Applying 2-D resistivity imaging and ground penetrating radar (GPR) methods to identify infiltration of water in the ground surface

2017 ◽  
Author(s):  
Azim Hilmy Mohamad Yusof ◽  
Muhamad Iqbal Mubarak Faharul Azman ◽  
Nur Azwin Ismail ◽  
Noer El Hidayah Ismail
Keyword(s):  
Ground Penetrating Radar ◽  
Ground Surface ◽  
Resistivity Imaging ◽  
Ground Penetrating

scholarly journals Estimating snow water equivalent over long mountain transects using snowmobile-mounted ground-penetrating radar

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA183-WA193 ◽  
Author(s):  
W. Steven Holbrook ◽  
Scott N. Miller ◽  
Matthew A. Provart
Keyword(s):  
Ground Penetrating Radar ◽  
Snow Water Equivalent ◽  
Ground Surface ◽  
Snow Accumulation ◽  
Accurate Estimation ◽  
Snow Density ◽  
Snow Stratigraphy ◽  
Flood Estimation ◽  
Snow Water ◽  
Ground Penetrating

The water balance in alpine watersheds is dominated by snowmelt, which provides infiltration, recharges aquifers, controls peak runoff, and is responsible for most of the annual water flow downstream. Accurate estimation of snow water equivalent (SWE) is necessary for runoff and flood estimation, but acquiring enough measurements is challenging due to the variability of snow accumulation, ablation, and redistribution at a range of scales in mountainous terrain. We have developed a method for imaging snow stratigraphy and estimating SWE over large distances from a ground-penetrating radar (GPR) system mounted on a snowmobile. We mounted commercial GPR systems (500 and 800 MHz) to the front of the snowmobile to provide maximum mobility and ensure that measurements were taken on pristine snow. Images showed detailed snow stratigraphy down to the ground surface over snow depths up to at least 8 m, enabling the elucidation of snow accumulation and redistribution processes. We estimated snow density (and thus SWE, assuming no liquid water) by measuring radar velocity of the snowpack through migration focusing analysis. Results from the Medicine Bow Mountains of southeast Wyoming showed that estimates of snow density from GPR ([Formula: see text]) were in good agreement with those from coincident snow cores ([Formula: see text]). Using this method, snow thickness, snow density, and SWE can be measured over large areas solely from rapidly acquired common-offset GPR profiles, without the need for common-midpoint acquisition or snow cores.

scholarly journals Study Effect of Objects Orientation in the Mediums On GPR Signal Reflections Using FDTD Simulation

2018 ◽  
Vol 3 (11) ◽  
pp. 73-77
Author(s):  
Aye Mint Mohamed Mostapha ◽  
Gamil Alsharahi ◽  
Abdellah Driouach
Keyword(s):  
Finite Difference ◽  
Time Domain ◽  
Ground Penetrating Radar ◽  
High Frequency ◽  
Electromagnetic Waves ◽  
Ground Surface ◽  
Propagation Path ◽  
Fdtd Simulation ◽  
Ground Penetrating ◽  
Dielectric Objects

Ground penetrating radar (GPR) is a very effective tool for detecting and identifying objects below the ground surface.  based on  the propagation and reflection of high-frequency electromagnetic waves. The GPR reflection can be affected by many things like the type of objects orientation, their shapes ..ect. The purpose of this paper is to  study by simulation the effect of objects orientation in two different mediums (dry and wet sand) on the GPR signal reflection using Reflexw software which is based on a numerical method known as finite difference in time domain (FDTD).  The simulations that have been realized included a conductor  and dielectric objects. The results obtained have led us to find that the propagation path, the reflection strength and the signal form change with the change of object orientation and nature. To confirm the validity of the results, we compared them with experimental results previously published by researchers under the same conditions.

scholarly journals GPR Signal Processing with Geography Adaptive Scanning using Vector Radar for Antipersonal Landmine Detection

10.5772/5696 ◽  
2007 ◽  
Vol 4 (2) ◽  
pp. 22 ◽  
Author(s):  
Toshio Fukuda ◽  
Yasuhisa Hasegawa ◽  
Yasuhiro Kawai ◽  
Shinsuke Sato ◽  
Zakarya Zyada ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Ground Surface ◽  
Landmine Detection ◽  
Image Objects ◽  
Kirchhoff Migration ◽  
Surface Image ◽  
Buried Object ◽  
Ground Penetrating ◽  
The Relationship ◽  
Object Imaging

Ground Penetrating Radar (GPR) is a promising sensor for landmine detection, however there are two major problems to overcome. One is the rough ground surface. The other problem is the distance between the antennas of GPR. It remains irremovable clutters on a sub-surface image output from GPR by first problem. Geography adaptive scanning is useful to image objects beneath rough ground surface. Second problem makes larger the nonlinearity of the relationship between the time for propagation and the depth of a buried object, imaging the small objects such as an antipersonnel landmine closer to the antennas. In this paper, we modify Kirchhoff migration so as to account for not only the variation of position of the sensor head, but also the antennas alignment of the vector radar. The validity of this method is discussed through application to the signals acquired in experiments.

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.

Sign in / Sign up

Export Citation Format

Share Document