scholarly journals Palaeotopography of a Palaeolithic landscape at Bestwood 1, South Africa, from ground-penetrating radar and magnetometry

2019 ◽  
Vol 115 (1/2) ◽  
Author(s):  
Konstantinos S. Papadimitrios ◽  
Carl-Georg Bank ◽  
Steven J. Walker ◽  
Michael Chazan
Keyword(s):  
Ground Penetrating Radar ◽  
Geophysical Methods ◽  
Small Stream ◽  
Radar Images ◽  
Buried Channel ◽  
Overburden Thickness ◽  
Northern Cape ◽  
Ground Penetrating ◽  
The Town ◽  
Archaeological Landscape

In order to investigate the buried landscape at the Fauresmith locality of Bestwood 1, outside the town of Kathu in the Northern Cape Province, we performed ground-penetrating radar and magnetometry surveys across the sand-filled central portion of the valley. The radar images a strong continuous reflector which we can assign to the boundary between the Kalahari sands and underlying Banded Ironstone Formation gravels. Moreover, the thickness of the sand delineates a buried depression in the centre of the valley with flat plateaus at the sides. Subtracting the sand thickness from the current topography produces a map of a small stream channel in the northern part of the valley. Analysis of the magnetic gradient data allows us to extend this buried channel further to the south. Our geophysical survey provides a valuable contribution towards understanding the context of hominin occupation along the banks of a small stream in the Kathu Complex. Significance: • We provide an example of combining two geophysical methods to map overburden thickness, useful for archaeological landscape interpretation.

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 Ground penetrating radar detection of subsnow slush on ice-covered lakes in interior Alaska

2012 ◽  
Vol 6 (6) ◽  
pp. 1435-1443 ◽  
Author(s):  
A. Gusmeroli ◽  
G. Grosse
Keyword(s):  
Ground Penetrating Radar ◽  
Geophysical Methods ◽  
Arctic Region ◽  
Water Harvesting ◽  
Lake Ice ◽  
Wet Snow ◽  
Dry Conditions ◽  
Ground Penetrating ◽  
Ice Water

Abstract. Lakes are abundant throughout the pan-Arctic region. For many of these lakes ice cover lasts for up to two thirds of the year. The frozen cover allows human access to these lakes, which are therefore used for many subsistence and recreational activities, including water harvesting, fishing, and skiing. Safe traveling condition onto lakes may be compromised, however, when, after significant snowfall, the weight of the snow acts on the ice and causes liquid water to spill through weak spots and overflow at the snow-ice interface. Since visual detection of subsnow slush is almost impossible our understanding on overflow processes is still very limited and geophysical methods that allow water and slush detection are desirable. In this study we demonstrate that a commercially available, lightweight 1 GHz, ground penetrating radar system can detect and map extent and intensity of overflow. The strength of radar reflections from wet snow-ice interfaces are at least twice as much in strength than returns from dry snow-ice interface. The presence of overflow also affects the quality of radar returns from the base of the lake ice. During dry conditions we were able to profile ice thickness of up to 1 m, conversely, we did not retrieve any ice-water returns in areas affected by overflow.

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.

Effectiveness of Geophysical Methods in Calcareous Harbor Fills

2013 ◽  
Author(s):  
Horst G. Brandes
Keyword(s):  
Ground Penetrating Radar ◽  
Seismic Refraction ◽  
Geophysical Methods ◽  
Fill Material ◽  
Object Locations ◽  
Ground Penetrating

The effectiveness of electromagnetic (EM), ground penetrating radar (GPR) and seismic refraction (SR) were evaluated by surveying a shallow trench in which a number of objects of varying composition and size were buried. The trench was excavated in granular calcareous fill material. An experienced geophysical contractor was asked to provide blind predictions of object locations using each of the techniques in turn. GPR with a 400 MHz antenna was the most successful, followed by SR and EM surveying. GPR and SR were also carried out at the port of Hilo to investigate complex subsurface conditions.

Identification, investigation and classification of surface depressions and chalk dissolution features using integrated LiDAR and geophysical methods

2020 ◽  
Vol 53 (4) ◽  
pp. 620-644 ◽  
Author(s):  
Zoe Elizabeth Jeffery ◽  
Stephen Penn ◽  
David Peter Giles ◽  
Linley Hastewell
Keyword(s):  
Ground Penetrating Radar ◽  
Image Interpretation ◽  
Geophysical Methods ◽  
Integrated Approach ◽  
Study Data ◽  
Aerial Photographs ◽  
Geophysical Techniques ◽  
Karstic Features ◽  
Ground Penetrating

The chalk bedrock of the Hampshire Basin, southern England is an important aquifer and is highly susceptible to dissolution, making the development and presence of karstic features a widespread occurrence. These features are hazardous because they provide possible pathways to the underlying aquifer and therefore present potential site-specific contamination risks. There is also evidence of extensive extraction, through both mining and surface quarrying, of chalk, flint and clay over many centuries. Geophysical techniques consisting of electromagnetic (EM31) and ground-penetrating radar surveys were used to identify and characterize target features identified from desk study data. The ground-penetrating radar and EM31 interpretations allowed the classification of non-anthropogenic target features, such as diffuse buried sinkholes with disturbed and subsiding clay-rich infill and varying symmetrical and asymmetrical morphologies. We describe here the investigations of such features identified at Holme Farm, Stansted House, Hampshire. The combination of EM31 data and ground-penetrating radar profiles facilitated the identification of a palaeovalley, cavities and irregular rockhead. This investigation identified locations of aquifer contamination risk as some sinkholes have been sites for the illegal dumping of waste or the infiltration of fertilizers, leaking sewage pipes or animal waste. This potential source of contamination utilizes the sinkhole as a pathway into the highly transmissive White Chalk Subgroup of Hampshire and has caused contamination of the aquifer. We conclude that our integrated approach of geophysical techniques linked to aerial photographs and LiDAR image interpretation was highly effective in the location and characterization of dissolution structures, infilled former quarries and mining features at this site.

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