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EnglishWe implemented a Deep Learning algorithm to estimate the subsurface EM velocity field from common offset GPR profiles. The Deep Learning approach is based on a Bi-Directional Long Short-Term Memory (LSTM) Neural Network (NN) architecture trained on simple synthetic profiles randomly generated. The trained network is then applied to each A-Scan of 2D or even 3D GPR datasets. We trained the network on a synthetic dataset with different numbers of reflectors, wavelets, Signal-to-Noise ratios. The application of the network to synthetic and field data successfully predicts the velocity model and provides a computationally effective alternative to classic methods.
| Titolo: | Velocity analysis on common offset GPR data: A deep learning approach |
| Autori: | |
| Data di pubblicazione: | 2020 |
| Abstract: | We implemented a Deep Learning algorithm to estimate the subsurface EM velocity field from common offset GPR profiles. The Deep Learning approach is based on a Bi-Directional Long Short-Term Memory (LSTM) Neural Network (NN) architecture trained on simple synthetic profiles randomly generated. The trained network is then applied to each A-Scan of 2D or even 3D GPR datasets. We trained the network on a synthetic dataset with different numbers of reflectors, wavelets, Signal-to-Noise ratios. The application of the network to synthetic and field data successfully predicts the velocity model and provides a computationally effective alternative to classic methods. |
| Handle: | http://hdl.handle.net/11368/2980215 |
| URL: | https://library.seg.org/doi/10.1190/gpr2020-101.1 |
| Appare nelle tipologie: | 4.1 Contributo in Atti Convegno (Proceeding) |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| Roncoroni et al. 2020 - ICGPR.pdf | Articolo principale | Documento in Versione Editoriale | Copyright Editore | Administrator Richiedi una copia |
