Research on Development 3D Ground Penetrating Radar Acquisition and Control Technology for Road Underground Diseases with Dual-Band Antenna Arrays

Fang, Liang; Yang, Feng; Xu, Maoxuan; Liu, Fengyu

doi:10.3390/s23198301

Cited by 2 publications

(2 citation statements)

References 27 publications

(30 reference statements)

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“…The comparison between antennas in GPR studies highlights a trade-off between resolution and penetration depth. The high-frequency antenna offers superior resolution, capable of detecting features as small as 2 cm, but its penetration depth is limited to 0.4 m. However, the low-frequency antenna provides a lower resolution but can penetrate up to 4.5 m, making it more suitable for deeper subsurface investigations [56,58,[82][83][84][85][86]. The choice of antenna is closely related to the resolution of a specific conservation problem as accurately as possible: the washing of paint, which is directly linked to water accumulated in the first centimeters from the basal surface of the Ceiling.…”

Section: Gpr Of Alt1 Control Areamentioning

confidence: 99%

“…Additionally, addressing deeper accumulations indirectly requires an antenna with greater penetration. Ultimately, the objective is to respond to a conservation need, balancing the requirement for resolution against the need for depth penetration to ensure optimal results in subsurface imaging and detection applications [56,58,[82][83][84][85][86]. The digital processing of time-domain GPR data generally relies on the amplitude values of the signal for interpretation.…”

Section: Gpr Of Alt1 Control Areamentioning

confidence: 99%

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Remote Sensing and Environmental Monitoring Analysis of Pigment Migrations in Cave of Altamira’s Prehistoric Paintings

Bayarri,

Prada,

García

et al. 2024

Remote Sensing

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The conservation of Cultural Heritage in cave environments, especially those hosting cave art, requires comprehensive conservation strategies to mitigate degradation risks derived from climatic influences and human activities. This study, focused on the Polychrome Hall of the Cave of Altamira, highlights the importance of integrating remote sensing methodologies to carry out effective conservation actions. By coupling a georeferenced Ground Penetrating Radar (GPR) with a 1.6 GHz central-frequency antenna along with photogrammetry, we conducted non-invasive and high-resolution 3D studies to map preferential moisture pathways from the surface of the ceiling to the first 50 cm internally of the limestone structure. In parallel, we monitored the dynamics of surface water on the Ceiling and its correlation with pigment and other substance migrations. By standardizing our methodology, we aim to increase knowledge about the dynamics of infiltration water, which will enhance our understanding of the deterioration processes affecting cave paintings related to infiltration water. This will enable us to improve conservation strategies, suggesting possible indirect measures to reverse active deterioration processes. Integrating remote sensing techniques with geospatial analysis will aid in the validation and calibration of collected data, allowing for stronger interpretations of subsurface structures and conditions. All of this puts us in a position to contribute to the development of effective conservation methodologies, reduce alteration risks, and promote sustainable development practices, thus emphasizing the importance of remote sensing in safeguarding Cultural Heritage.

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Section: Gpr Of Alt1 Control Areamentioning

confidence: 99%

Section: Gpr Of Alt1 Control Areamentioning

confidence: 99%

Remote Sensing and Environmental Monitoring Analysis of Pigment Migrations in Cave of Altamira’s Prehistoric Paintings

Bayarri,

Prada,

García

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

Development of a Background Filtering Algorithm to Improve the Accuracy of Determining Underground Cavities Using Multi-Channel Ground-Penetrating Radar and Deep Learning

Park,

Kim,

Lee

et al. 2024

Remote Sensing

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In the process of using multi-channel ground-penetrating radar (GPR) for underground cavity exploration, the acquired 3D data include reflection data from underground cavities or various underground objects (structures). Reflection data from unspecified structures can interfere with the identification process of underground cavities. This study aims to identify underground cavities using a C-GAN model with an applied ResBlock technique. This deep learning model demonstrates excellent performance in the image domain and can automatically classify the presence of cavities by analyzing 3D GPR data, including reflection waveforms (A-scan), cross-sectional views (B-scan), and plan views (C-scan) measured from the ground under roads. To maximize the performance of the C-GAN model, a background filtering algorithm (BFA) was developed and applied to enhance the visibility and clarity of underground cavities. To verify the performance of the developed BFA, 3D data collected from roads in Seoul, Republic of Korea, using 3D GPR equipment were transformed, and the C-GAN model was applied. As a result, it was confirmed that the recall, an indicator of cavity prediction, improved by approximately 1.15 times compared to when the BFA was not applied. This signifies the verification of the effectiveness of the BFA. This study developed a special algorithm to distinguish underground cavities. This means that in the future, not only the advancement of separate equipment and systems but also the development of specific algorithms can contribute to the cavity exploration process.

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Research on Development 3D Ground Penetrating Radar Acquisition and Control Technology for Road Underground Diseases with Dual-Band Antenna Arrays

Cited by 2 publications

References 27 publications

Remote Sensing and Environmental Monitoring Analysis of Pigment Migrations in Cave of Altamira’s Prehistoric Paintings

Remote Sensing and Environmental Monitoring Analysis of Pigment Migrations in Cave of Altamira’s Prehistoric Paintings

Development of a Background Filtering Algorithm to Improve the Accuracy of Determining Underground Cavities Using Multi-Channel Ground-Penetrating Radar and Deep Learning

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