scholarly journals Estimating vadose zone hydraulic properties using ground penetrating radar: The impact of prior information

2011 ◽  
Vol 47 (10) ◽  
Author(s):  
Marie Scholer ◽  
James Irving ◽  
Andrew Binley ◽  
Klaus Holliger
Keyword(s):  
Vadose Zone ◽  
Ground Penetrating Radar ◽  
Hydraulic Properties ◽  
Prior Information ◽  
Ground Penetrating ◽  
The Impact

Improved Solute-Sampling Protocol in a Sandy Vadose Zone Using Ground-Penetrating Radar

1991 ◽  
Vol 55 (6) ◽  
pp. 1543-1545 ◽  
Author(s):  
K-J. S. Kung ◽  
S. V. Donohue
Keyword(s):  
Vadose Zone ◽  
Ground Penetrating Radar ◽  
Sampling Protocol ◽  
Ground Penetrating

scholarly journals Working with Gaussian Random Noise for Multi-Sensor Archaeological Prospection: Fusion of Ground Penetrating Radar Depth Slices and Ground Spectral Signatures from 0.00 m to 0.60 m below Ground Surface

2019 ◽  
Vol 11 (16) ◽  
pp. 1895 ◽  
Author(s):  
Agapiou ◽  
Sarris
Keyword(s):  
Regression Model ◽  
Normal Distribution ◽  
Ground Penetrating Radar ◽  
Regression Models ◽  
Random Noise ◽  
Ground Surface ◽  
Archaeological Prospection ◽  
Spectral Signatures ◽  
Ground Penetrating ◽  
The Impact

The integration of different remote sensing datasets acquired from optical and radar sensors can improve the overall performance and detection rate for mapping sub-surface archaeological remains. However, data fusion remains a challenge for archaeological prospection studies, since remotely sensed sensors have different instrument principles, operating in different wavelengths. Recent studies have demonstrated that some fusion modelling can be achieved under ideal measurement conditions (e.g., simultaneously measurements in no hazy days) using advance regression models, like those of the nonlinear Bayesian Neural Networks. This paper aims to go a step further and investigate the impact of noise in regression models, between datasets obtained from ground-penetrating radar (GPR) and portable field spectroradiometers. Initially, the GPR measurements provided three depth slices of 20 cm thickness, starting from 0.00 m up to 0.60 m below the ground surface while ground spectral signatures acquired from the spectroradiometer were processed to calculate 13 multispectral and 53 hyperspectral indices. Then, various levels of Gaussian random noise ranging from 0.1 to 0.5 of a normal distribution, with mean 0 and variance 1, were added at both GPR and spectral signatures datasets. Afterward, Bayesian Neural Network regression fitting was applied between the radar (GPR) versus the optical (spectral signatures) datasets. Different regression model strategies were implemented and presented in the paper. The overall results show that fusion with a noise level of up to 0.2 of the normal distribution does not dramatically drop the regression model between the radar and optical datasets (compared to the non-noisy data). Finally, anomalies appearing as strong reflectors in the GPR measurements, continue to provide an obvious contrast even with noisy regression modelling.

Detection of Subsurface Hydrocarbons in the Vadose Zone with Ground Penetrating Radar — A Case Study

1993 ◽  
Author(s):  
Wayne R. Saunders ◽  
Michael Wightman ◽  
Robert Windschauer
Keyword(s):  
Vadose Zone ◽  
Ground Penetrating Radar ◽  
Ground Penetrating

Qualitative research: The impact of root orientation on coarse roots detection using ground-penetrating radar (GPR)

BioResources ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 2237-2257
Author(s):  
Mingkai Wang ◽  
Jian Wen ◽  
Wenbin Li
Keyword(s):  
Ground Penetrating Radar ◽  
Simulation Experiment ◽  
Simulation Software ◽  
Dielectric Constants ◽  
Three Dimensions ◽  
Coarse Roots ◽  
Coarse Root ◽  
Ground Penetrating ◽  
The Impact ◽  
Root Orientation

