Automatic detection of diffraction-apex using fully convolutional networks

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.