TeX 59%
Python 40.8%
1{"status": "ok", "message-type": "work-list", "message-version": "1.0.0", "message": {"facets": {}, "total-results": 772162, "items": [{"indexed": {"date-parts": [[2025, 5, 14]], "date-time": "2025-05-14T12:57:28Z", "timestamp": 1747227448582, "version": "3.40.5"}, "posted": {"date-parts": [[2021, 3, 4]]}, "group-title": "pico", "reference-count": 0, "publisher": "Copernicus GmbH", "content-domain": {"domain": [], "crossmark-restriction": false}, "abstract": "<jats:p>&lt;p&gt;Deep Learning (DL) is becoming an increasingly important tool to produce accurate streamflow prediction across a wide range of spatial and temporal scales. However, classical DL networks do not incorporate uncertainty information but only return a point prediction. Monte-Carlo Dropout (MC-Dropout) approach offers a mathematically grounded framework to reason about DL uncertainty which was used here as random diagonal matrices to introduce randomness to the streamflow prediction process. This study employed Recurrent Neural Networks (RNNs) to simulate daily streamflow records across a coastal plain drainage system, i.e., the Northeast Cape Fear River Basin, North Carolina, USA. We employed MC-Dropout approach with the DL algorithm to make streamflow simulation more robust to potential overfitting by introducing random perturbation during training period. Daily streamflow was calibrated during 2000-2010 and validated during 2010-2014 periods. Our results provide a unique and strong evidence that variational sampling via MC-Dropout acts as a dissimilarity detector. The MC-Dropout method successfully captured the predictive error after tuning a hyperparameter on a representative training dataset. This approach was able to mitigate the problem of representing model uncertainty in DL simulations without sacrificing computational complexity or accuracy metrics and can be used for all kind of DL-based streamflow (time-series) model training with dropout.&lt;/p&gt;</jats:p>", "DOI": "10.5194/egusphere-egu21-9145", "type": "posted-content", "created": {"date-parts": [[2021, 3, 4]], "date-time": "2021-03-04T05:25:27Z", "timestamp": 1614835527000}, "source": "Crossref", "is-referenced-by-count": 0, "title": ["Model Uncertainty in Deep Learning Simulation of Daily Streamflow with Monte Carlo Dropout"], "prefix": "10.5194", "author": [{"ORCID": "https://orcid.org/0000-0001-9157-3397", "authenticated-orcid": false, "given": "Sadegh", "family": "Sadeghi Tabas", "sequence": "first", "affiliation": [], "role": [{"role": "author", "vocabulary": "crossref"}]}, {"given": "Vidya", "family": "Samadi", "sequence": "additional", "affiliation": [], "role": [{"role": "author", "vocabulary": "crossref"}]}], "member": "3145", "deposited": {"date-parts": [[2021, 3, 4]], "date-time": "2021-03-04T05:28:35Z", "timestamp": 1614835715000}, "score": 35.45014, "resource": {"primary": {"URL": "https://meetingorganizer.copernicus.org/EGU21/EGU21-9145.html"}}, "issued": {"date-parts": [[2021, 3, 4]]}, "references-count": 0, "URL": "https://doi.org/10.5194/egusphere-egu21-9145", "published": {"date-parts": [[2021, 3, 4]]}, "subtype": "other"}, {"indexed": {"date-parts": [[2026, 10, 13]], "date-time": "2026-10-13T18:16:58Z", "timestamp": 1791915418196, "version": "4.3.4"}, "reference-count": 20, "publisher": "Walter de Gruyter GmbH", "issue": "4", "license": [{"start": {"date-parts": [[2020, 12, 1]], "date-time": "2020-12-01T00:00:00Z", "timestamp": 1606780800000}, "content-version": "unspecified", "delay-in-days": 0, "URL": "http://creativecommons.org/licenses/by/4.0"}], "content-domain": {"domain": [], "crossmark-restriction": false}, "published-print": {"date-parts": [[2020, 12, 1]]}, "abstract": "<jats:title>Abstract</jats:title>\n <jats:p>Although deep learning-based valuation models are spreading throughout the real estate