In this episode, host Andrew Geary and Xinming Wu discuss the present and future on deep learning for seismic interpretation. Xinming and Andrew discuss how deep learning can improve training data sets, the importance of open software packages, the value of understanding seismic interpretation across the workflow, and what would happen if this topic reached its full potential.

Visit https://seg.org/podcast/Post/8847 for the complete show notes.

BIOGRAPHY

Xinming Wu serves as a professor at the University of Science and Technology of China, where he started the Computational Interpretation Group.

Xinming received an engineering degree (2009) in geophysics from Central South University, an M.Sc. (2012) in geophysics from Tongji University, and a Ph.D. (2016) in geophysics from the Colorado School of Mines where he was a member working with Dave Hale at the Center for Wave Phenomena.

He received SEG awards for Best Paper in GEOPHYSICS with Dave Hale in 2016, Best Student Poster Paper with Sean Bader and Sergey Fomel in 2017, and an Honorable Mention for Best Paper presented at the 2018 SEG Annual Meeting with Sergey Fomel. He also received the Shanghai excellent master thesis award in 2013.

Xinming writes a lot of software packages for his research on seismic structural and stratigraphic interpretation, deep learning (e.g., FaultSeg), subsurface modeling, joint seismic and well-log interpretation, and geophysical inversion with geologic constraints.

CREDITS

Original music by Zach Bridges.

This episode was hosted, edited, and produced by Andrew Geary. Thank you to the SEG podcast team: Jennifer Crockett, Ally McGinnis, and Mick Swiney.

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