Peer-Reviewed Publications - 2023

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BEG Peer-reviewed Papers published online-first

Lawton, T. F., Juárez-Arriaga, E., Stockli, D. F., and Fildani, A., 2023, Modern sand provenance and transport across the western Gulf of Mexico margin: Geological Society of America Bulletin, http://doi.org/10.1130/B37002.1, In press checked 9 18 2023.

Lin, N., Liying Xu, and Moscardelli, L. G., 2023, Market-based asset valuation of hydrogen geological storage: International Journal of Hydrogen Energy, 16 p., http://doi.org/10.1016/j.ijhydene.2023.07.074, In press checked 9 18 2023.

Schuba, C. N., and Moscardelli, L., 2023, Subsurface storage in the Mississippi Salt Basin domes: considerations for the emerging hydrogen economy: AAPG Bulletin, 33 p., http://doi.org/10.1306/05302322160, In press checked 9 18 2023.

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.

Bump, A. P., Bakhshian, S., Ni, H., Hovorka, S. D., Olariu, M. I., Dunlap, D., Hosseini, S. A., and Meckel, T. A., 2023, Composite confining systems: Rethinking geologic seals for permanent CO2 sequestration: International Journal of Greenhouse Gas Control, v. 126, no. 103908, 12 p., http://doi.org/10.1016/j.ijggc.2023.103908.

Chatterjee, A., Igonin, N., and Trugman, D. T., 2023, A real-time and data-driven ground-motion prediction framework for earthquake early warning: Bulletin of the Seismological Society of America, v. 113, no. 2, p. 676–689, http://doi.org/10.1785/0120220180.

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, W., Oboué, Y. A. S. I., Saad, O. M., Yang, L., Wang, H., and Chen, Y., 2023, Imaging point diffractors using a low-rank approximation method: Geophysics, v. 88, no. 5, p. N47–N58, http://doi.org/10.1190/GEO2022-0374.1.

Chen, Y., and Fomel, S., 2023, 3D true-amplitude elastic wave-vector decomposition in heterogeneous anisotropic media: Geophysics, v. 88, no. 3, p. C79–C89, http://doi.org/10.1190/GEO2022-0361.1.

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, Chen, Yunfeng, Fomel, S., Savvaidis, A., Saad, O. M., and Oboué, Y. A. S. I., 2023, Pyekfmm: a Python package for 3D fast-marching-based travel-time calculation and its applications in seismology: Seismological Research Letters, v. 94, no. 4, p. 2050–2059, http://doi.org/10.1785/0220230042.

Chen, Yangkang, Huang, W., Yang, L., Oboué, Y. A. S. I., Saad, O. M., and Chen, Yunfeng, 2023, DRR: an open-source multi-platform package for the damped rank-reduction method and its applications in seismology: Computers & Geosciences, v. 180, no. 105440, 13 p., http://doi.org/10.1016/j.cageo.2023.105440.

Chen, Yangkang, Savvaidis, A., Chen, Yunfeng, Saad, O. M., and Fomel, S., 2023, Enhancing earthquake detection from distributed acoustic sensing data by coherency measure and moving-rank-reduction filtering: Geophysics, v. 88, no. 6, p. WC13–WC23, http://doi.org/10.1190/GEO2023-0020.1.

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., Oboué, Y. A. S. I., Zhang, Q., and Chen, W., 2023, Pyseistr: a Python package for structural denoising and interpolation of multichannel seismic data: Seismological Research Letters, v. 94, no. 3, p. 1703–1714, http://doi.org/10.1785/0220220242.

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.

Doungkaew, N., and Eichhubl, P., 2023, High-temperature fracture growth by constrained sintering of jadeite and quartz aggregates: Journal of Geophysical Research: Solid Earth, v. 128, no. 4, article no. e2022JB025565, 22 p., http://doi.org/10.1029/2022JB025565.

Duffy, O. B., Hudec, M. R., Peel, F., Apps, G., Bump, A., Moscardelli, L., Dooley, T. P., Fernandez, N., Bhattacharya, S., Wisian, K., and Shuster, M. W., 2023, The role of salt tectonics in the energy transition: an overview and future challenges: Tektonika, v. 1, no. 1, p. 18–48, http://doi.org/10.55575/tektonika2023.1.1.11.