The growth of coarse roots is complex, leading to intricate patterns of root systems in three dimensions. To detect and recognize coarse roots, ground-penetrating radar (GPR) was used. According to the GPR theory, a clear profile hyperbola is formed on the GPR radargrams when electromagnetic waves travel across two surfaces with different dielectric constants. First, the forward models (different root orientations) were built with simulation software (GprMax3.0) based on the finite-different time-domain method (FDTD). As the radar moved forward, the signal reflection curve was generated in different root orientations. An algorithm was proposed to obtain the coordinates of a single coarse root and analyze the influence of root direction on the hyperbola of coarse root through a symmetry curve and relative error (RE). Based on GPR datasets from the simulation experiment, the controlled experiment evaluated feasibility and effectiveness of the simulation experiment. To demonstrate the effect of the root orientation, the algorithm was applied to in situ recognition of the Summer Palace. The results showed that the localization of root orientation was relatively accurate. However, the proposed algorithm was unable to implement automatic detection, and the results still required human intervention. This research provides a solid basis for the biomass measurement, diameter estimation, and especially the three-dimensional reconstruction of ancient and famous trees.

scholarly journals Bayesian inverse modelling of in situ soil water dynamics: using prior information about the soil hydraulic properties

2011 ◽  
Vol 8 (1) ◽  
pp. 2019-2063 ◽  
Author(s):  
B. Scharnagl ◽  
J. A. Vrugt ◽  
H. Vereecken ◽  
M. Herbst
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Vadose Zone ◽  
Hydraulic Properties ◽  
Prior Information ◽  
Hydraulic Parameters ◽  
Soil Hydraulic Properties ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic

Abstract. In situ observations of soil water state variables under natural boundary conditions are often used to estimate field-scale soil hydraulic properties. However, many contributions to the soil hydrological literature have demonstrated that the information content of such data is insufficient to reliably estimate all the soil hydraulic parameters. In this case study, we tested whether prior information about the soil hydraulic properties could help improve the identifiability of the van Genuchten-Mualem (VGM) parameters. Three different prior distributions with increasing complexity were formulated using the ROSETTA pedotransfer function (PTF) with input data that constitutes basic soil information and is readily available in most vadose zone studies. The inverse problem was posed in a formal Bayesian framework and solved using Markov chain Monte Carlo (MCMC) simulation with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Synthetic and real-world soil water content data were used to illustrate our approach. The results of this study corroborate and explicate findings previously reported in the literature. Indeed, soil water content data alone contained insufficient information to reasonably constrain all VGM parameters. The identifiability of these soil hydraulic parameters was substantially improved when an informative prior distribution was used with detailed knowledge of the correlation structure among the respective VGM parameters. A biased prior did not distort the results, which inspires confidence in the robustness and effectiveness of the presented method. The Bayesian framework presented in this study can be applied to a wide range of vadose zone studies and provides a blueprint for the use of prior information in inverse modelling of soil hydraulic properties at various spatial scales.

Traveltime tomography of crosshole ground-penetrating radar based on an arctangent functional with compactness constraints

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. H1-H14 ◽  
Author(s):  
Shufan Hu ◽  
Yonghui Zhao ◽  
Tan Qin ◽  
Chunfeng Rao ◽  
Cong An
Keyword(s):  
Ground Penetrating Radar ◽  
Gradient Algorithm ◽  
Order Differential Operator ◽  
Data Set ◽  
Actual Structure ◽  
Trade Off ◽  
Traveltime Tomography ◽  
Ground Penetrating ◽  
The Impact ◽  
Low Order

Regularization has been an effective technique to provide unique and stable solutions for crosshole ground-penetrating radar (GPR) traveltime tomography. The traditional form of this method, in which a low-order differential operator was used, commonly yields a smooth solution that may not be appropriate when anomalies occur in block patterns, such as voids or irregular objects. The minimum support (MS) functional can be used to improve the resolution of blocky structures; however, in crosshole GPR traveltime tomography, the MS functional is unable to resolve residual artifacts, whose departure from an a priori model are smaller than the focusing parameter selected from a trade-off curve. In addition, it would result in severe instability and yield a trade-off curve with poorly defined corners when the focusing parameter nears the precision of the apparatus. We have developed a new stabilizing functional based on the arctangent (AT) function that effectively removes the artificially small values in the crosshole GPR traveltime tomography, and ultimately is more efficient because it does not require the user to select a focusing parameter. We inverted three 2D synthetic data sets based on the reweighted regularized conjugate gradient algorithm. Compared with the low-order differential and MS functional, the user will be able to clearly distinguish the anomaly boundary using this method, which will yield results that are closer to the actual structure. We also discussed the impact of some influencing factors caused by the noise contained in the data, the central frequency of the antenna, the anomalous trends, and the ray coverage angle. We further inverted an experimental data set to test the effectiveness and robustness of the method.

