Publications

Advances and challenges in machine learning-based identification of organic pollutant sources in heterogeneous aquifers
Zhang, Y., M. Cao, Z. Dai, H. Wang, S. Jia, L. Xu, X. Zhang, M.R. Soltanian, J.S. Calvete, H. Yin, and K.C. Carroll (2026). Journal of Environmental Chemical Engineering. (Machine Learning)

Unraveling soil moisture dynamics with dual-scale interpretable machine learning: Cover cropping and irrigation insights in semi-arid agriculture
Yin, H., P. Bista, R. Ghimire, H. Yang, and K.C. Carroll (2026). Vadose Zone Journal. (Machine Learning)

Innovations in underground hydrogen storage with multiphysics simulations, optimization, and monitoring: A review
Zhang, Y., Z. Dai, H.V. Thanh, M. Cao, L. Xu, X. Zhang, B. Yan, P.H. Stauffer, H. Yin, K.C. Carroll, and M.R. Soltanian (2026). Earth-Science Reviews. (Machine Learning)

Combining hydrologic, chemical, and geophysical deep learning-based inversion for heterogeneous aquifer structure identification
Xia, Y., C. Zhan, Z. Dai, J. Wu, X. Zhang, H. Yin, L. Zhu, J. Yan, Z. Wang, M.R. Soltanian, and K.C. Carroll (2026). Journal of Hydrology. (Machine Learning)

Impact of observation and surrogate-model noises on deep learning-based subsurface heterogeneous structure identification through monitoring network optimization
Xia, Y., C. Zhan, Z. Dai, J. Wu, X. Zhang, H. Yin, J. Yan, J. Chen, Z. Wang, M.R. Soltanian, and K.C. Carroll (2026). Advances in Water Resources. (Machine Learning)

Impacts of validation strategy and geological complexity on machine learning-based subsurface structure modeling
Zhan, C., J.J. Jiao, Y. Ma, H. Wang, M.R. Soltanian, K.C. Carroll, Z. Dai (2025). Journal of Hydrology. (Machine Learning)

Fluid stretching at facies interfaces governs solute transport
Soltanian, M.R., C.D. Wallace, F.P.J. de Barros, Z. Dai, and K.C. Carroll (2025). Geophysical Research Letters.

Imaging hyporheic exchange by integrating deep learning and physics-informed inversion of time-lapse self-potential data
Yin, H., S.J. Ikard, D.F. Rucker, S.C. Brooks, Z. Dai, and K.C. Carroll (2025). Geophysical Research Letters. (Machine Learning)

Hybrid vision transformer with convolutional blocks approach for subsurface electrical resistivity tomography inversion
Yin, H., K.C. Carroll, Yuan, Y., Jamil, A., Rucker, D.F., Dai, Z., & Soltanian, M.R. (2025). Journal of Geophysical Research: Machine Learning and Computation. (Machine Learning)

Second-order degradation modeling and multiscale feature fusion for high-fidelity segmentation of low-quality digital rock images
Xua, L., Z. Dai, Y. Du, X. Zhang, H. Yin, M.R. Soltanian, H.V. Thanh, M. Cai, and K.C. Carroll (2025). Geophysical Research Letters. (Machine Learning)

Vadose Zone Inverse Modeling of Pneumatic Pumping Tests at the Hanford Site: Influence of Barometric Pressure Fluctuations
Moeini, F., K.C. Carroll, Z. Dai, and M.R. Soltanian (2025). Science of the Total Environment.

Incorporating Deep Learning into Hydrogeological Modeling: Advancements, Challenges, and Future Directions
Dai, Z., C. Zhan, H. Yin, J. Chen, L. Xu, Y. Xia, S. Yang, W. Chen, M. Cao, Z. Du, X. Zhang, B. Yan, Y. Ma, H. Wang, F. Moeini, M.R. Soltanian, and K.C. Carroll (2025). Journal of Geophysical Research: Machine Learning and Computation. (Machine Learning)

Towards Artificial General Intelligence in Hydrogeological Modeling with an Integrated Latent Diffusion Framework
Zhan, C., Z. Dai, J.J. Jiao, M.R. Soltanian, H. Yin, and K.C. Carroll (2025). Geophysical Research Letters. (Machine Learning)

Self-potential tomography preconditioned by particle swarm optimization—application to monitoring hyporheic exchange in a Bedrock River
Ikard, S.J., K.C. Carroll, S.C. Brooks, D.F. Rucker, G. Smith-Vega, and A. Elwes (2024). Water Resources Research.

