Automatic salt deposits segmentation: A deep learning approach
This work addresses a domain-specific problem in geophysics for oil and gas exploration, but it is incremental as it combines existing methods without introducing a new paradigm.
The paper tackled the problem of segmenting salt deposits in seismic images for hydrocarbon exploration by merging several deep learning techniques into a single neural network, achieving 27th place (top 1%) in a Kaggle competition.
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.