| Yazarlar (5) |
Dr. Öğr. Üyesi Reyhan YURT
Kırşehir Ahi Evran Üniversitesi, Türkiye |
Hamid Torpi
Yıldız Teknik Üniversitesi, Türkiye |
|
Ahmet Kızılay
Yıldız Teknik Üniversitesi, Türkiye |
|
Slawomir Koziel
|
|
Peyman Mahouti
Yıldız Teknik Üniversitesi, Türkiye |
| Özet |
| In this study, in order to characterize the buried object via deep-learning-based surrogate modeling approach, 3-D full-wave electromagnetic simulations of a GPR model have been used. The task is to independently predict characteristic parameters of a buried object of diverse radii allocated at different positions (depth and lateral position) in various dispersive subsurface media. This study has analyzed variable data structures (raw B-scans, extracted features, consecutive A-scans) with respect to computational cost and accuracy of surrogates. The usage of raw B-scan data and the applications for processing steps on B-scan profiles in the context of object characterization incur high computational cost so it can be a challenging issue. The proposed surrogate model referred to as the deep regression network (DRN) is utilized for time frequency spectrogram (TFS) of consecutive A-scans. DRN is developed with the main aim being computationally efficient (about 13 times acceleration) compared to conventional network models using B-scan images (2D data). DRN with TFS is favorably benchmarked to the state-of-the-art regression techniques. The experimental results obtained for the proposed model and second-best model, CNN-1D show mean absolute and relative error rates of 3.6 mm, 11.8 mm and 4.7%, 11.6% respectively. For the sake of supplementary verification under realistic scenarios, it is also applied for scenarios involving noisy data. Furthermore, the proposed surrogate modeling approach is validated using measurement data, which is indicative of suitability of the approach to handle physical measurements as data sources. |
| Anahtar Kelimeler |
| Artificial intelligence | Buried object characterization | Deep regression network | Ground penetrating radar (GPR) | Surrogate modeling | Time frequency spectrogram |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | SCIENTIFIC REPORTS |
| Dergi ISSN | 2045-2322 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 06-2024 |
| Cilt No | 14 |
| Sayı | 1 |
| Doi Numarası | 10.1038/s41598-024-65996-0 |
| Makale Linki | http://dx.doi.org/10.1038/s41598-024-65996-0 |
| Atıf Sayıları |
