Peer-Reviewed Publications - 2023

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BEG Peer-reviewed Papers In Press

Avigyan Chatterjee, Igonin, N., and Trugman D., 2022, A real-time and data-driven ground motion prediction framework for earthquake early warning: Bulletin of the Seismological Society of America, http://doi.org/10.1785/0120220180.

Bhattacharya, S., Wisian, K., Savvaidis, A., and Eros, M., 2022, Integrated subsurface characterization of a low-temperature geothermal test site, Gulf Coast, Texas: Second International Meeting for Applied Geoscience & Energy© 2022 Society of Exploration Geophysicists and the American Association of Petroleum Geologists, 28 August - 2 September, 2022, Houston, TX, p. 2007–2011, http://doi.org/10.1190/image2022-3745410.1.

Jin, L., Curry, W., Zoback, M., Hussenoeder, S., Savvaidis, A., Nicot, J.-P., Hennings, P., and Bhargava, P., 2022, Rapid geomechanical analysis of injection-related earthquakes: Second International Meeting for Applied Geoscience & Energy © 2022 Society of Exploration Geophysicists and the American Association of Petroleum Geologists, 28 August - 2 September, 2022, Houston, TX, p. 1531–1535, http://doi.org/10.1190/image2022-3740083.1.

Larson, T. E., Loucks, R. G., Sivil, J. E., Hattori, K. E., and Zahm, C. K., 2022, Machine learning classification of Austin Chalk chemofacies from high-resolution X-ray fluorescence core characterization: AAPG Bulletin, http://doi.org/10.1306/09232220095.

Liu, L., Xu, J., Stockli, D.F., Lawton, T., and Blakey, R.C., 2022, Decoding post-orogenic sediment recycling and dispersal using detrital zircon core and rim ages: Basin Research, 21 p., http://doi.org/10.1111/bre.12719.

BEG Peer-reviewed Papers

Bump, A. P., and Hovorka, S. D., 2023, Fetch-trap pairs: exploring definition of carbon storage prospects to increase capacity and flexibility in areas with competing uses: International Journal of Greenhouse Gas Control, v. 122, no. 103817, 10 p., http://doi.org/10.1016/j.ijggc.2022.103817.

Chen, W., Oboué, Y. A. S. I., and Chen, Y., 2023, Retrieving useful signals from highly corrupted erratic noise using robust residual dictionary learning: Geophysics, v. 88, no. 1, p. WA55–WA64, http://doi.org/10.1190/geo2022-0168.1.

Chen, Y., Fomel, S., and Abma, R., 2023, Joint deblending and source time inversion: Geophysics, v. 88, no. 1, p. WA27–WA35, http://doi.org/10.1190/geo2022-0149.1.

Chen, Yangkang, Savvaidis, A., Fomel, S., Chen, Yunfeng, and Saad, O. M., 2023, RFloc3D: a machine learning method for 3D microseismic source location using P- and S-wave arrivals: IEEE Transactions on Geoscience and Remote Sensing, v. 61, no. 5901310, 10 p., http://doi.org/10.1109/TGRS.2023.3236572.

Chen, Yangkang, Savvaidis, A., Fomel, S., Chen, Yunfeng, Saad, O. M., Wang, H., Oboué, Y. A. S. I., Yang, L., and Chen, W., 2023, Denoising of distributed acoustic sensing seismic data using an integrated framework: Seismological Research Letters, v. 94, no. 1, p. 457–472, http://doi.org/10.1785/0220220117.

Ershadnia, R., Singh, M., Mahmoodpour, S., Meyal, A., Moeini, F., Hosseini, S. A., Sturmer, D. M., Rasoulzadeh, M., Dai, Z., and Soltanian, M. R., 2023, Impact of geological and operational conditions on underground hydrogen storage: International Journal of Hydrogen Energy, v. 48, no. 4, p. 1450–1471, http://doi.org/10.1016/j.ijhydene.2022.09.208.

