  Understanding Earth's subsurface structures has been and continues to be an
essential component of various applications such as environmental monitoring,
carbon sequestration, and oil and gas exploration. By viewing the seismic
volumes that are generated through the processing of recorded seismic traces,
researchers were able to learn from applying advanced image processing and
computer vision algorithms to effectively analyze and understand Earth's
subsurface structures. In this paper, first, we summarize the recent advances
in this direction that relied heavily on the fields of image processing and
computer vision. Second, we discuss the challenges in seismic interpretation
and provide insights and some directions to address such challenges using
emerging machine learning algorithms.
