Palavras-chave
Geophysics
Fully convolutional network
Como Citar
Resumo
Diffractions play a significant role in seismic processing and imaging since they can image structures smaller than the seismic wavelength, such as discontinuities, faults, and pinch-outs. The traveltime of a non-migrated stacked diffraction event typically has a hyperbolic shape around its apex, which collapses after a migration procedure. In this work, we introduce a Fully Convolutional Network (namely, LeNet-5 FCN) to automatic detect diffraction apexes on real seismic data. To deal with the low amount of annotated data, we propose to use data augmentation and ensemble strategies. By combining our LeNet-5 FCN with those strategies, we reached 91.2% average accuracy on three land seismic datasets.
Referências
Araújo, L. M.; Oliveira, F. M. C.; Faccipieri, J. H.; Coimbra, T. A.; Avila, S.; Tygel, M.; Borin, E. Detecção de estuturas em dados sísmicos com Deep Learning. Boletim Sociedade Brasileira de Geofísica. 2018. 18-21.
Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Conference on Computer Vision and Pattern Recognition. 2015. 3431-3440.
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