industry following the artificial intelligence boom, property owners and investors continue to doubt the accuracy of the results. In this study, we specify a neural network for predicting house prices. We suggest a standard feed-forward network with two hidden layers, and show that it is sufficiently reasonable to apply its prediction to real-world projects such as property valuation. In addition, we propose a Bayesian neural network for describing uncertainty in house price predictions while providing a means to quantify uncertainty for each prediction. We choose Gangnam-gu, Seoul for the analysis, and predict house prices in the area using both networks. Although the Bayesian neural network did not perform better than the conventional network, it could provide a tool to measure the uncertainty inherent in predicted prices. The findings of this study show that a Bayesian approach can model uncertainty in property valuation, thereby promoting the adoption of deep learning tools in the real estate industry.</jats:p>", "DOI": "10.1515/remav-2020-0028", "type": "journal-article", "created": {"date-parts": [[2020, 12, 22]], "date-time": "2020-12-22T12:27:47Z", "timestamp": 1608640067000}, "page": "15-23", "source": "Crossref", "is-referenced-by-count": 16, "title": ["Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach"], "prefix": "10.1515", "volume": "28", "author": [{"given": "Changro", "family": "Lee", "sequence": "first", "affiliation": [{"name": "Department of Real Estate , Kangwon National University"}], "role": [{"vocabulary": "crossref", "role": "author"}]}, {"given": "Keith Key-Ho", "family": "Park", "sequence": "additional", "affiliation": [{"name": "Department of Geography , Seoul National University, Institute for Korean Regional Studies"}], "role": [{"vocabulary": "crossref", "role": "author"}]}], "member": "374", "published-online": {"date-parts": [[2020, 12, 12]]}, "reference": [{"key": "2021040703125191789_j_remav-2020-0028_ref_001_w2aab3b7b4b1b6b1ab1ab1Aa", "unstructured": "Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015). Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424."}, {"key": "2021040703125191789_j_remav-2020-0028_ref_002_w2aab3b7b4b1b6b1ab1ab2Aa", "unstructured": "Conway, J. (2018). Artificial intelligence and machine learning: current applications in real estate. Master’s Thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_003_w2aab3b7b4b1b6b1ab1ab3Aa", "doi-asserted-by": "crossref", "unstructured": "French, N. (2001). Uncertainty in property valuation: The pricing of flexible leases. Journal of Corporate Real Estate, 3(1), 17–27. https://doi.org/10.1108/14630010110811454", "DOI": "10.1108/14630010110811454"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_004_w2aab3b7b4b1b6b1ab1ab4Aa", "doi-asserted-by": "crossref", "unstructured": "French, N., & Gabrielli, L. (2004). The uncertainty of valuation. Journal of Property Investment & Finance, 22(6), 484–500. https://doi.org/10.1108/14635780410569470", "DOI": "10.1108/14635780410569470"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_005_w2aab3b7b4b1b6b1ab1ab5Aa", "unstructured": "Gal, Y., & Ghahramani, Z. (2016). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning (pp. 1050-1059)."}, {"key": "2021040703125191789_j_remav-2020-0028_ref_006_w2aab3b7b4b1b6b1ab1ab6Aa", "doi-asserted-by": "crossref", "unstructured": "Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459. https://doi.org/10.1038/nature14541 PMID:26017444", "DOI": "10.1038/nature14541"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_007_w2aab3b7b4b1b6b1ab1ab7Aa", "unstructured": "Graves, A. (2011). Practical variational inference for neural networks. In Advances in neural information processing systems, 2348-2356."