Jiajia Sun is currently Assistant Professor of Geophysics in the Department of Earth and Atmospheric Sciences at University of Houston. He obtained his B.Sc. in Geophysics from China University of Geosciences (Wuhan) in 2008 and his Ph.D. in Geophysics from the Colorado School of Mines in 2015. His current research focuses on (1) tackling magnetic remanence problems by integrating unsupervised machine learning techniques into inversion of magnetic data, and (2) developing joint inversion methods for multiple geophysical data sets. He is Active Member of SEG, member of AGU and EAGE, and serves in the SEG Gravity and Magnetics Committee. He received honorable mention for Best Paper in GEOPHYSICS in 2015, and Best Paper in the Mining sessions at the 2016 SEG Annual Meeting.
- Electromagnetic Methods for Exploration (2018 Fall at University of Houston)
- Data Analytics and Machine Learning for Geoscientists (2018 Spring, 2019 Spring at University of Houston)
- Geophysical Field Camp (2018 Summer at University of Houston)
- Inversion Theory (2016 Spring, 2017 Spring at Colorado School of Mines)
- Joint inversion of multi-physics geoscience data for better characterization of subsurface structures
- Magnetization clustering inversion for interpreting magnetic data complicated by remanence and for geology differentiation
- Machine learning applied to geophysical data processing, interpretation and imaging
- Sparse signal processing applied to geophysical data processing, modeling and modeling and inversion
- ,Li, Y., and J. Sun, 2016, 3D magnetization inversion using fuzzy c-means clustering with application to geology differentiation: Geophysics, 81(5), J61-J78.
- ,Sun, J., and Y. Li, 2018, Magnetization clustering inversion Part I: Building an automated numerical optimization algorithm: Geophysics, 83(5), J61-J73.
- ,Sun, J., and Y. Li, 2016, Joint inversion of multiple geophysical data using guided fuzzy c-means clustering: Geophysics, 81(3), ID37-ID57.
- ,Sun, J., and Y. Li, 2017, Joint inversion of multiple geophysical and petrophysical data using generalized fuzzy clustering algorithms: Geophys. J. Int., 208(2), 1201-1216.
- ,Li, Y., A. Melo, C. Martinez, and J. Sun, 2019, Geology differentiation: A new frontier in quantitative geophysical interpretation in mineral exploration: The Leading Edge, 38(1), pp. 60-66.