Eakin, A. L., Reece, J. S., Milliken, K. L., Locklair, R., and Rathbun, A. P., 2023, Classification of elemental chemofacies as indicators of cement diagenesis in mudrocks of the Permian Spraberry Formation and Wolfcamp formation, western Texas: AAPG Bulletin, v. 107, no. 6, p. 863–886, http://doi.org/https://doi.org/10.1306/10242221142.

Erdi, A., Jackson, C. A.-L., and Soto, J. I., 2023, Extensional deformation of a shale-dominated delta: Tarakan Basin, offshore Indonesia: Basin Research, v. 35, no. 3, p. 1071–1101, http://doi.org/10.1111/bre.12747.

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.

Haddad, M., and Eichhubl, P., 2023, Fault reactivation in response to saltwater disposal and hydrocarbon production for the Venus, TX, Mw 4.0 earthquake sequence: Rock Mechanics and Rock Engineering, v. 56, no. 3, p. 2103–2135, http://doi.org/10.1007/s00603-022-03083-4.

Hennings, P., and Young, M. H., 2023, The TexNet-CISR collaboration and steps toward understanding induced seismicity in Texas, in Buchanan, R. C., Young, M. H., and Murray, K. E., eds., Recent seismicity in the Southern Midcontinent, USA: scientific, regulatory, and industry responses: Geological Society of America, Special Paper, v. 559, p. 53-71, http://doi.org/10.1130/2023.2559(06).

Hooker, J. N., Katz, R. F., Laubach, S. E., Cartwright, J., Eichhubl, P., Ukar, E., Bloomfield, D., and Engelder, T., 2023, Fracture-pattern growth in the deep, chemically reactive subsurface: Journal of Structural Geology, v. 173, no. 104915, 21 p., http://doi.org/10.1016/j.jsg.2023.104915.

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.

Ikonnikova, S. A., Scanlon, B. R., and Berdysheva, S. A., 2023, A global energy system perspective on hydrogen trade: a framework for the market color and the size analysis: Applied Energy, v. 330, part A, no. 120267, 23 p., http://doi.org/10.1016/j.apenergy.2022.120267.

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.

Kaur, H., Zhang, Q., Witte, P., Liang, L., Wu, L., and Fomel, S., 2023, Deep-learning-based 3D fault detection for carbon capture and storage: Geophysics, v. 88, no. 4, p. IM101–IM112, http://doi.org/10.1190/geo2022-0755.1.

Khaled, M. S., Chen, D., Ashok, P., and van Oort, E., 2023, Drilling heat maps for active temperature management in geothermal wells: Society of Petroleum Engineers Journal, v. 28, no. 4, p. 1577–1593, http://doi.org/10.2118/210306-PA.

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.

Khaled, M., Wang, N., Ashok, P., Chen, D., and van Oort, E., 2023, Strategies for prevention of downhole tool failure caused by high bottomhole temperature in geothermal and high-pressure/high-temperature oil and gas wells: Society of Petroleum Engineers Drilling & Completion, v. 38, no. 2, p. 243–260, http://doi.org/10.2118/212550-PA.

Kyle, J. R., Quintero, T. R., Ukar, E., Miller, N. R., Elliott, S. J., and Colbert, M., 2023, Dolomite cement microstratigraphy: a record of brine evolution and ore precipitation mechanisms, upper Knox Group, Tennessee and Kentucky, USA: Geology, v. 51, no. 4, p. 392–396, http://doi.org/10.1130/G50689.1.

Kyle, J. R., Stockli, D. F., McBride, E. F., and Elliott, B. A., 2023, Covering the Great Unconformity in southern Laurentia: detrital zircon studies of provenance evolution during Cambrian marine transgression (Llano Uplift, Texas): Geological Society of America Bulletin, v. 135, no. 5-6, p. 1163–1177, http://doi.org/10.1130/B36389.1.

Larson, T. E., Loucks, R. G., Sivil, J. E., Hattori, K. E., and Zahm, C. K., 2023, Machine learning classification of Austin Chalk chemofacies from high-resolution x-ray fluorescence core characterization: AAPG Bulletin, v. 107, no. 6, p. 907–927, http://doi.org/10.1306/09232220095.

