Automatic detection of diffraction-apex using fully convolutional networks
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, wh...
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| Main Authors: | , , , , , |
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| Format: | Online |
| Language: | Portuguese |
| Published: |
Universidade Estadual de Campinas
2019
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| Subjects: | |
| Online Access: | https://econtents.sbu.unicamp.br/eventos/index.php/pibic/article/view/2260 |
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| Summary: | 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. |
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| ISSN: | 2596-1969 |