}, {"key": "2021040703125191789_j_remav-2020-0028_ref_008_w2aab3b7b4b1b6b1ab1ab8Aa", "unstructured": "Hasenclever, L., Webb, S., Lienart, T., Vollmer, S., Lakshminarayanan, B., Blundell, C., & Teh, Y. W. (2017). Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server. Journal of Machine Learning Research, 18(1), 3744–3780."}, {"key": "2021040703125191789_j_remav-2020-0028_ref_009_w2aab3b7b4b1b6b1ab1ab9Aa", "unstructured": "Harper, R., & Southern, J. (2019). A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat. arXiv preprint arXiv:1902.03043."}, {"key": "2021040703125191789_j_remav-2020-0028_ref_010_w2aab3b7b4b1b6b1ab1ac10Aa", "doi-asserted-by": "crossref", "unstructured": "Jang, H., & Lee, J. (2019). Generative Bayesian neural network model for risk-neutral pricing of American index options. Quantitative Finance, 19(4), 587–603. https://doi.org/10.1080/14697688.2018.1490807", "DOI": "10.1080/14697688.2018.1490807"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_011_w2aab3b7b4b1b6b1ab1ac11Aa", "doi-asserted-by": "crossref", "unstructured": "Johnson, A. E., Ghassemi, M. M., Nemati, S., Niehaus, K. E., Clifton, D. A., & Clifford, G. D. (2016). Machine learning and decision support in critical care. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 104(2), 444. https://doi.org/10.1109/JPROC.2015.2501978", "DOI": "10.1109/JPROC.2015.2501978"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_012_w2aab3b7b4b1b6b1ab1ac12Aa", "doi-asserted-by": "crossref", "unstructured": "Joslin, A. (2005). An investigation into the expression of uncertainty in property valuations. Journal of Property Investment & Finance, 23(3), 269–285. https://doi.org/10.1108/14635780510599476", "DOI": "10.1108/14635780510599476"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_013_w2aab3b7b4b1b6b1ab1ac13Aa", "unstructured": "Kendall, A., & Gal, Y. (2017). What uncertainties do we need in bayesian deep learning for computer vision? In Advances in neural information processing systems, 5574-5584."}, {"key": "2021040703125191789_j_remav-2020-0028_ref_014_w2aab3b7b4b1b6b1ab1ac14Aa", "doi-asserted-by": "crossref", "unstructured": "Kucharska-Stasiak, E. (2013). Uncertainty of property valuation as a subject of academic research. Real Estate Management and Valuation, 21(4), 17–25. https://doi.org/10.2478/remav-2013-0033", "DOI": "10.2478/remav-2013-0033"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_015_w2aab3b7b4b1b6b1ab1ac15Aa", "doi-asserted-by": "crossref", "unstructured": "Mallinson, M., & French, N. (2000). Uncertainty in property valuation–The nature and relevance of uncertainty and how it might be measured and reported. Journal of Property Investment & Finance, 18(1), 13–32. https://doi.org/10.1108/14635780010316636", "DOI": "10.1108/14635780010316636"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_016_w2aab3b7b4b1b6b1ab1ac16Aa", "doi-asserted-by": "crossref", "unstructured": "Meszek, W. (2007). Uncertainty phenomenon in property valuation. International Journal of Management and Decision Making, 8(5/6), 575–585. https://doi.org/10.1504/IJMDM.2007.013419", "DOI": "10.1504/IJMDM.2007.013419"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_017_w2aab3b7b4b1b6b1ab1ac17Aa", "unstructured": "Neal, R. M. (2012). Bayesian learning for neural networks (Vol. 118). Springer Science & Business Media."}, {"key": "2021040703125191789_j_remav-2020-0028_ref_018_w2aab3b7b4b1b6b1ab1ac18Aa", "unstructured": "Seoul. (2017). Seoul Statistics. Department of housing policy."}, {"key": "2021040703125191789_j_remav-2020-0028_ref_019_w2aab3b7b4b1b6b1ab1ac19Aa", "doi-asserted-by": "crossref", "unstructured": "Wang, Y., & Blei, D. M. (2019). Frequentist consistency of variational Bayes. Journal of the American Statistical Association, 114(527), 1147–1161. https://doi.org/10.1080/01621459.2018.1473776", "DOI": "10.1080/01621459.2018.1473776"}, {"key": "2021040703125191789_j_remav-2020-0028_ref_020_w2aab3b7b4b1b6b1ab1ac20Aa", "unstructured": "Welling, M., & Teh, Y. W. (2011). Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11) (pp. 681-688)."}], "container-title": ["Real Estate Management and Valuation"], "language": "en", "link": [{"URL": "https://content.sciendo.com/view/journals/remav/28/4/article-p15.xml", "content-type": "text/html", "content-version": "vor", "intended-application": "text-mining"}, {"URL": "https://www.sciendo.com/article/10.1515/remav-2020-0028", "content-type": "unspecified", "content-version": "vor", "intended-application": "similarity-checking"}], "deposited": {"date-parts": [[2021, 4, 7]], "date-time": "2021-04-07T06:17:50Z", "timestamp": 1617776270000}, "score": 33.45593, "resource": {"primary": {"URL": "https://www.sciendo.com/article/10.1515/remav-2020-0028"}}, "issued": {"date-parts": [[2020, 12, 1]]}, "references-count": 20, "journal-issue": {"issue": "4", "published-online": {"date-parts": [[2020, 12, 12]]}, "published-print": {"date-parts": [[2020, 12, 1]]}}, "alternative-id": ["10.1515/remav-2020-0028"], "URL": "https://doi.org/10.1515/remav-2020-0028", "ISSN": ["2300-5289"], "issn-type": [{"value": "2300-5289", "type": "electronic"}], "published": {"date-parts": [[2020, 12, 1]]}}, {"indexed": {"date-parts": [[2026, 9, 25]], "date-time": "2026-09-25T23:41:38Z", "timestamp": 1790379698956, "version": "4.1.0"}, "reference-count": 0, "publisher": "SCITEPRESS - Science and Technology Publications", "content-domain": {"domain": [], "crossmark-restriction": false}, "published-print": {"date-parts": [[2026]]}, "DOI": "10.5220/0014245800004061", "type": "proceedings-article", "created": {"date-parts": [[2026, 3, 8]], "date-time": "2026-03-08T07:34:39Z", "timestamp": 1772955279000}, "page": "505-514", "source": "Crossref", "is-referenced-by-count": 1, "title": ["Uncertainty-Aware Reinforcement Learning for Zero Trust: An Empirical Evaluation of PPO, MC Dropout, and Bayesian Neural Networks"], "prefix": "10.5220", "author": [{"given": "Vikram", "family": "Kalekar", "sequence": "first", "affiliation": [{"name": "Independent Researcher, Austin, Texas, U.S.A."}], "role": [{"vocabulary": "crossref", "role": "author"}]}], "member": "3171", "event": {"name": "12th International Conference on Information Systems Security and Privacy", "location": "Marbella, Spain", "start": {"date-parts": [[2026, 3, 4]]}, "end": {"date-parts": [[2026, 3, 6]]}}, "container-title": ["Proceedings of the 12th International Conference on Information Systems Security and Privacy"], "original-title": ["Uncertainty-Aware Reinforcement Learning for Zero Trust: An Empirical Evaluation of PPO, MC Dropout, and Bayesian Neural Networks"], "deposited": {"date-parts": [[2026, 3, 18]], "date-time": "2026-03-18T05:03:00Z", "timestamp": 1773810180000}, "score": 32.70189, "resource": {"primary": {"URL": "https://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0014245800004061"}}, "subtitle": [""], "issued": {"date-parts": [[2026]]}, "references-count": 0, "URL": "https://doi.org/10.5220/0014245800004061", "published": {"date-parts": [[2026]]}}, {"indexed": {"date-parts": [[2026, 9, 26]], "date-time": "2026-09-26T04:31:44Z", "timestamp": 1790397104787, "version": "4.1.0"}, "reference-count": 46, "publisher": "Elsevier BV", "license": [{"start": {"date-parts": [[2022, 1, 1]], "date-time": "2022-01-01T00:00:00Z", "timestamp": 1640995200000}, "content-version": "tdm", "delay-in-days": 0, "URL": "https://www.elsevier.com/tdm/userlicense/1.0/"}, {"start": {"date-parts": [[2022, 1, 1]], "date-time": "2022-01-01T00:00:00Z", "timestamp": 1640995200000}, "content-version": "tdm", "delay-in-days": 0, "URL": "https://www.elsevier.com/legal/tdmrep-license"}, {"start": {"date-parts": [[2022, 1, 1]], "date-time": "2022-01-01T00:00:00Z", "timestamp": 1640995200000}, "content-version": "stm-asf", "delay-in-days": 