Lee, J., and Lumley, D. E., 2023, Interpreting the effects of shale rock properties on seismic anisotropy by statistical and machine learning methods: Geoenergy Science and Engineering, v. 224, no. 211631, 17 p., http://doi.org/10.1016/j.geoen.2023.211631.

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.

Li, H., Liu, B., Liu, X., Al-Shuhail, A. A., Mahmoud, S. M. H., and Chen, Y., 2023, Frequency-Independent Centroid Frequency Shift Method for Signal Attenuation Estimation: IEEE Transactions on Geoscience and Remote Sensing, v. 61, no. 1, p. 4504212, http://doi.org/10.1109/TGRS.2023.3293645.

Liu, L., Xu, J., Stockli, D. F., Lawton, T. F., and Blakey, R. C., 2023, Decoding post-orogenic sediment recycling and dispersal using detrital zircon core and rim ages: Basin Research, v. 35, no. 2, p. 489–509, http://doi.org/10.1111/bre.12719.

Liu, W., Liu, Y., Li, S., and Chen, Y., 2023, A review of variational mode decomposition in seismic data analysis: Surveys in Geophysics, v. 44, no. 2, p. 323–355, http://doi.org/10.1007/s10712-022-09742-z.

Loucks, R. G., Reed, R. M., Zeng, H., and Periwal, P., 2023, Carbonate sedimentation and reservoirs associated with a volcanic mound in an open-marine, deep-water, drowned platform setting, Elaine field area, Upper Cretaceous Anacacho Formation, South Texas U.S.A.: Marine and Petroleum Geology, v. 154, no. 106314, 17 p., http://doi.org/10.1016/j.marpetgeo.2023.106314.

Loucks, R. G., Zahm, C. K., Hattori, K. E., and Sanchez, T., 2023, Middle platform carbonate depositional systems and lithofacies patterns in the Lower Ordovician Ellenburger Group, Tobosa Basin in West Texas, U.S.A., and subsequent Sauk-Tppecanoe megasequence boundary meteoric karsting: Marine and Petroleum Geology, v. 150, no. 106176, 17 p., http://doi.org/10.1016/j.marpetgeo.2023.106176.

Male, F., Marder, M. P., Ruiz-Maraggi, L. M., and Lake, L. W., 2023, Bluebonnet: Scaling solutions for production analysis from unconventional oil and gas wells: The Journal of Open Source Software, v. 8, no. 88, article no. 5255, 3 p., http://doi.org/10.21105/joss.05255.

Meckel, T. A., and Beckham, E. C., 2023, High-resolution geologic modeling and CO2 flow simulation of a realistic clastic deltaic 3D model derived from a laboratory flume tank experiment: International Journal of Greenhouse Gas Control, v. 125, no. 103892, 16 p., http://doi.org/10.1016/j.ijggc.2023.103892, Graduate student co-author.

Meckel, T. A., Treviño, R. H., Hovorka, S. D., and Bump, A. P., 2023, Mapping existing wellbore locations to compare technical risks between onshore and offshore CCS activities in Texas: Greenhouse Gases: Science and Technology, v. 13, no. 3, p. 493–504, http://doi.org/10.1002/ghg.2220.

Ni, H., Bakhshian, S., and Meckel, T. A., 2023, Effects of grain size and small-scale bedform architecture on CO2 saturation from buoyancy-driven flow: Scientific Reports, v. 13, no. 2474, 13 p., http://doi.org/10.1038/s41598-023-29360-y.

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.

Nikolinakou, M. A., Flemings, P. B., Gao, B., and Saffer, D. M., 2023, The evolution of pore pressure, stress, and physical properties during sediment accretion at subduction zones: Journal of Geophysical Research: Solid Earth, v. 128, no. 6, article no. e2022JB025504, 38 p., http://doi.org/10.1029/2022JB025504.

Oboué, Y. A. S. I., Chen, W., Saad, O. M., and Chen, Y., 2023, Adaptive damped rank-reduction method for random noise attenuation of three-dimensional seismic data: Surveys in Geophysics, v. 44, no. 3, p. 847–875, http://doi.org/10.1007/s10712-022-09756-7.

