Application of Alford rotation to ground-penetrating radar data

Geophysics ◽  
2001 ◽  
Vol 66 (6) ◽  
pp. 1781-1792 ◽  
Author(s):  
Jean‐Paul Van Gestel ◽  
Paul L. Stoffa
Keyword(s):  
Ground Penetrating Radar ◽  
Field Survey ◽  
Test Site ◽  
Radar Data ◽  
Practical Implementation ◽  
Good Prediction ◽  
Buried Objects ◽  
Rotation Method ◽  
Homogeneous Soil ◽  
Ground Penetrating

We investigate the application of Alford rotation to ground‐penetrating radar (GPR) data. By recording the reflected field amplitudes using four different configurations, we extract information about the orientation of buried objects that have angle‐dependent reflectivity. In theory this method can be successfully applied to find the orientation of dipping layers, cylinders, and vertical fractures. Modeling results show angle‐dependent reflections in all three cases; as a result, we can exactly determine the orientation of these targets. Analysis of a field survey at a controlled GPR test site in which reflections were collected from an elongate cylinder buried in a homogeneous soil show good prediction of the angle of orientation of the cylinder and confirm the expected theoretical and modeling results. The Alford rotation method requires accurate data acquisition for effective practical implementation. Improved results will require exact knowledge of the radiation pattern of the GPR antennas under different circumstances.

scholarly journals Characteristics and small-scale variability of GPR signals and their relation to snow accumulation in Greenland’s percolation zone

2008 ◽  
Vol 54 (185) ◽  
pp. 333-342 ◽  
Author(s):  
Thorben Dunse ◽  
Olaf Eisen ◽  
Veit Helm ◽  
Wolfgang Rack ◽  
Daniel Steinhage ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Test Site ◽  
Radar Data ◽  
Snow Accumulation ◽  
Small Scale ◽  
Snowpack Properties ◽  
A Site ◽  
Ground Penetrating ◽  
Percolation Zone ◽  
Bounded Sequences

AbstractWe investigate snowpack properties at a site in west-central Greenland with ground-penetrating radar (GPR), supplemented by stratigraphic records from snow pits and shallow firn cores. GPR data were collected at a validation test site for CryoSat (T05 on the Expéditions Glaciologiques Internationales au Groenland (EGIG) line) over a 100 m × 100 m grid and along 1 km sections at frequencies of 500 and 800 MHz. Several internal reflection horizons (IRHs) down to a depth of 10 m were tracked. IRHs are usually related to ice-layer clusters in vertically bounded sequences that obtain their initial characteristics near the surface during the melt season. Warm conditions in the following melt season can change these characteristics by percolating meltwater. In cold conditions, smaller melt volumes at the surface can lead to faint IRHs. The absence of simple mechanisms for internal layer origin emphasizes the need for independent dating to reliably interpret remotely sensed radar data. Our GPR-derived depth of the 2003 summer surface of 1.48 m (measured in 2004) is confirmed by snow-pit observations. The distribution of IRH depths on a 1 km scale reveals a gradient of increasing accumulation to the northeast of about 5 cm w.e. km−1. We find that point measurements of accumulation in this area are representative only over several hundred metres, with uncertainties of about 15% of the spatial mean.

scholarly journals A DEEP LEARNING APPROACH FOR URBAN UNDERGROUND OBJECTS DETECTION FROM VEHICLE-BORNE GROUND PENETRATING RADAR DATA IN REAL-TIME

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 293-299
Author(s):  
Z. Zong ◽  
C. Chen ◽  
X. Mi ◽  
W. Sun ◽  
Y. Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Ground Penetrating Radar ◽  
Large Scale ◽  
Test Site ◽  
Training Dataset ◽  
Underground Space ◽  
Buried Objects ◽  
Real Time Detection ◽  
Ground Penetrating

<p><strong>Abstract.</strong> GPRs (Ground Penetrating Radar) are widely adopted in underground space survey and mapping, because of their advantages of fast data acquisition, convenience, high imaging resolution and NDT (Non Destructive Testing) inspection. However, at present, the automation of the GPR data post-processing is low and the identification of underground objects needs expert interpretation. The heavy manual interpretation labor limits the GPR applications in large-scale urban scenarios. According to the latest research, it is still an unsolved problem to detect targets or defects in GPR data automatically and needs further exploration. In this paper, we propose a deep learning method for real-time detection of underground targets from GPR data. Seven typical targets in urban underground space are identified and labelled to construct the training dataset. The constructed dataset is consist of 489 labelled samples including rainwater wells, cables, metal/nonmetal pipes, sparse/dense steel reinforcement, voids. The training dataset is further augmented to produce more samples. DarkNet53 convolutional neural network (CNN) is trained using the constructed training dataset including realistic data and augmented data to extract features of the buried objects. And then the end-to-end YOLO detection framework is used to classify and locate the seven specific categories buried targets in the GPR data in real time. Experiments show that the automatic real-time detection method proposed in this paper can effectively detect the buried objects in the ground penetrating radar image in real time at Shenzhen test site (typical urban road scene).</p>

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

scholarly journals Ice thickness distribution of all Swiss glaciers based on extended ground-penetrating radar data and glaciological modeling

2021 ◽  
pp. 1-19
Author(s):  
Melchior Grab ◽  
Enrico Mattea ◽  
Andreas Bauder ◽  
Matthias Huss ◽  
Lasse Rabenstein ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Thickness Distribution ◽  
Radar Data ◽  
Tourism Industry ◽  
Swiss Alps ◽  
Ice Thickness ◽  
Hazard Management ◽  
Bed Topography ◽  
Ice Volume ◽  
Ground Penetrating

Abstract Accurate knowledge of the ice thickness distribution and glacier bed topography is essential for predicting dynamic glacier changes and the future developments of downstream hydrology, which are impacting the energy sector, tourism industry and natural hazard management. Using AIR-ETH, a new helicopter-borne ground-penetrating radar (GPR) platform, we measured the ice thickness of all large and most medium-sized glaciers in the Swiss Alps during the years 2016–20. Most of these had either never or only partially been surveyed before. With this new dataset, 251 glaciers – making up 81% of the glacierized area – are now covered by GPR surveys. For obtaining a comprehensive estimate of the overall glacier ice volume, ice thickness distribution and glacier bed topography, we combined this large amount of data with two independent modeling algorithms. This resulted in new maps of the glacier bed topography with unprecedented accuracy. The total glacier volume in the Swiss Alps was determined to be 58.7 ± 2.5 km3 in the year 2016. By projecting these results based on mass-balance data, we estimated a total ice volume of 52.9 ± 2.7 km3 for the year 2020. Data and modeling results are accessible in the form of the SwissGlacierThickness-R2020 data package.

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