Gale, J. F. W., Elliott, S.J., Rysak, B. G., and Laubach, S. E., 2023, The critical role of core in understanding hydraulic fracturing, in Neal, A., Ashton, M., Williams, L. S., Dee, S. J., Dodd, T. J. H. and Marshall, J. D., eds., Core values: the role of core in twenty-first century reservoir characterization: Geological Society of London, Special Publication, v. 527, no. 1, 16 p., http://doi.org/10.1144/SP527-2021-198.

Hopmans, J. W., Green, T. R., and Young, M. H., 2023, Western U.S. multistate research project on “water movement in soils”: a retrospective: Vadose Zone Journal, v. 22, no. e20245, 7 p., http://doi.org/10.1002/vzj2.20245.

Hosseini, S., Larson, R., Shokouhi, P., Kumar, V., Prathipati, S., Kifer, D., Garcez, J., Ayala, L., Reidl, M., Hill, B., and and three others, 2023, Reservoir modeling using fast predictive machine learning algorithms for geological carbon storage, in Mishra, S., ed., Machine learning applications in subsurface energy resource management: Boca Raton, Fla., CRC Press, p. 233-250, http://doi.org/10.1201/9781003207009-17.

Kaur, H., Fomel, S., and Pham, N., 2023, Automated hyperparameter optimization for simulating boundary conditions for acoustic and elastic wave propagation using deep learning: Geophysics, v. 88, no. 1, p. WA309–WA318, http://doi.org/10.1190/geo2022-0231.1.

Kaur, H., Pham, N., Fomel, S., Geng, Z., Decker, L., Gremillion, B., Jervis, M., Abma, R., and Gao, S., 2023, A deep learning framework for seismic facies classification: Interpretation, v. 11, no. 1, p. T107–T116, http://doi.org/10.1190/INT-2022-0048.1.

Khaled, M. S., Wang, N., Ashok, P., and van Oort, E., 2023, Downhole heat management for drilling shallow and ultra-deep high enthalpy geothermal wells: Geothermics, v. 107, no. 102604, 17 p., http://doi.org/j.geothermics.2022.102604.

Lee, J., and Lumley, D. E., 2023, Predicting shale mineralogical brittleness index from seismic and elastic property logs using interpretable deep learning: Journal of Petroleum Science and Engineering, v. 220, part A, no. 111231, 14 p., http://doi.org/10.1016/j.petrol.2022.111231.

Nicot, J.-P., Darvari, R., Smye, K. M., and Goodman, E., 2023, Geochemical insights from formation waters produced from Wolfcampian and Leonardian intervals of the Midland Basin, Texas, USA: Applied Geochemistry, v. 150, no. 6, 33 p., http://doi.org/10.1016/j.apgeochem.2023.105585.

Saad, O. M., Fomel, S., Abma, R., and Chen, Y., 2023, Unsupervised deep learning for 3D interpolation of highly incomplete data: Geophysics, v. 88, no. 1, p. WA189–WA200, http://doi.org/10.1190/GEO2022-0232.1.

Yang, L., Fomel, S., Wang, S., Chen, X., Chen, W., Saad, O. M., and Chen, Y., 2023, Porosity and permeability prediction using a transformer and periodic long short-term network: Geophysics, v. 88, no. 1, p. WA293–WA308, http://doi.org/10.1190/GEO2022-0150.1.

Yang, L., Fomel, S., Wang, S., Chen, X., Chen, W., Saad, O., and Chen, Y., 2023, Denoising of distributed acoustic sensing data using supervised deep learning: Geophysics, v. 88, no. 1, p. WA91–WA104, http://doi.org/10.1190/geo2022-0138.1.

Yang, L., Wang, S., Chen, X., Chen, W., Saad, O. M., and Chen, Y., 2023, Deep-learning missing well-log prediction via long short-term memory network with attention-period mechanism: Geophysics, v. 88, no. 1, p. D31–D48, http://doi.org/10.1190/GEO2020-0749.1.