About Xinming Wu (伍新明)

Biography

Xinming Wu received a B.S. in Geophysics from Central South University in 2009, a M.Sc. in Geophysics from Tongji University in 2012, and a Ph.D. in Geophysics from Colorado School of Mines in 2016. At Mines, he worked with Dr. Dave Hale at the Center for Wave Phenomena.

Before joining USTC as a professor, he worked as an intern for Drillinginfo in summer and winter of 2014, visiting scholar (2015) at UT Austin, senior geophysical advisor (2017-2018) for Energective, Houston, and postdoc (2016-2019) with Dr. Sergey Fomel at the Bureau of Economic Geology, UT Austin.

Xinming contributes to the geophysics community by serving as

  • Council Member (理事会成员, 全球第11区代表), SEG/IMAGE (2023-2025),
  • Research Committee, SEG/IMAGE (2023-present),
  • AE, Computers&Geosciences (2022-present),
  • AE, Geophysics (2020-present),
  • AE, Interpretation (2017-present),
  • AE, IEEE GRSL (2021-2022),
  • AE, Journal of Applied Geophysics (2017-2020),
  • Member, SEG Distinguished Lecture Committee (2020-present),
  • Session Chair, SEG Annual Meeting (2016-present),
  • 副秘书长, 中国地球物理学会人工智能地球物理专业委员会 (2022-present),
  • 副主任, 中国地震学会地震人工智能专业委员会 (2022-present),
  • 委员, 中国地球物理学会油气地球物理专业委员会 (2022-present),
  • 委员, 中国地球物理学会工程地球物理专业委员会 (2021-present),
  • Reviewer, multiple journals.

    • Xinming received the following honors

  • J. Clarence Karcher Award, SEG, 2020
  • Honorary Lecturer, SEG, 2020
  • Best Paper, Honorable Mention, 2018 SEG Annual Convention (Least-squares horizons)
  • Best Poster Paper, 2017 SEG Annual Convention (Missing log interpolation)
  • Best Paper, Geophysics, 2016 (3D seismic image processing for faults)
  • 高层次科技创新人才工程—青年科技人才，国家自然资源部，2023
  • 海外校友基金会青年教师事业奖，中国科大，2023
  • 入选“全球前2%顶尖科学家榜单”，斯坦福大学、Scopus数据库，2023
  • 入选“高被引学者年度榜单”，ELSEVIER，2022
  • 中国地球物理科学技术进步奖二等奖，个人排名第二，中国地球物理学会，2022
  • 技术发明奖二等奖，个人排名第四，中国石油和化工自动化应用协会，2022
  • AI名师奖, 华为, 2021
  • 上海市优秀硕士论文, 上海市教育局, 2013

    • Xinming writes a lot of Java and Python packages for his research on seismic structural and stratigraphic interpretation, deep learning, subsurface modeling, joint seismic and well-log interpretation, and geophysical inversion with geologic constraints.