectively.</p> "> Figure 5
<p>Normal Moveout corrected AVO velocity model, containing three velocities with two different reflectors; velocity is associated with the color contrast.</p> "> Figure 6
<p>(<b>a</b>) Correlated velocity line with the model and with the pre-stacks data yields the clear depth of the reflectors, and the red line indicates the cutoff point of reflectors; (<b>b</b>) Velocity model generated by using Common Midpoint (CMP) GPR data (<b>c</b>) Velocity model generated by using Amplitude variation with offset (AVO) analysis light blue and yellow colors indicate the stacking of GPR AVO data.</p> "> Figure 7
<p>(<b>a</b>) GPR synthetic data acquisition system with GPR pulse ekko unshielded and separate transmitting and receiving antennas. (<b>b</b>) Drilling machine setup and sample collection; a borehole used to extract subsurface samples for velocity measurement and experimental velocity acquisition at the test site at Zhejiang University, Hangzhou, China.</p> "> Figure 8
<p>Velocity trend using dielectric constant values and the general trend with depth; layers 1, 2, and 3 are those reflectors shown in a synthetic study.</p> ">
Abstract
:1. Introduction
2. Methods
2.1. Principle
2.2. Data Acquisition
2.3. Data Processing
3. Results and Discussion
3.1. Numerical Simulation Analysis
3.2. GPR Data Analysis with Amplitude Variation with Offset (AVO)
3.3. Experimental Velocity Analysis
3.4. Velocity Estimation Using Dielectric Constant
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Values |
---|---|
a. Step Size | 0.1 m and 0.00 |
b. Stacking | 64 |
c. Sample Rate | 0.6 ns |
d. Time Window | 500 ns |
Depth (m) | AVO Velocities (m/nsec) | CDP Velocities (m/nsec) | Lithology For the Dielectric Constant |
Experimental Velocities via Dielectric Constant (m/nsec) |
---|---|---|---|---|
1.0 | 0.1152 | 3.9 | Dry Sand Stone | 0.23717 |
1.5 | 0.300 | 4.121 | Dry Sand Stone | 0.20226 |
2.0 | 0.965 | 4.1 | Wet Sand Stone | 0.13553 |
2.5 | 0.123 | 4.52 | Wet Sand Stone | 0.15390 |
3.0 | 0.500 | 5.0 | Clay | 0.14639 |
3.5 | 0.366 | 0.56 | Wet Clay | 0.20702 |
4.0 | 0.136 | 0.122 | Wet Clay | 0.17617 |
4.5 | 0.120 | 0.125 | Wet Clay | 0.13416 |
5.0 | 0.139 | 0.622 | Wet Clay | 0.12247 |
5.5 | 0.1639 | 0.750 | Clay | 0.19365 |
6.0 | 0.122 | 3.1 | Granite | 0.15191 |
6.5 | 0.600 | 3.1 | Wet Granite | 0.11310 |
7.0 | 0.160 | 3.1 | Wet Granite | 0.15596 |
7.5 | 0.4213 | 0.45 | Clay | 0.15390 |
8.0 | 0.1222 | 0.52 | Wet Clay | 0.1300 |
8.5 | 0.2121 | 0.52 | Wet Clay | 0.23717 |
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Iqbal, I.; Bin, X.; Tian, G.; Wang, H.; Sanxi, P.; Yang, Y.; Masood, Z.; Hanwu, S. Near Surface Velocity Estimation Using GPR Data: Investigations by Numerical Simulation, and Experimental Approach with AVO Response. Remote Sens. 2021, 13, 2814. https://doi.org/10.3390/rs13142814
Iqbal I, Bin X, Tian G, Wang H, Sanxi P, Yang Y, Masood Z, Hanwu S. Near Surface Velocity Estimation Using GPR Data: Investigations by Numerical Simulation, and Experimental Approach with AVO Response. Remote Sensing. 2021; 13(14):2814. https://doi.org/10.3390/rs13142814
Chicago/Turabian StyleIqbal, Ibrar, Xiong Bin, Gang Tian, Honghua Wang, Peng Sanxi, Yang Yang, Zahid Masood, and Sun Hanwu. 2021. "Near Surface Velocity Estimation Using GPR Data: Investigations by Numerical Simulation, and Experimental Approach with AVO Response" Remote Sensing 13, no. 14: 2814. https://doi.org/10.3390/rs13142814