  Applying deep-learning models to geophysical applications has attracted
special attentions during the past a couple of years. There are several papers
published in this domain involving with different topics primarily focusing on
synthetic data. Based on the presented results, reaching the satisfaction from
geophysicists is largely far away the performance of the conventional
algorithms. Moreover, it is extremely hard to find documents with detailed
illustrations about what are the best practices that researchers should follow
regarding how to design appropriate deep-learning models to effectively and
precisely tackle problems relevant to the field data. This paper serves as a
summarization to demonstrate successful stories and share with extensive
experiences we have gained during past several years in the process of
designing and deploying of deep-learning models to the geophysical projects in
a large scale. Four different disciplines are discussed individually with
seismic acquisition, processing, interpretation, and inversion. Finally,
special attentions about designing an effective deep-neural-networks especially
for geophysics are discussed.
