  Almost all work to understand Earth's subsurface on a large scale relies on
the interpretation of seismic surveys by experts who segment the survey
(usually a cube) into layers; a process that is very time demanding. In this
paper, we present a new deep neural network architecture specially designed to
semantically segment seismic images with a minimal amount of training data. To
achieve this, we make use of a transposed residual unit that replaces the
traditional dilated convolution for the decode block. Also, instead of using a
predefined shape for up-scaling, our network learns all the steps to upscale
the features from the encoder. We train our neural network using the Penobscot
3D dataset; a real seismic dataset acquired offshore Nova Scotia, Canada. We
compare our approach with two well-known deep neural network topologies: Fully
Convolutional Network and U-Net. In our experiments, we show that our approach
can achieve more than 99 percent of the mean intersection over union (mIOU)
metric, outperforming the existing topologies. Moreover, our qualitative
results show that the obtained model can produce masks very close to human
interpretation with very little discontinuity.

Image and Video Processing;Machine Learning