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physics.geo-ph2020
Fully reversible neural networks for large-scale 3D seismic horizon tracking
Bas Peters, Eldad Haber
Tracking a horizon in seismic images or 3D volumes is an integral part of seismic interpretation. The last few decades saw progress in using neural networks for this task, starting…
physics.geo-ph2020
Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing
Bas Peters, Eldad Haber, Keegan Lensink
The large spatial/frequency scale of hyperspectral and airborne magnetic and gravitational data causes memory issues when using convolutional neural networks for (sub-) surface cha…