| Imputation of missing well log data by random forest and its uncertainty analysis R Feng, D Grana, N Balling Computers & Geosciences 152, 104763, 2021 | 97 | 2021 |
| An unsupervised deep-learning method for porosity estimation based on poststack seismic data R Feng, T Mejer Hansen, D Grana, N Balling Geophysics 85 (6), M97-M105, 2020 | 82 | 2020 |
| Bayesian convolutional neural networks for seismic facies classification R Feng, N Balling, D Grana, JS Dramsch, TM Hansen IEEE transactions on geoscience and remote sensing 59 (10), 8933-8940, 2021 | 71 | 2021 |
| Uncertainty quantification in fault detection using convolutional neural networks R Feng, D Grana, N Balling Geophysics 86 (3), M41-M48, 2021 | 59 | 2021 |
| Estimation of reservoir porosity based on seismic inversion results using deep learning methods R Feng Journal of Natural Gas Science and Engineering 77, 103270, 2020 | 56 | 2020 |
| Improving uncertainty analysis in well log classification by machine learning with a scaling algorithm R Feng Journal of Petroleum Science and Engineering 196, 107995, 2021 | 49 | 2021 |
| Lithofacies classification of a geothermal reservoir in Denmark and its facies-dependent porosity estimation from seismic inversion R Feng, N Balling, D Grana Geothermics 87, 101854, 2020 | 46 | 2020 |
| Reservoir lithology classification based on seismic inversion results by hidden Markov models: Applying prior geological information R Feng, SM Luthi, D Gisolf, E Angerer Marine and Petroleum Geology 93, 218-229, 2018 | 42 | 2018 |
| Reservoir lithology determination by hidden Markov random fields based on a Gaussian mixture model R Feng, SM Luthi, D Gisolf, E Angerer IEEE Transactions on Geoscience and Remote Sensing 56 (11), 6663-6673, 2018 | 39 | 2018 |
| Variational inference in Bayesian neural network for well-log prediction R Feng, D Grana, N Balling Geophysics 86 (3), M91-M99, 2021 | 32 | 2021 |
| Lithofacies classification based on a hybrid system of artificial neural networks and hidden Markov models R Feng Geophysical Journal International 221 (3), 1484-1498, 2020 | 26 | 2020 |
| Application of Bayesian generative adversarial networks to geological facies modeling R Feng, D Grana, T Mukerji, K Mosegaard Mathematical Geosciences 54 (5), 831-855, 2022 | 23 | 2022 |
| Uncertainty analysis in well log classification by Bayesian long short-term memory networks R Feng Journal of Petroleum Science and Engineering 205, 108816, 2021 | 23 | 2021 |
| Obtaining a high-resolution geological and petrophysical model from the results of reservoir-orientated elastic wave-equation-based seismic inversion R Feng, SM Luthi, D Gisolf, S Sharma Petroleum Geoscience 23 (3), 376-385, 2017 | 20 | 2017 |
| Unsupervised learning elastic rock properties from pre-stack seismic data R Feng Journal of Petroleum Science and Engineering 192, 107237, 2020 | 16 | 2020 |
| A Bayesian approach in machine learning for lithofacies classification and its uncertainty analysis R Feng IEEE Geoscience and Remote Sensing Letters 18 (1), 18-22, 2020 | 10 | 2020 |
| Simulating reservoir lithologies by an actively conditioned Markov chain model R Feng, SM Luthi, D Gisolf Journal of Geophysics and Engineering 15 (3), 800-815, 2018 | 9 | 2018 |
| Stochastic facies inversion with prior sampling by conditional generative adversarial networks based on training image R Feng, K Mosegaard, D Grana, T Mukerji, TM Hansen Mathematical Geosciences 56 (4), 665-690, 2024 | 8 | 2024 |
| An outcrop-based detailed geological model to test automated interpretation of seismic inversion results R Feng, S Sharma, SM Luthi, A Gisolf 77th EAGE Conference and Exhibition 2015 2015 (1), cp-451-00628, 2015 | 7 | 2015 |
| Interpretations of gravity and magnetic anomalies in the Songliao Basin with Wavelet Multi-scale Decomposition C Li, L Wang, B Sun, R Feng, Y Wu Frontiers of Earth Science 9, 427-436, 2015 | 6 | 2015 |
Dario GranaUniversity of WyomingVerified email at uwyo.edu
