scholarly journals Entropy-Based Clutter Rejection for Intrawall Diagnostics

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Raffaele Solimene ◽  
Antonietta D'Alterio
Keyword(s):  
Ground Penetrating Radar ◽  
High Amplitude ◽  
Masonry Structure ◽  
Front Face ◽  
Multiple Reflections ◽  
Rejection Algorithm ◽  
Radar Images ◽  
Clutter Rejection ◽  
Entropy Measures ◽  
Ground Penetrating

The intrawall diagnostic problem of detecting localized inhomogeneities possibly present within the wall is addressed. As well known, clutter arising from masonry structure can impair detection of embedded scatterers due to high amplitude reflections that wall front face introduces. Moreover, internal multiple reflections also can make it difficult ground penetrating radar images (radargramms) interpretation. To counteract these drawbacks, a clutter rejection method, properly tailored on the wall features, is mandatory. To this end, here we employ a windowing strategy based on entropy measures of temporal traces “similarity.” Accordingly, instants of time for which radargramms exhibit entropy values greater than a prescribed threshold are “silenced.” Numerical results are presented in order to show the effectiveness of the entropy-based clutter rejection algorithm. Moreover, a comparison with the standard average trace subtraction is also included.

Seismic reflection and ground‐penetrating radar imaging of a shallow aquifer

Geophysics ◽  
1998 ◽  
Vol 63 (4) ◽  
pp. 1310-1317 ◽  
Author(s):  
Steven J. Cardimona ◽  
William P. Clement ◽  
Katharine Kadinsky‐Cade
Keyword(s):  
Water Table ◽  
Ground Penetrating Radar ◽  
Low Frequency ◽  
Shallow Aquifer ◽  
Cone Penetrometer ◽  
Gradual Change ◽  
Cone Penetration ◽  
Geotechnical Data ◽  
Radar Images ◽  
Ground Penetrating

In 1995 and 1996, researchers associated with the US Air Force’s Phillips and Armstrong Laboratories took part in an extensive geophysical site characterization of the Groundwater Remediation Field Laboratory located at Dover Air Force Base, Dover, Delaware. This field experiment offered an opportunity to compare shallow‐reflection profiling using seismic compressional sources and low‐frequency ground‐penetrating radar to image a shallow, unconfined aquifer. The main target within the aquifer was the sand‐clay interface defining the top of the underlying aquitard at 10 to 14 m depth. Although the water table in a well near the site was 8 m deep, cone penetration geotechnical data taken across the field do not reveal a distinct water table. Instead, cone penetration tests show a gradual change in electrical properties that we interpret as a thick zone of partial saturation. Comparing the seismic and radar data and using the geotechnical data as ground truth, we have associated the deepest coherent event in both reflection data sets with the sand‐clay aquitard boundary. Cone penetrometer data show the presence of a thin lens of clays and silts at about 4 m depth in the north part of the field. This shallow clay is not imaged clearly in the low‐frequency radar profiles. However, the seismic data do image the clay lens. Cone penetrometer data detail a clear change in the soil classification related to the underlying clay aquitard at the same position where the nonintrusive geophysical measurements show a change in image character. Corresponding features in the seismic and radar images are similar along profiles from common survey lines, and results of joint interpretation are consistent with information from geotechnical data across the site.

scholarly journals Object Detection in Ground-Penetrating Radar Images Using a Deep Convolutional Neural Network and Image Set Preparation by Migration

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Kazuya Ishitsuka ◽  
Shinichiro Iso ◽  
Kyosuke Onishi ◽  
Toshifumi Matsuoka
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ground Penetrating Radar ◽  
Deep Convolutional Neural Network ◽  
Training Data ◽  
Neural Network Approach ◽  
Radar Images ◽  
Image Set ◽  
Ground Penetrating ◽  
Better Than

Ground-penetrating radar allows the acquisition of many images for investigation of the pavement interior and shallow geological structures. Accordingly, an efficient methodology of detecting objects, such as pipes, reinforcing steel bars, and internal voids, in ground-penetrating radar images is an emerging technology. In this paper, we propose using a deep convolutional neural network to detect characteristic hyperbolic signatures from embedded objects. As a first step, we developed a migration-based method to collect many training data and created 53510 categorized images. We then examined the accuracy of the deep convolutional neural network in detecting the signatures. The accuracy of the classification was 0.945 (94.5%)–0.979 (97.9%) when using several thousands of training images and was much better than the accuracy of the conventional neural network approach. Our results demonstrate the effectiveness of the deep convolutional neural network in detecting characteristic events in ground-penetrating radar images.

Non-invasive estimation of root zone soil moisture from coarse root reflections in ground-penetrating radar images

2019 ◽  
Vol 436 (1-2) ◽  
pp. 623-639 ◽  
Author(s):  
Xinbo Liu ◽  
Xihong Cui ◽  
Li Guo ◽  
Jin Chen ◽  
Wentao Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Ground Penetrating Radar ◽  
Root Zone ◽  
Coarse Root ◽  
Radar Images ◽  
Non Invasive ◽  
Ground Penetrating

Semiautomated suppression of above-surface diffractions in GPR data

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. J43-J50 ◽  
Author(s):  
Stefan F. Carpentier ◽  
Heinrich Horstmeyer ◽  
Alan G. Green ◽  
Joseph Doetsch ◽  
Ilaria Coscia
Keyword(s):  
Ground Penetrating Radar ◽  
Synthetic Data ◽  
Complete Removal ◽  
Original Data ◽  
High Amplitude ◽  
Air Velocity ◽  
Efficient Suppression ◽  
Useful Signal ◽  
Multichannel Filtering ◽  
Ground Penetrating

Diffractions from above-surface objects can be a major problem in the processing and interpretation of ground-penetrating radar (GPR) data. Whereas methods to reduce random and many other types of source-generated noise are available, the efficient suppression of above-surface diffractions (ASDs) continues to be challenging. We have developed a scheme for semiautomatically detecting and suppressing ASDs. Initially, an accurate representation of ASDs is obtained by (1) Stolt [Formula: see text] migrating the GPR data using the air velocity to focus ASDs, (2) multichannel filtering to minimize other signals, (3) setting an amplitude threshold that targets the high-amplitude ASDs and effectively eliminates other signals, and (4) Stolt [Formula: see text] demigrating the ASDs using the air velocity, and remigrating them using the ground velocity. By excluding the obliquity correction in the Stolt algorithms and avoiding intermediate amplitude scaling, we preserve the ASDs’ amplitude and phase information. The final stepinvolves subtracting this image of ASDs from a standard migrated version of the original data. This scheme, which includes some important extensions to a previously proposed method, makes it possible to semiautomatically process large volumes of GPR data characterized by numerous highly clustered and overlapping ASDs. The user has control over the tradeoff between ASD suppression and undesired removal of useful signal. It achieves nearly complete removal of ASDs in synthetic data and significant suppression in field data. Once ASDs have been suppressed, their influence can be reduced further by applying relatively gentle multichannel filters. It is not possible to remove line diffractions that resemble subhorizontal reflections or retrieve subsurface signals from data saturated by ASDs, such that some blank regions may be left after applying the suppression scheme. Nevertheless, subsequent processing and interpretation of the GPR data benefit significantly from the suppression of ASDs, which otherwise would clutter the final images.

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