Open Access
| Issue |
E3S Web Conf.
Volume 700, 2026
Journées Scientifiques AGAP Qualité 2026
|
|
|---|---|---|
| Article Number | 02005 | |
| Number of page(s) | 10 | |
| Section | Geophysical Measurements Using Optical Fiber | |
| DOI | https://doi.org/10.1051/e3sconf/202670002005 | |
| Published online | 23 March 2026 | |
- G. Armand, F. Leveau, C. Nussbaum, R. de La Vaissière, A. Noiret, D. Jaeggi & C. Righini, Geometry and properties of the excavation-induced fractures at the Meuse/Haute-Marne URL drifts, Rock Mech. Rock Eng., 47, 21–41 (2014). [Google Scholar]
- R. de La Vaissière, J. Morel, A. Noiret, P. Côte, B. Helmlinger, R. Sohrabi & C. Nussbaum, Excavation-induced fractures network surrounding tunnel: properties and evolution under loading, Geol. Soc. Spec. Publ., 400, 279–291 (2014). [Google Scholar]
- A. H. Hartog, An introduction to distributed optical fibre sensors, CRC Press (2017) [Google Scholar]
- S. Hörning & B. Haese, RMWSPy (v 1.1): A Python code for spatial simulation and inversion for environmental applications, Environ. Model. Softw., 138, 104970 (2021) [Google Scholar]
- L. Räss, D. Kolyukhin & A. Minakov, Efficient parallel random field generator for large 3D geophysical problems, Comput. Geosci., 131, 158–169 (2019) [Google Scholar]
- A. Richardson, Deepwave (v0.0.20), Zenodo (2023) [Google Scholar]
- G. F. Margrave & M. P. Lamoureux, Numerical methods of exploration seismology: With algorithms in MATLAB®, Cambridge University Press (2019) [Google Scholar]
- J. Freire de Souza, J. B. D. Moreira, K. J. Roberts, R. d. R. A. Gaioso, E. S. Gomi, E. C. N. Silva & H. Senger, simwave – A Finite Difference Simulator for Acoustic Waves Propagation, arXiv, 2201.05278 (2022). [Google Scholar]
- F. Yang & J. Ma, Deep-learning inversion: A next-generation seismic velocity model building method, Geophysics, 84, R583–R599 (2019) [Google Scholar]
- S. Li, B. Liu, Y. Ren, Y. Chen, S. Yang, Y. Wang & P. Jiang, Deep-learning inversion of seismic data, arXiv, 1901.07733 (2019) [Google Scholar]
- O. Ronneberger, P. Fischer & T. Brox, U-net: Convolutional networks for biomedical image segmentation, Med. Image Comput. Comput.-Assist. Interv., 9351, 234–241 (2015) [Google Scholar]
- K. Sohn, H. Lee & X. Yan, Learning structured output representation using deep conditional generative models, Adv. Neural Inf. Process. Syst., 28 (2015). [Google Scholar]
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