Bureau of Economic Geology

Dr. Alexander Sun

Alex Sun
Senior Research Scientist
Bureau of Economic Geology
The University of Texas at Austin
University Station, Box X
Austin, Texas 78713-8924

Research Interests

Sustainable water resources management and decision support systems

Multiphase flow and multicomponent transport in porous media

Real-time data analytics, machine learning, optimization

Risk assessment and monitoring related to geologic carbon storage 

Remote sensing applications in hydrology

Modeling and management of karst aquifer systems


B.S. Civil and Environmental Engineering, University of California at Los Angeles, Los Angeles, California, 1995

M.S. Civil and Environmental Engineering, University of California at Berkeley, Berkeley, California, 1996

Ph.D. Civil and Environmental Engineering, University of California at Berkeley, Berkeley, California, 2000

Professional History

Senior Research Scientist, Research Scientist—Bureau of Economic Geology, 2011–Present

Principal Research Engineer, Senior Research Engineer, Research Engineer—Southwest Research Institute, 2003–2011

Environmental Engineer—SUNDA Environmental Technologies, LLC, 2000-2003

Environmental Engineer—Tetra Tech Inc., 1999–2000

Graduate Research Fellow—Los Alamos National Laboratory, 1998–1999

Selected Publications

Sun, A.Y., 2020, Optimal carbon storage reservoir management through deep reinforcement learning, Applied Energy, v. 278, 115660. [PDF]

Sun, A. Y. and Scanlon, B.R., 2019, How Can Big Data and Machine Learning Benefit Environment and Water Management: A Survey of Methods, Applications, and Future Directions, Environmental Research Letters, 14, 073001. [PDF]

Sun, A. Y., Scanlon, B.R., Zhang, Z., Walling, D., Bhanja, S., Mukherjee, A., Zhong, Z., 2019, Combining physically-based modeling and deep earning for fusing GRACE satellite data: Can we learn from mismatch? Water Resources Research, 55(2), 1179-1195 [PDF]

Sun, A.Y., Zhong, Z., Jeong, H., Yang, Q., 2019. Building complex event processing capability for intelligent environmental monitoring. Environmental Modelling & Software, 116: 1-6. [PDF]

Sun, A. Y., 2018, Discovering state-parameter mappings in subsurface models using generative adversarial networks, Geophysical Research Letters, 45(20): 11,137-11,146. [PDF]

Sun, A. Y., Xia, Y., Caldwell, T., and Hao, Z., 2018, Patterns of precipitation and soil moisture extremes in Texas, U.S.: A complex network analysis, Advances in Water Resources, v. 112, 203-213. [PDF]

Sun, A. Y., Jeong, H., Gonzalez, A., and Templeton, T., 2018, Metamodeling-based approach for risk assessment and cost estimation: application to geological carbon sequestration, Computers and Geosciences, v. 113, p. 70-80. [PDF]

Sun, A. Y., Lu, J., Freifeld, B. M., Hovorka, S. D., & Islam, A. (2016). Using pulse testing for leakage detection in carbon storage reservoirs: A field demonstration. International Journal of Greenhouse Gas Control, 46, 215-227. [PDF]

Sun, A.Y., Chen, J., Donges, J., 2015, Global Terrestrial Water Storage Connectivity Revealed Using Complex Climate Network Analyses, Nonlinear Geophysical Processes, 22, 433-446, 2015.[PDF]

Sun, A. Y., Lu, J., & Hovorka, S. 2015. A harmonic pulse testing method for leakage detection in deep subsurface storage formations. Water Resources Research [PDF]

Sun, A. Y., Miranda, R. M., & Xu, X. 2014. Development of multi-metamodels to support surface water quality management and decision making. Environmental Earth Sciences, 1-12. [PDF]

Sun, A.Y., D. Wang, and X. Xu, 2014, Monthly streamflow forecasting using Gaussian Process regression, Journal of Hydrology, 511, 72–81 [PDF]

Sun, A.Y., J.-P. Nicot, X. Zhang, 2013, Optimal design of pressure-Based, leakage detection monitoring networks for geologic carbon sequestration repositories: International Journal of Greenhouse Gas Control. 19, 251–261.[PDF]

Sun, A.Y., 2013, Predicting groundwater level changes using GRACE data. Water Resources Research, 49, 1–13, doi:10.1002/

Sun, A. Y., 2013, Enabling collaborative decision-making in watershed management using cloud-computing services: Environmental Modelling & Software, v. 41, p. 93-97. [PDF]

Sun, A. Y., Zeidouni, M., Nicot, J. -P., Lu, Zhiming, and Zhang, D., 2013, Assessing leakage detectability at geologic CO2 sequestration sites using the probabilistic collocation method: Advances in Water Resources, v. 56 (2013) 49-60. [PDF]

Sun, A. Y., and Nicot, J. -P., 2012, Inversion of pressure anomaly data for detecting leakage at geologic carbon sequestration sites: Advances in Water Resources, v. 44, p. 20-29.[PDF]

Liu, Y., Sun, A. Y., Nelson, K., and Hipke, W. E., 2012, Cloud computing for integrated stochastic groundwater uncertainty analysis: International Journal of Digital Earth, v. 5, no. 5, p. 1-25. DOI:10.1080/17538947.2012.687778. [PDF]

Sun, A. Y., Green, R., Swenson, S., and Rodell, M., 2012, Toward calibration of regional groundwater models using GRACE data: Journal of Hydrology, v. 422-423, p. 1-9. [PDF]

Sun, A. Y., 2011, Identification of geologic fault network geometry by using a grid-based ensemble Kalman filter: Journal of Hazardous, Toxic, and Radioactive Waste, v. 15, no. 4, p. 228-233. [PDF]

Sun, A. Y., Green, R., Rodell, M., and Swenson, S., 2010, Inferring aquifer storage parameters using satellite and in situ measurements: estimation under uncertainty: Geophysical Research Letters, v. 37, L10401. [PDF]

Sun, A. Y., Morris, A., and Mohanty, S., 2009, Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques: Water Resources Research, v. 45, W07424.

University of Texas at Austin

University of Texas

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