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المؤلفون: Dongfang Qu, Klaus Mosegaard, Runhai Feng, Lars Nielsen
المصدر: Interpretation. 11:T339-T347
مصطلحات موضوعية: Geophysics, Geology
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::aae7bf882d20b4dbfe00254be7f49492
https://doi.org/10.1190/int-2022-0059.1 -
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المؤلفون: Runhai Feng, Dario Grana, Tapan Mukerji, Klaus Mosegaard
المصدر: Mathematical Geosciences. 54:831-855
مصطلحات موضوعية: Mathematics (miscellaneous), General Earth and Planetary Sciences
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المؤلفون: Dongfang Qu, Klaus Mosegaard, Runhai Feng, Lars Nielsen
المصدر: Qu, D, Mosegaard, K, Feng, R & Nielsen, L 2023, ' Using Synthetic Data Trained Convolutional Neural Network for Predicting Sub-resolution Thin Layers from Seismic Data ', Interpretation . https://doi.org/10.31223/X5QD2T
وصف الملف: application/pdf
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b0c6f998da5aea584cf7646d752d049e
https://curis.ku.dk/ws/files/336138858/int_2022_0059r1_proof_hi.pdf -
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المصدر: Feng, R, Balling, N, Grana, D, Dramsch, J S & Hansen, T M 2021, ' Bayesian Convolutional Neural Networks for Seismic Facies Classification ', IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 10, pp. 8933-8940 . https://doi.org/10.1109/TGRS.2020.3049012
مصطلحات موضوعية: Rocks, uncertainty quantification, Computer science, Bayesian probability, Posterior probability, Initialization, Convolutional neural network, Physics::Geophysics, Bayesian convolutional neural networks, Training, Electrical and Electronic Engineering, Training data, seismic facies classification, Artificial neural network, business.industry, Deep learning, Uncertainty, Monte Carlo methods, Pattern recognition, variational approach, Bayes methods, Backpropagation, Facies, General Earth and Planetary Sciences, Artificial intelligence, business
وصف الملف: application/pdf
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c20d7eab341a81b9802188dbe53f954
https://doi.org/10.1109/tgrs.2020.3049012
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المؤلفون: Runhai Feng, Niels Balling, Dario Grana
المصدر: Feng, R, Grana, D & Balling, N 2021, ' Variational inference in Bayesian neural network for well-log prediction ', Geophysics, vol. 86, no. 3, pp. M91-M99 . https://doi.org/10.1190/geo2020-0609.1
مصطلحات موضوعية: 010504 meteorology & atmospheric sciences, business.industry, Computer science, Inference, 010502 geochemistry & geophysics, Bayesian neural networks, Machine learning, computer.software_genre, 01 natural sciences, machine learning, Geophysics, Geochemistry and Petrology, Artificial intelligence, business, computer, well-log interpretation, 0105 earth and related environmental sciences
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bbfe7c8a5de35c7289fc599aaa393ac
https://doi.org/10.1190/geo2020-0609.1 -
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المؤلفون: Runhai Feng
المصدر: Feng, R 2021, ' A Bayesian Approach in Machine Learning for Lithofacies Classification and Its Uncertainty Analysis ', IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 1, pp. 18-22 . https://doi.org/10.1109/LGRS.2020.2968356
مصطلحات موضوعية: Computer Science::Neural and Evolutionary Computation, Posterior probability, Bayesian probability, 0211 other engineering and technologies, Markov process, 02 engineering and technology, Lithofacies classification, Machine learning, computer.software_genre, symbols.namesake, Electrical and Electronic Engineering, uncertainty analysis, Uncertainty analysis, 021101 geological & geomatics engineering, Artificial neural network, business.industry, Process (computing), Stochastic matrix, Markov matrix, Potential method, Geotechnical Engineering and Engineering Geology, machine learning, symbols, Artificial intelligence, business, computer
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::261ef68be43ca16716322f54e57f7f1e
https://doi.org/10.1109/lgrs.2020.2968356
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المؤلفون: Runhai Feng, Dario Grana, Niels Balling, Thomas Mejer Hansen
المصدر: Feng, R, Hansen, T M, Grana, D & Balling, N 2020, ' An unsupervised deep-learning method for porosity estimation based on poststack seismic data ', Geophysics, vol. 85, no. 6, pp. M97-M105 . https://doi.org/10.1190/GEO2020-0121.1
مصطلحات موضوعية: business.industry, 020209 energy, Deep learning, 02 engineering and technology, ROCK-PHYSICS, 010502 geochemistry & geophysics, computer.software_genre, 01 natural sciences, Geophysics, Geochemistry and Petrology, 0202 electrical engineering, electronic engineering, information engineering, Reservoir modeling, Artificial intelligence, Data mining, business, Porosity, computer, Geology, 0105 earth and related environmental sciences
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c1aa107a369dfce658464532e8dd60f
https://doi.org/10.1190/geo2020-0121.1 -
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المؤلفون: Niels Balling, Runhai Feng, Dario Grana
المصدر: Feng, R, Grana, D & Balling, N 2021, ' Imputation of missing well log data by random forest and its uncertainty analysis ', Computers and Geosciences, vol. 152, 104763 . https://doi.org/10.1016/j.cageo.2021.104763
مصطلحات موضوعية: Correlation coefficient, 0208 environmental biotechnology, Well logging, Log imputation, Prediction interval, 02 engineering and technology, 010502 geochemistry & geophysics, 01 natural sciences, Feature importance, 020801 environmental engineering, Random forest, Multicollinearity, Principal component analysis, Statistics, Imputation (statistics), Computers in Earth Sciences, Uncertainty analysis, 0105 earth and related environmental sciences, Information Systems, Mathematics
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aaa0e627141c4f54e6209ac2d491f337
https://pure.au.dk/portal/da/publications/imputation-of-missing-well-log-data-by-random-forest-and-its-uncertainty-analysis(a714de0f-b5ff-4c8e-b319-d0da5f272ffb).html -
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