Ogiesoba, O. C., Bhattacharya, S., Karakaya, S., and Cortez, T., 2023, Prestack seismic velocity ratio evaluation of a mixed siliciclastic–carbonate formation: case study from the Strawn Group on the Eastern Shelf Texas: Energies, v. 16, no. 2037, 24 p., http://doi.org/10.3390/en16042037.

Ogiesoba, O., Karakaya, S., and Cortez, T., 2023, Simultaneous seismic inversion study for channel sandstone identification, northern part of the Eastern Shelf, King County, North-Central Texas: Interpretation, v. 11, no. 3, p. T593–T610, http://doi.org/10.1190/INT-2022-0096.1.

Peng, S., LaManna, J., Periwal, P., and Shevchenko, P., 2023, Water imbibition and oil recovery in shale: dynamics and mechanisms using integrated centimeter-to-nanometer-scale imaging: SPE Reservoir Evaluation & Engineering, v. 26, no. SPE-210567-PA, p. 51–63, http://doi.org/10.2118/210567-PA.

Peng, S., Shevchenko, P., and Ko, L. T., 2023, Shale wettability: untangling the elusive property with an integrated imbibition and imaging technique and a new hypothetical theory: SPE Reservoir Evaluation & Engineering, v. 26, no. SPE-212276-PA, p. 40–50, http://doi.org/10.2118/212276-PA.

Ruiz Maraggi, L. M., and Moscardelli, L. G., 2023, Modeling hydrogen storage capacities, injection and withdrawal cycles in salt caverns: introducing the GeoH2 salt storage and cycling app: International Journal of Hydrogen Energy, v. 48, no. 69, p. 26921–26936, http://doi.org/10.1016/j.ijhydene.2023.03.293.

Ruiz Maraggi, L. M., Lake, L. W., and Walsh, M. P., 2023, Limitations of rate normalization and material balance time in rate-transient analysis of unconventional reservoirs: Geoenergy Science and Engineering, v. 227, no. 211844, 20 p., http://doi.org/10.1016/j.geoen.2023.211844.

Saad, O. M., Chen, W., Zhang, F., Yang, L., Zhou, X., and Chen, Y., 2023, Self-attention fully convolutional DenseNets for automatic salt segmentation: IEEE Transactions on Neural Networks and Learning Systems, v. 34, no. 7, p. 3415–3428, http://doi.org/10.1109/TNNLS.2022.3175419.

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.

Sahoo, S. K., Gilleaudeau, G. J., Wilson, K., Hart, B., Barnes, B. D., Faison, T., Bowman, A. R., Larson, T. E., and Kaufman, A. J., 2023, Basin-scale reconstruction of euxinia and Late Devonian mass extinctions: Nature, v. 615, p. 640–645, http://doi.org/10.1038/s41586-023-05716-2.

Scanlon, B. R., Fakhreddine, S., Rateb, A., de Graaf, I., Famiglietti, J., Gleeson, T., Grafton, R. Q., Jobbagy, E., Kebede, S., Kolusu, S. R., Konikow, L. F., Long, D., Mekonnen, M., Schmied, H. M., Mukherjee, A., MacDonald, A., Reedy, R. C., Shamsudduha, M., Simmons, C. T., Sun, A., Taylor, R. G., Villholth, K. G., Vörösmarty, C. J., and Zheng, C., 2023, Global water resources and the role of groundwater in a resilient water future: Nature Reviews: Earth & Environment, v. 4, p. 87–101, http://doi.org/10.1038/s43017-022-00378-6.

Song, Y., Lee, J., Yeeh, Z., Kim, M., and Byun, J., 2023, Improved lithospheric seismic velocity and density model of the Korean Peninsula from ambient seismic noise data using machine learning: Journal of Asian Earth Sciences, v. 254, no. 105728, 12 p., http://doi.org/10.1016/j.jseaes.2023.105728.

Soto, J. I., and Hudec, M. R., 2023, Mud volcanoes guided by thrusting in compressional settings: Geology, v. 51, no. 8, p. 779–784, http://doi.org/10.1130/G51235.1.

Tari, G., Connors, C., Flinch, J., Granath, J., Pace, P., Sobornov, K., and Soto, J. I., 2023, Negative structural inversion: an overview: Marine and Petroleum Geology, v. 152, no. 106223, 24 p., http://doi.org/10.1016/j.marpetgeo.2023.106223.

