  One of the most important applications of seismic reflection is the
hydrocarbon exploration which is closely related to salt deposits analysis.
This problem is very important even nowadays due to it's non-linear nature.
Taking into account the recent developments in deep learning networks TGS-NOPEC
Geophysical Company hosted the Kaggle competition for salt deposits
segmentation problem in seismic image data. In this paper, we demonstrate the
great performance of several novel deep learning techniques merged into a
single neural network which achieved the 27th place (top 1%) in the mentioned
competition. Using a U-Net with ResNeXt-50 encoder pre-trained on ImageNet as
our base architecture, we implemented Spatial-Channel Squeeze & Excitation,
Lovasz loss, CoordConv and Hypercolumn methods. The source code for our
solution is made publicly available at
https://github.com/K-Mike/Automatic-salt-deposits-segmentation.
