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Faculty Profile

Jiajia SunJiajia Sun

Associate Professor of Geophysics

Curriculum Vitae

Office: Science & Research 1, 127A
Contact: jsun20@uh.edu - 713-743-7380

Education: Ph.D., 2015, Geophysics, minor in Mathematical & Computer Sciences, Colorado School of Mines
B.S., 2008, Geophysics, China University of Geosciences

Google Scholar Profile

Accepting Graduate Students? Yes

My research interests revolve around the theme of better imaging, characterizing and monitoring of subsurface systems. My research is highly interdisciplinary because I constantly cross disciplinary boundaries and utilize methods and tools developed in convex optimization, computer vision, pattern recognition, remote sensing, medical imaging and machine learning. My research is also computationally intensive because I rely on heavy computational resources such as GPUs and clusters to carry out my research.

My current research focuses on:

  • Developing advanced methods for critical minerals and rare earth element (REE) deposit exploration using airborne geophysics and joint inversion;
  • Solving geophysical inverse problems and assessing uncertainty using deep generative models;
  • Developing joint inversion algorithms for integrated imaging of the Earth based on multi-physical geoscience data sets;
  • Differentiating geological units through integrative modeling of multi-physical geoscience data;
  • Quantifying uncertainties of geophysical inversions in both deterministic and Bayesian inversion frameworks;
  • Tackling magnetic remanence problem by integrating geophysics and machine learning; and
  • Developing advanced numerical algorithms for geologically constrained inversion of various geophysical data.

18. Wei, X., K. Li, and J. Sun, 2023, Mapping critical mineral resources using airborne geophysics, 3D joint inversion and geology differentiation: A case study of a buried niobium deposit in the Elk Creek carbonatite, Nebraska, USA, Geophysical Prospecting, accepted for publication, https://doi.org/10.1111/1365-2478.13280

17. Wei, X., J. Sun, and M. K. Sen, 2023, Quantifying uncertainty of salt body shapes recovered from gravity data using trans-dimensional Markov chain Monte Carlo sampling: Geophys. J. Int., 232(3), 1957-1978, https://doi.org/10.1093/gji/ggac430

16. Hu, Y, X. Wei, X. Wu, J. Sun, J. Chen, Y. Huang and J. Chen, 2023, A deep learning enhanced framework for multi-physics joint inversion: Geophysics, 88(1), K13-K26, https://doi.org/10.1190/geo2021-0589.1

15. Wei, X., and J. Sun, 2022, 3D probabilistic geology differentiation based on airborne geophysics, mixed Lp norm joint inversion and physical property measurements, Geophysics, 87(4), K19-K33, https://doi.org/10.1190/geo2021-0833.1
*Nominated by editors to be highlighted in Geophysics Bright Spots in TLE: https://library.seg.org/doi/epub/10.1190/tle41100730.1

14. Li, X., and J. Sun, 2022, Toward a better understanding of the recoverability of physical property relationships from geophysical inversions of multiple potential-field datasets: Geophys. J. Int., 230(3), 1489-1507, https://doi.org/10.1093/gji/ggac130

13. Wei, X., and J. Sun, 2021, Uncertainty analysis of 3D potential-field deterministic inversion using mixed Lp norms: Geophysics, 86(6), G133-G158, https://doi.org/10.1190/geo2020-0672.1

12. Sun, J., and X. Wei, 2021, Recovering sparse models in 3D potential-field inversion without bound dependence or staircasing problems using a mixed Lp-norm regularization, Geophysical Prospecting, 69, 901-910, https://doi.org/10.1111/1365-2478.13063

11. Nurindrawati, F. D., and J. Sun, 2020, Predicting total magnetization directions using convolutional neural networks: Journal of Geophysical Research: Solid Earth, 125, no. 10, e2020JB019675, https://doi.org/10.1029/2020JB019675
* Featured as Editor’s Highlight on Eos: https://eos.org/editor-highlights/machine-learning-for-magnetics
* Featured as cover image on the same issue: https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/jgrb.53494

10. Sun, J., A. Melo, J. D. Kim, and X. Wei, 2020, Unveiling the 3D undercover structure of the Precambrian intrusive complex by integrating airborne magnetic and gravity gradient data into 3D quasi-geology model building: Interpretation, 8(4), SS15-SS29, https://doi.org/10.1190/int-2019-0273.1

9. Bernier, C., Y. Wang, M. Estes, R. Lei, B. Jia, S. Wang, and J. Sun, 2019, Clustering surface ozone diurnal cycles to understand the impact of circulation patterns in Houston, TX: Journal of Geophysical Research: Atmospheres, 124, 13,457-13,474. https://doi.org/10.1029/2019JD031725

8. Sun, J., and Y. Li, 2019, Magnetization clustering inversion Part II: Assessing the uncertainty of recovered magnetization directions: Geophysics, 84(4), J17-J29. https://doi.org/10.1190/geo2018-0480.1
* Nominated by editors to be highlighted in Geophysics Bright Spots in TLE https://library.seg.org/doi/pdf/10.1190/tle38080646.1

7. Sun, J., and Y. Li, 2018, Magnetization clustering inversion Part I: Building an automated numerical optimization algorithm: Geophysics, 83(5), J61-J73. https://doi.org/10.1190/geo2017-0844.1
* Nominated by editors to be mentioned in Geophysics Bright Spots in TLE https://library.seg.org/doi/pdf/10.1190/tle37100780.1

6. Melo, A., J. Sun and Y. Li, 2017, Geophysical inversions applied to 3D geology characterization of an iron oxide copper gold deposit in Brazil: Geophysics, 82(5), K1-K13. https://doi.org/10.1190/geo2016-0490.1

5. 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. https://doi.org/10.1093/gji/ggw442

4. Li, Y., and J. Sun, 2016, 3D magnetization inversion using fuzzy c-means clustering with application to geology differentiation: Geophysics, 81(5), J61-J78. https://doi.org/10.1190/geo2015-0636.1

3. Sun, J., and Y. Li, 2016, Joint inversion of multiple geophysical data using guided fuzzy c-means clustering: Geophysics, 81(3), ID37-ID57. https://doi.org/10.1190/geo2015-0457.1

2. Sun, J., and Y. Li, 2015, Multidomain petrophysically constrained inversion and geology differentiation using guided fuzzy c-means clustering: Geophysics, 80(4), ID1-ID18. https://doi.org/10.1190/geo2014-0049.1
* Awarded Honorable Mention of Best Paper in GEOPHYSICS

1. Sun, J., and Y. Li, 2014, Adaptive Lp inversion for simultaneous recovery of both blocky and smooth features in a geophysical model: Geophys. J. Int., 197(2), 882-899. https://doi.org/10.1093/gji/ggu067