Walters, C. C., Gong, C., Sun, X., and Zhang, T., 2023, Geochemistry of oils and condensates from the lower Eagle Ford formation, South Texas. Part 3: basin modeling: Marine and Petroleum Geology, v. 150, no. 106117, 21 p., http://doi.org/10.1016/j.marpetgeo.2023.106117.

Wright, E., Landry, C. J., and Eichhubl, P., 2023, Occurrence and origin of nanoscale grain boundary channels under diagenetic conditions: Journal of Geophysical Research: Solid Earth, v. 128, no. 7, article no. e2023JB026961, 20 p., http://doi.org/10.1029/2023JB026961.

Yang, L., Fomel, S., Wang, S., Chen, X., and Chen, Y., 2023, Denoising distributed acoustic sensing data using unsupervised deep learning: Geophysics, v. 88, no. 4, p. V317–V332, http://doi.org/10.1190/geo2022-0460.1.

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., Fomel, S., Wang, S., Chen, X., Chen, Yunfeng, and Chen, Yangkang, 2023, SLKNet: an attention-based deep-learning framework for downhole distributed acoustic sensing data denoising: Geophysics, v. 88, no. 6, p. WC69–WC89, http://doi.org/10.1190/geo2022-0724.1.

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.

Yang, L., Wang, S., Chen, X., Chen, W., Saad, O. M., Zhou, X., Pham, N., Geng, Z., Fomel, S., and Chen, Y., 2023, High-fidelity permeability and porosity prediction using deep learning with the self-attention mechanism: IEEE Transactions on Neural Networks and Learning Systems, v. 34, no. 7, p. 3429–3443, http://doi.org/10.1109/TNNLS.2022.3157765.

Ye, J., Afifi, A., Rowaihy, F., Baby, G., De Santiago, A., Tasianas, A., Hamieh, A., Khodayeva, A., Al-Juaied, M., Meckel, T. A., and Hoteit, H., 2023, Evaluation of geological CO2 storage potential in Saudi Arabian sedimentary basins: Earth-Science Reviews, v. 244, no. 104539, 29 p., http://doi.org/10.1016/j.earscirev.2023.104539.

Young, M. H., and Wisian, K., 2023, Environmental consideration and impacts, in Beard, J. C., and Jones, B. A., eds., The future of geothermal energy in Texas: Austin, Tex., Mitchell Foundation & Educational Foundation of America, p. 264-282, http://doi.org/10.26153/tsw/44078.

Young, M. H., Buchanan, R. C., and Murray, K. E., 2023, Introduction, in Buchanan, R. C., Young, M. H., and Murray, K. E., eds., Recent seismicity in the Southern Mid-continent, USA: scientific, regulatory, and industry responses: Geological Society of America, Special Paper, v. 559, p. v-x, http://doi.org/10.1130/2023.2559(001).

Zeng, H., He, Y., Olariu, M., and Treviño, R., 2023, Machine learning-based inversion for acoustic impedance with large synthetic training data: workflow and data characterization: Geophysics, v. 88, no. 2, p. R193–R207, http://doi.org/10.1190/GEO2021-0726.1.

Zeng, H., Loucks, R. G., and Reed, R. M., 2023, Three-dimensional seismic architecture of an Upper Cretaceous volcanic complex and associated carbonate systems; Taylor Group, Elaine field, South Texas, USA: Marine and Petroleum Geology, v. 155, no. 106350, 20 p., http://doi.org/10.1016/j.marpetgeo.2023.106350.

Zhang, J., Moscardelli, L., Dooley, T. P., and Schuba, N., 2023, Halokinetic induced topographic controls on sediment routing in salt-bearing basins: a combined physical and numerical modeling approach: GSA Today, v. 33, no. 6, p. 4–9, http://doi.org/10.1130/GSATG561A.1.

Zhang, J., Zhao, X., Chen, Y., and Sun, H., 2023, Domain knowledge-guided data-driven prestack seismic inversion using deep learning: Geophysics, v. 88, no. 2, p. M31–M47, http://doi.org/10.1190/GEO2021-0560.1.