Nonintrusive Depth Estimation of Buried Radioactive Wastes Using Ground Penetrating Radar and a Gamma Ray Detector

Ukaegbu, Ikechukwu K.; Gamage, Kelum A. A.; Aspinall, Michael

doi:10.3390/rs11020141

Cited by 11 publications

(14 citation statements)

References 42 publications

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“…The GPR technology is quite versatile and has been consolidated since the 1980s. Currently, geoscientists have applied the GPR method in a range of approaches, such as interference mapping [12][13][14][15][16][17][18][19][20][21][22][23], natural resources exploration [24][25][26][27], environmental contamination studies [28][29][30][31], archaeological studies [32][33][34][35][36][37][38], forensic studies [39][40][41], sedimentological studies [42][43][44][45], studies of rivers and lakes [46][47][48], and so forth.…”

Section: Gpr Methodsmentioning

confidence: 99%

GPR Survey on an Iron Mining Area after the Collapse of the Tailings Dam I at the Córrego do Feijão Mine in Brumadinho-MG, Brazil

2019

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This article shows the interesting results of a pioneer effort by IAG/USP researchers to use ground-penetrating radar (GPR) for humanitarian purposes, guiding the rescue of victims in the tragedy of Brumadinho. The tailings Dam I at the Córrego do Feijão iron ore mine, located in the Brumadinho complex, Minas Gerais State, Brazil, collapsed on 25 January 2019. About 11.7 million m3 of mining mud was spilled from the dam, burying bodies, equipment, structural buildings, buses, and cars along a length of 8.5 km up to the Paraopeba River. Additionally, the contaminated mud traveled more than 300 km along the bed of the Paraopeba River toward the São Francisco River. This work shows the results of a geophysical investigation using the GPR method 17 days after the event. To carry out the geophysical survey, an excavator was used for soil compaction. The data acquisition was performed on the tracks left by the excavator chain using SIR-4000 equipment and antennas of 200 and 270 MHz (GSSI). The GPR studies aimed to map bodies, structural buildings, and equipment buried in the mud. The location of the profiles followed preferably the edge of the slope due to the higher probability of finding buried bodies and objects. The GPR results allowed the detection of subsoil structures, such as concentrations of iron ore and accumulations of sand from the dam filter. The GPR was effective because the iron ore sludge in the mixing process became porous and the pores were filled with air, which provided penetration and reflection of the GPR electromagnetic waves up to a depth of 3.5 m. The results were surprising. Although no bodies or underground equipment were found, the results of this research served to eliminate the studied areas from future excavations, thus redirecting the rescue teams and optimizing the search process. These important results can serve as an additional motivation for the use of GPR in future humanitarian work in areas of tragedies.

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Section: Gpr Methodsmentioning

confidence: 99%

GPR Survey on an Iron Mining Area after the Collapse of the Tailings Dam I at the Córrego do Feijão Mine in Brumadinho-MG, Brazil

2019

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“…[S] = S HH S HV S V H S VV (1) where S XY represents the GPR data that is collected with an X type polarimetric receiving antenna and Y type polarimetric transmitting antenna, H represents horizontal polarimetric antenna, V represents vertical polarimetric antenna. According to the reciprocity theorem, S HV is equal to S VH .…”

Section: H-alpha Decompositionmentioning

confidence: 99%

“…Among the existing methods, geometrical features and polarimetric attributes are primarily used in classification. For example, Ikechukwu K. Ukaegbu et al combined GPR and a gamma ray detector to estimate the nonintrusive depth of buried radioactive wastes [1]; Wentao Li et al applied a randomized Hough transform to achieve the automatic recognition of tree roots [2]; Byeongjin Park et al used instantaneous phase depths, diameters, and materials; subsequently, classical H-Alpha classification and support vector machine (SVM) analysis are performed to compare with the PCSP method.…”

Section: Introductionmentioning

confidence: 99%

Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

et al. 2019

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The subsurface target classification of ground penetrating radar (GPR) is a popular topic in the field of geophysics. Among the existing classification methods, geometrical features and polarimetric attributes of targets are primarily used. As polarimetric attributes contain more information of targets, polarimetric decomposition methods, such as H-Alpha decomposition, have been developed for target classification of GPR in recent years. However, the classification template used in H-Alpha classification is preset depending on the experience of synthetic aperture radar (SAR); therefore, it may not be suitable for GPR. Moreover, many existing classification methods require excessive human operation, particularly when outliers exist in the sample (the data set containing the features of targets); therefore, they are not efficient or intelligent. We herein propose a new machine learning method based on sample centers, i.e., particle center supported plane (PCSP). The sample center is defined as the point with the smallest sum of distances from all points in the same sample, which is considered as a better representation of the sample without significant effect of the outliers. In this proposed method, particle swarm optimization (PSO) is performed to obtain the sample centers; the new criterion for subsurface target classification is achieved. We applied this algorithm to full polarimetric GPR data measured in the laboratory and outdoors. The results indicate that, comparing with support vector machine (SVM) and classical H-Alpha classification, this new method is more efficient and the accuracy is relatively high.

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“…The radiation intensities I ( x, y, z) at different positions on the surface (x, y) of a material volume due to a radioactive source buried at depth z is given by [3]:…”

Section: A Theoretical Frameworkmentioning

confidence: 99%

“…However, intrusive depth estimation methods are slow while nonintrusive methods such as [1]- [2] are either based on empirical models or are limited to specific radioisotopes. This study reports on the combination of a ground penetrating radar (GPR) and a gamma ray detector for nonintrusive estimation of the depth of a buried radioactive source [3]. Four permittivity mixing formulas were studied to estimate the material bulk density required to fit a gamma ray attenuation model to the data from the gamma ray detector.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Depth of Buried Radioactive Sources using Ground Penetrating Radar and a Gamma Ray Detector

Ukaegbu

Aspinall

Gamage

2019

2019 USNC-URSI Radio Science Meeting (Joint With AP-S Symposium)

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This study reports on nonintrusive depth estimation of a buried caesium-137 radioactive source using a ground penetrating radar (GPR) and a gamma ray detector. Specifically, the bulk density estimates from the GPR was used to fit a gamma ray attenuation model to the data from the detector. The depth of the source was successfully estimated when buried up to 18 cm in sand, gravel and soil. This will help in nonintrusive monitoring of nuclear contaminated sites.

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Nonintrusive Depth Estimation of Buried Radioactive Wastes Using Ground Penetrating Radar and a Gamma Ray Detector

Cited by 11 publications

References 42 publications

GPR Survey on an Iron Mining Area after the Collapse of the Tailings Dam I at the Córrego do Feijão Mine in Brumadinho-MG, Brazil

GPR Survey on an Iron Mining Area after the Collapse of the Tailings Dam I at the Córrego do Feijão Mine in Brumadinho-MG, Brazil

Particle Center Supported Plane for Subsurface Target Classification based on Full Polarimetric Ground Penetrating Radar

Estimating the Depth of Buried Radioactive Sources using Ground Penetrating Radar and a Gamma Ray Detector

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