scholarly journals Ground-penetrating radar land mine imaging: Two-dimensional seismic migration and three-dimensional inverse scattering in layered media

Radio Science ◽  
2005 ◽  
Vol 40 (1) ◽  
pp. n/a-n/a ◽  
Author(s):  
Lin-Ping Song ◽  
Qing Huo Liu
Keyword(s):  
Inverse Scattering ◽  
Ground Penetrating Radar ◽  
Three Dimensional ◽  
Layered Media ◽  
Two Dimensional ◽  
Seismic Migration ◽  
Land Mine ◽  
Ground Penetrating

Three-dimensional convolutional neural network–based underground object classification using three-dimensional ground penetrating radar data

2020 ◽  
Vol 19 (6) ◽  
pp. 1884-1893
Author(s):  
Shekhroz Khudoyarov ◽  
Namgyu Kim ◽  
Jong-Jae Lee
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Ground Penetrating Radar ◽  
Three Dimensional ◽  
Object Classification ◽  
Radar Data ◽  
Two Dimensional ◽  
Underground Cavities ◽  
Ground Penetrating

Ground-penetrating radar is a typical sensor system for analyzing underground facilities such as pipelines and rebars. The technique also can be used to detect an underground cavity, which is a potential sign of urban sinkholes. Multichannel ground-penetrating radar devices are widely used to detect underground cavities thanks to the capacity of informative three-dimensional data. Nevertheless, the three-dimensional ground-penetrating radar data interpretation is unclear and complicated when recognizing underground cavities because similar ground-penetrating radar data reflected from different underground objects are often mixed with the cavities. As it is prevalently known that the deep learning algorithm-based techniques are powerful at image classification, deep learning-based techniques for underground object detection techniques using two-dimensional GPR (ground-penetrating radar) radargrams have been researched upon in recent years. However, spatial information of underground objects can be characterized better in three-dimensional ground-penetrating radar voxel data than in two-dimensional ground-penetrating radar images. Therefore, in this study, a novel underground object classification technique is proposed by applying deep three-dimensional convolutional neural network on three-dimensional ground-penetrating radar data. First, a deep convolutional neural network architecture was developed using three-dimensional convolutional networks for recognizing spatial underground objects such as, pipe, cavity, manhole, and subsoil. The framework of applying the three-dimensional convolutional neural network into three-dimensional ground-penetrating radar data was then proposed and experimentally validated using real three-dimensional ground-penetrating radar data. In order to do that, three-dimensional ground-penetrating radar block data were used to train the developed three-dimensional convolutional neural network and to classify unclassified three-dimensional ground-penetrating radar data collected from urban roads in Seoul, South Korea. The validation results revealed that four underground objects (pipe, cavity, manhole, and subsoil) are successfully classified, and the average classification accuracy was 97%. In addition, a false alarm was rarely indicated.

Three‐Dimensional Ground‐Penetrating Radar Imaging Through Multilayered Subsurface

Radio Science ◽  
2019 ◽  
Vol 54 (8) ◽  
pp. 728-737 ◽  
Author(s):  
Wenji Zhang ◽  
Ahmad Hoorfar ◽  
Qiang An
Keyword(s):  
Ground Penetrating Radar ◽  
Three Dimensional ◽  
Radar Imaging ◽  
Ground Penetrating

Reconstructing the palaeoenvironment at the early Mesolithic site of Lake Duvensee: Ground-penetrating radar and geoarchaeology for 3D facies mapping

The Holocene ◽  
2020 ◽  
Vol 30 (6) ◽  
pp. 820-833 ◽  
Author(s):  
Erica Corradini ◽  
Dennis Wilken ◽  
Marco Zanon ◽  
Daniel Groß ◽  
Harald Lübke ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Three Dimensional ◽  
Sedimentary Facies ◽  
Landscape Development ◽  
Fine Grained ◽  
Stratigraphic Model ◽  
Lake Bottom ◽  
Organic Sediments ◽  
Spatio Temporal ◽  
Ground Penetrating

We investigate the landscape development of the early Mesolithic hunter-gatherer sites of Duvensee (10000–6500 cal. BCE). Based on ground-penetrating radar (GPR) and geoarchaeological drillings, we present for the first time a three-dimensional (3D) reconstruction of the palaeoenvironment of 63 ha covering subarea of the former lake during the Mesolithic. The archaeological aims were (1) to detect the location of former islands possibly hosting hunter-gatherer settlements and (2) to reconstruct the ancient landscape development for understanding prehistoric land use. The research in Duvensee lasts almost 100 years, providing vivid illustrations of early Mesolithic life. Clusters of Mesolithic camps have been found located on small sand hills that formed islands in the prehistoric lake. For this environment, we present depth maps of the three most important sedimentary facies interfaces of the ancient Lake Duvensee. Interface1 represents the transition between coarse organic sediments (peat and coarse detritus gyttja) and fine-grained organic sediments (fine detritus gyttja, calcareous gyttja), Interface2 represents the transition to the underlying clayish-loamy sediments, and Interface3 marks the top of the basal sand deposits at the lake bottom. From Interface3, we identified the location and extent of five former islands with Mesolithic camps. Stratigraphic information from the corings enabled us to create a 3D model of the spatio-temporal development of the Duvensee bog. The locations of the islands and their estimated dive-up times agree with the spatio-temporal pattern of the previous archaeological finds. The model shows where hunter-gatherers could settle and move from one island to another following the shorelines of the overgrowing lake. The 3D stratigraphic model provides growth and shrinking rates of the island and lake areas in the Mesolithic, and volumes of organic and non-organic deposited lake sediments. Besides, it provides a basis for a sustainable groundwater management needed for heritage preservation.

scholarly journals Formation of band ogives and associated structures at Bas Glacier d’Arolla, Valais, Switzerland

2002 ◽  
Vol 48 (161) ◽  
pp. 287-300 ◽  
Author(s):  
Becky Goodsell ◽  
Michael J. Hambrey ◽  
Neil F. Glasser
Keyword(s):  
Ground Penetrating Radar ◽  
Three Dimensional ◽  
Shear Zones ◽  
Glacier Surface ◽  
Glacier Tongue ◽  
Basal Ice ◽  
Planar Structures ◽  
Geophysical Techniques ◽  
Reverse Faulting ◽  
Ground Penetrating

AbstractStructural glaciological, sedimentological and geophysical techniques are used to provide new insight concerning the formation of band ogives and associated structures at Bas Glacier d’Arolla, Switzerland. Sedimentary stratification, crevasse traces and transverse foliation are identified as planar structures in the lower icefall and glacier tongue. Stratification and crevasse traces are progressively deformed into, and enhance, the transverse foliation found in the glacier tongue. Three-dimensional geometry has been defined using ground-penetrating radar, which portrays four main characteristics: (i) deep reflectors interpreted as the ice/bed interface, (ii) alternating reflection-rich and reflection-poor zones interpreted as ogives, (iii) up-glacier-dipping reflectors, interpreted as planar structures, and (iv) down-glacier-dipping reflectors of uncertain origin. At the glacier surface, each band ogive consists of a light and dark band. The dark bands contain more intense foliation which, on differential weathering, traps fine debris. Clasts and clear ice of basal character within dark ogive bands suggest that basal ice has been raised to the glacier surface. The most applicable model for the formation of band ogives at Bas Glacier d’Arolla is a refinement of Posamentier’s (1978) “reverse faulting” hypothesis. In this context, multiple shear zones are formed, through which basal ice is uplifted to the glacier surface to produce the dark, foliated ogive bands. This model fits observations reported from other glaciers with band ogives.

Sign in / Sign up

Export Citation Format

Share Document