scholarly journals Symmetry between Theoretical and Physical Investigation of Water Contamination Using Amplitude Variation with Offset Analysis of Ground-Penetrating Radar Data

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 991
Author(s):  
Ibrar Iqbal ◽  
Gang Tian ◽  
Zhejiang Wang ◽  
Zahid Masood ◽  
Yu Liu ◽  
...  
Keyword(s):  
Aqueous Phase ◽  
Ground Penetrating Radar ◽  
Water Contamination ◽  
Experimental Models ◽  
Theoretical Modeling ◽  
Avo Analysis ◽  
Geophysical Surveys ◽  
Phase Liquid ◽  
Ground Penetrating ◽  
The Impact

We evaluated the symmetry of theoretical and experimental analysis of water contamination such as non-aqueous phase liquid (NAPL) by using amplitude variations with offset analysis (AVO) of ground-penetrating radar (GPR) data. We used both theoretical and experimental approaches for AVO responses of GPR to small distributions of contamination. Theoretical modeling is a tool used to confirm the feasibility of geophysical surveys. Theoretical modeling of NAPL-contaminated sites containing wet sand—both with the water and light non-aqueous phase liquid—was applied by keeping in consideration the GPR AVO analysis in acquisition. Reflectivity was significantly altered with the changes in the contents of water and NAPL during modeling. The wet and dry sands introduced in our model changed two major phenomena: one, the wave pattern—implying a slight phase shift in the wave; and two, an amplitude jump with the dim reflection radar gram observed in the model. Experimental data were collected and analyzed; two observations were recorded during physical data analysis. First, relative permittivity confirmed the presence of NAPL in an experimental tank. Second, reflection patterns with jumps in amplitude and changes in polarity confirmed the theoretical investigation. Our results demonstrate that GPR AVO analysis can be as effective for detection of non-aqueous phase liquid (NAPLs) as it has been used to determine moisture contents in the past. The theoretical and experimental models were in symmetry, and both found a jump in reflection strength. The reflection pattern normally jumped with NAPL-intrusion. From the perspective of water contamination, this study emphasizes the need to take into account the impact of GPR AVO analyses along with the expert’s adaptive capacities.

Estimating moisture contents in the vadose zone using cross-borehole ground penetrating radar: A study of accuracy and repeatability

2002 ◽  
Vol 38 (12) ◽  
pp. 45-1-45-12 ◽  
Author(s):  
David Alumbaugh ◽  
Ping Yu Chang ◽  
Lee Paprocki ◽  
James R. Brainard ◽  
Robert J. Glass ◽  
...  
Keyword(s):  
Vadose Zone ◽  
Ground Penetrating Radar ◽  
Moisture Contents ◽  
Ground Penetrating ◽  
Accuracy And Repeatability

Signal Stability and the Height-Correction Method for Ground-Penetrating Radar In Situ Asphalt Concrete Density Prediction

2021 ◽  
pp. 036119812110045
Author(s):  
Qingqing Cao ◽  
Imad L. Al-Qadi
Keyword(s):  
Adaptive Filter ◽  
Ground Penetrating Radar ◽  
Asphalt Concrete ◽  
Correction Method ◽  
Least Square ◽  
Least Mean Square ◽  
Signal Stability ◽  
Density Prediction ◽  
Ground Penetrating ◽  
The Impact

Ground-penetrating radar (GPR) has shown great potential for asphalt concrete density prediction used in quality control and quality assurance. One challenge of continuous GPR measurements is that the measured dielectric constant could be affected by signal stability and antenna height. This would jeopardize the accuracy of the asphalt concrete density prediction along the pavement. In this study, signal instability and shifting antenna height during continuous real-time GPR measurements were identified as main sources of error. After using a bandpass filter to preprocess the signal, a least-square adaptive filter, using gradient descent and least mean square methods, was developed to reconstruct the received signal to improve its stability. In addition, simulations were performed to evaluate the impact of geometric spreading caused by shifting antenna height during testing. A height correction was developed using a power model to correct the height-change impact. The proposed filter and height-correction method were assessed using static and dynamic tests. The least-square adaptive filter improved signal stability by 50% and the height-correction method removed the effect of shifting antenna height almost entirely.

scholarly journals The Impact of Frequency in Surveying Engineering Slopes Using Ground Penetrating Radar

2017 ◽  
Vol 08 (03) ◽  
pp. 296-304
Author(s):  
Angelo Indelicato
Keyword(s):  
Ground Penetrating Radar ◽  
Ground Penetrating ◽  
The Impact
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