  Deep Learning is gaining traction with geophysics community to understand
subsurface structures, such as fault detection or salt body in seismic data.
This study describes using deep learning method for iceberg or ship recognition
with synthetic aperture radar (SAR) data. Drifting icebergs pose a potential
threat to activities offshore around the Arctic, including for both ship
navigation and oil rigs. Advancement of satellite imagery using
weather-independent cross-polarized radar has enabled us to monitor and
delineate icebergs and ships, however a human component is needed to classify
the images. Here we present Transfer Learning, a convolutional neural network
(CNN) designed to work with a limited training data and features, while
demonstrating its effectiveness in this problem. Key aspect of the approach is
data augmentation and stacking of multiple outputs, resulted in a significant
boost in accuracy (logarithmic score of 0.1463). This algorithm has been tested
through participation at the Statoil/C-Core Kaggle competition.

Machine Learning;Machine Learning