Jiajia Sun

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Jiajia Sun
Jiajia Sun
Latest company University of Houston
Membership SEG, AGU, EAGE
BSc Geophysics
PhD Geophysics
BSc university China University of Geosciences (Wuhan)
PhD university Colorado School of Mines

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[1],[2] and (2) developing joint inversion methods for multiple geophysical data sets[3][4][5]. 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.

SEG J. Clarence Karcher Award 2021

Jiajia Sun is an assistant professor at the University of Houston and a recognized expert in joint inversion of multiple geophysical data sets. He developed a new inversion methodology that combined the classical regularized inversion formalism with fuzzy clustering allowing a priori statistical petrophysical data to be incorporated into geophysical inversions. A paper that Sun coauthored on this topic received Honorable Mention Best Paper in Geophysics in 2015. Sun’s joint-inversion method has been called powerful and innovative, and it also resulted in the ability to invert for the full magnetization vector rather than the susceptibility alone. His work in unsupervised machine learning in geophysical inversion began in 2011, well out in front of the curve and others in this area of research, where even today Sun’s work combining unsupervised machine learning with 3D geophysical inversion is at the very cutting edge of technology. His open collaboration style is nowhere more evident than in his open-source textbook with data examples — clearly a geoscientist of the future. SEG is Sun’s home society and that is evident in his volunteer work. He spends significant time as a heavily relied upon reviewer, in supporting the Mining Committee, and with his consistent service as cochair for technical sessions and workshops. Sun is currently serving as associate editor for joint inversion and multiphysics in Geophysics. Sun has a solid publication record with 12 peer reviewed journal articles (six in Geophysics) and an H of 12.

Biography Citation for the J. Clarence Karcher Award

by Yaoguo Li

I met Jiajia Sun when he was an undergraduate student studying geophysics in China. At the time, Jiajia cofounded and ran a student publication, Sparkle, which introduced the advances in geophysics to his peers and promoted English learning among college students. Not contending with any single data set interpretation, he spoke with conviction and passion that he would like to integrate multiple data sets to image the subsurface. That direction became Jiajia’s PhD research at the Colorado School of Mines.

Jiajia’s PhD research focused on the joint inversion of multiple geophysical data sets, which is an important direction for geophysical integration with broad applications to resource exploration, reservoir monitoring, and carbon storage. Jiajia combined the classical regularized inversion formalism with fuzzy clustering and developed a new inversion methodology that enables a priori statistical petrophysical data to be incorporated into geophysical inversions. He investigated extensively the applicability of this new method to the inversion of various data sets such as seismic traveltimes, gravity, magnetic, and induced polarization data. The work has paved the way for maximizing the value of petrophysical data in building reliable subsurface models. Jiajia also creatively combined such unsupervised machine learning with magnetic data inversion and developed methods to construct 3D magnetization models in the presence of remanent magnetization. This method has tremendous potential for interpreting magnetic data in exploration, solid earth, and planetary studies.

Jiajia is also a leading researcher on the subject of geology differentiation, which integrates multiple geophysical models and petrophysical data to predict distinct geologic units. This emerging methodology helps maximize the value of geophysical data by advancing the geophysical inversions into the realm of geologic model construction. It represents one critical step toward geoscientists’ ultimate goal of characterizing the subsurface geology. Jiajia has made significant contributions to the development of geology differentiation, especially in the context of mineral exploration.

Since joining the University of Houston as an assistant professor, Jiajia has established a strong research group with a focus on 3D probabilistic geology differentiation, uncertainty quantification of potential-field data inversion, and machine learning. His work on predicting magnetization directions using convolutional neural networks has attracted significant attention in solid earth geophysics. He is contributing fully to the education of the next generation of geophysicists by teaching several major courses, instructing in the geophysical field camp, and serving as the faculty advisor of an SEG Student Chapter.

As an early-career geophysicist, Jiajia has expended an amazingly large amount of time and effort for SEG and our community. He has reviewed more than 80 manuscripts for Geophysics, Interpretation, and other geophysical journals since completing his PhD. He served as technical committee chair of GEM 2019 Xi’an: International Workshop on Gravity, Electrical and Magnetic Methods and Their Applications and as the lead organizer of a postconvention workshop, Machine Learning/Artificial Intelligence in Mineral Exploration, at the 2020 SEG Annual Meeting. He is currently serving as the SEG Mining Committee key contact (2020–2022). Jiajia is also an associate editor for the new multiphysics section of Geophysics.

Jiajia has established himself as a talented and prolific researcher with a passion for the field of geophysics. He is also a devoted contributor to SEG and a dedicated educator. It is fitting that Jiajia is honored by SEG with the J. Clarence Karcher Award. On behalf of the friends and colleagues who nominated Jiajia, it is my pleasure to write this citation.


Courses taught

  • 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)

Research Interests

  • 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

References

  1. [1],Li, Y., and J. Sun, 2016, 3D magnetization inversion using fuzzy c-means clustering with application to geology differentiation: Geophysics, 81(5), J61-J78.
  2. [2],Sun, J., and Y. Li, 2018, Magnetization clustering inversion Part I: Building an automated numerical optimization algorithm: Geophysics, 83(5), J61-J73.
  3. [3],Sun, J., and Y. Li, 2016, Joint inversion of multiple geophysical data using guided fuzzy c-means clustering: Geophysics, 81(3), ID37-ID57.
  4. [4],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.
  5. [5],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.

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