Comparison of Machine Learning and Electrical Resistivity Arrays to Inverse Modeling for Locating and Characterizing Subsurface Targets
Jamil, A., D.F. Rucker, D. Lu, S.C. Brooks, A.M. Tartakovsky, H. Cao, and K.C. Carroll (2024). Journal of Applied Geophysics. (Machine Learning)

Employing generative adversarial neural networks as surrogate model for reactive transport modeling in the hyporheic zone
Moeini, F., R. Ershadnia, R.L. Rubinstein, R.Versteeg, P. Li, J.T. McGarr, A. Meyal, C.D. Wallace, Z. Dai, K.C. Carroll, and M.R. Soltanian (2024). Journal of Hydrology. (Machine Learning)

Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence
Zhan, C., Z. Dai, S. Yin, K.C. Carroll, and M.R. Soltanian (2024). Water Research. (Machine Learning)

Analysis and Prediction of Produced Water Quantity and Quality in the Permian Basin using Machine Learning Techniques
Jiang, W., Pokharel, B., Lin, L., Cao, H., Carroll, K.C., Zhang, Y., Galdeano, C., Musale, D.A., Ghurye, G.L., & Xu, P. (2021). Science of the Total Environment.

Characterization of Produced Water and Surrounding Surface Water in the Permian Basin, US
Jiang, W., Xu, X., Hall, R., Zhang, Y., Carroll, K.C., Ramos, F., Engle, M.A., Lin, L., Wang, H., Sayer, M., & Xu, P. (2022). Journal of Hazardous Materials.

Datasets Associated with the Characterization of Produced Water and Pecos River Water in the Permian Basin
Jiang, W., Xu, X., Hall, R., Zhang, Y., Carroll, K.C., Ramos, F., Engle, M.A., Lin, L., Wang, H., Sayer, M., & Xu, P. (2022). Data in Brief.

Mesilla/Conejos-Médanos Basin: U.S.-Mexico Transboundary Water Resources
Robertson, A.J., Matherne, A.-M., Pepin, J.D., Ritchie, A.B., Sweetkind, D.S., Teeple, A., Granados Olivas, A., García Vásquez, A.C., Carroll, K.C., Fuchs, E.H., & Galanter, A. (2022). Water.

Spatial variability of produced-water quality and alternative-source water analysis applied to the Permian Basin, USA
Chaudhary, B.K., Sabie, R., Engle, M.A., Xu, P., Willman, S., & Carroll, K.C. (2019). Hydrogeology Journal.

Toxicological Characterization of Produced Water from the Permian Basin
Hu, L., Jiang, W., Xu, X., Wang, H., Carroll, K.C., Xu, P., & Zhang, Y. (2022). Science of the Total Environment.

Volatile-Organic Molecular Characterization of Shale-Oil Produced Water from the Permian Basin
Khan, N.A., Engle, M.A., Dungan, B., Holguin, F.O., Xu, P., & Carroll, K.C. (2016). Chemosphere.

Characterization of Produced Water in the Permian Basin for Potential Beneficial Use
Xu, P., Zhang, Y., Jiang, W., Hu, L., Xu, X., Carroll, K.C., & Khan, N. (2022). WRRI Technical Completion Report T-398.

An Integrated Geochemical Approach for Defining Sources of Groundwater Salinity in the Southern Rio Grande Valley of the Mesilla Basin, New Mexico and West Texas, USA
Kubicki, C., Carroll, K.C., Witcher, J.C., & Robertson, A. (2020). WRRI Technical Completion Report T-388.

Conference papers coming soon.