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\begin{document}

\title{Deep learning for data-driven geophysics}



\author[1]{Jianwei Ma}%
\author[2]{Siwei Yu}%
\affil[1]{Peking University}%
\affil[2]{Harbin Institute of Technology, China}%


\vspace{-1em}



  
  \date{June 18, 2024}


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\begin{abstract}
``Model-driven'' and ``data-driven'' approaches together have helped
humans understand the principles of geophysical phenomena for a long
time. With increasingly available geophysical data, a new data-driven
technique, i.e., deep learning, has played an important role in the
accurate prediction of complex system states and relieving the curse of
dimensionality in large temporal and spatial geophysical applications.
In this article, we review the basic concepts of and recent advances in
data-driven deep learning approaches in a variety of geophysical
scenarios. Explorational geophysics including data processing and
imaging, are the main focus. Deep learning applications in the
geosciences including the Earth interior, earthquakes, water resources,
atmospheric science, satellite remote sensing, and space sciences are
also reviewed. A coding tutorial and a summary of tips for rapidly
exploring deep learning are presented for beginners and interested
readers of geophysics. Several promising directions are provided for
future research involving deep learning in geophysics, such as
unsupervised learning, transfer learning, multimodal deep learning,
federated learning, uncertainty estimation, and active learning.%
\end{abstract}%



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