Development of Software for the Interpretation of Radar Images using Deep Learning Methods
Iskakov K. Tatin A.
2025Budapest Tech Polytechnical Institution
Acta Polytechnica Hungarica
2025#22Issue 9185 - 204 pp.
This study introduces a practical method for diagnosing subsurface structures which focuses on roads and uses ground-penetrating radar (GPR) and deep learning. The study proposes an implementation to interpret radargrams captured by the OKO-2 system, which records signals reflected from underground layers. Two interpretation methods are used, i.e., one based on physics formulas, the other on mathematical modeling. Before applying neural networks, the data undergo analytical processing using the GeoScan32 software, which filters noise, enhances signals, and identifies layer boundaries. This analytical foundation is crucial for effectively training deep learning models to interpret complex subsurface data.
Fourier transform , geoelectric section , Geophysics , ground penetrating radar , Hilbert transform , ill-posed and inverse problems , software
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L. N. Gumilyov Eurasian National University, 11 Pushkin street, Astana, 010005, Kazakhstan
L. N. Gumilyov Eurasian National University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026