0, "URL": "https://doi.org/10.15223/policy-017"}, {"start": {"date-parts": [[2022, 1, 1]], "date-time": "2022-01-01T00:00:00Z", "timestamp": 1640995200000}, "content-version": "stm-asf", "delay-in-days": 0, "URL": "https://doi.org/10.15223/policy-037"}, {"start": {"date-parts": [[2022, 1, 1]], "date-time": "2022-01-01T00:00:00Z", "timestamp": 1640995200000}, "content-version": "stm-asf", "delay-in-days": 0, "URL": "https://doi.org/10.15223/policy-012"}, {"start": {"date-parts": [[2022, 1, 1]], "date-time": "2022-01-01T00:00:00Z", "timestamp": 1640995200000}, "content-version": "stm-asf", "delay-in-days": 0, "URL": "https://doi.org/10.15223/policy-029"}, {"start": {"date-parts": [[2022, 1, 1]], "date-time": "2022-01-01T00:00:00Z", "timestamp": 1640995200000}, "content-version": "stm-asf", "delay-in-days": 0, "URL": "https://doi.org/10.15223/policy-004"}], "funder": [{"DOI": "10.13039/501100002380", "name": "Hanyang University", "doi-asserted-by": "publisher", "id": [{"id": "10.13039/501100002380", "id-type": "DOI", "asserted-by": "publisher"}]}, {"DOI": "10.13039/501100003052", "name": "Ministry of Trade, Industry and Energy", "doi-asserted-by": "publisher", "id": [{"id": "10.13039/501100003052", "id-type": "DOI", "asserted-by": "publisher"}]}, {"DOI": "10.13039/501100007053", "name": "Korea Institute of Energy Technology Evaluation and Planning", "doi-asserted-by": "publisher", "award": ["20182510102470"], "award-info": [{"award-number": ["20182510102470"]}], "id": [{"id": "10.13039/501100007053", "id-type": "DOI", "asserted-by": "publisher"}]}, {"DOI": "10.13039/501100007053", "name": "Korea Institute of Energy Technology Evaluation and Planning", "doi-asserted-by": "publisher", "award": ["20194010201920"], "award-info": [{"award-number": ["20194010201920"]}], "id": [{"id": "10.13039/501100007053", "id-type": "DOI", "asserted-by": "publisher"}]}], "content-domain": {"domain": ["elsevier.com", "sciencedirect.com"], "crossmark-restriction": true}, "short-container-title": ["Journal of Petroleum Science and Engineering"], "published-print": {"date-parts": [[2022, 1]]}, "DOI": "10.1016/j.petrol.2021.109288", "type": "journal-article", "created": {"date-parts": [[2021, 7, 31]], "date-time": "2021-07-31T11:13:25Z", "timestamp": 1627730005000}, "page": "109288", "update-policy": "https://doi.org/10.1016/elsevier_cm_policy", "source": "Crossref", "is-referenced-by-count": 29, "special_numbering": "PA", "title": ["Uncertainty estimation in AVO inversion using Bayesian dropout based deep learning"], "prefix": "10.1016", "volume": "208", "author": [{"given": "Choi", "family": "Junhwan", "sequence": "first", "affiliation": [], "role": [{"vocabulary": "crossref", "role": "author"}]}, {"ORCID": "https://orcid.org/0000-0001-8200-4096", "authenticated-orcid": false, "given": "Oh", "family": "Seokmin", "sequence": "additional", "affiliation": [], "role": [{"vocabulary": "crossref", "role": "author"}]}, {"given": "Byun", "family": "Joongmoo", "sequence": "additional", "affiliation": [], "role": [{"vocabulary": "crossref", "role": "author"}]}], "member": "78", "reference": [{"key": "10.1016/j.petrol.2021.109288_bib1", "series-title": "Quantitative Seismology: Theory and Methods", "author": "Aki", "year": "1980"}, {"key": "10.1016/j.petrol.2021.109288_bib2", "series-title": "Petrophysical property estimation from seismic data using recurrent neural networks, 88th Ann", "first-page": "2141", "author": "Alfarraj", "year": "2018"}, {"key": "10.1016/j.petrol.2021.109288_bib3", "series-title": "Semi-supervised Learning for Acoustic Impedance Inversion, 89th Ann", "first-page": "2298", "author": "Alfarraj", "year": "2019"}, {"key": "10.1016/j.petrol.2021.109288_bib4", "first-page": "215", "article-title": "Ensemble learning in Bayesian neural networks", "volume": "168", "author": "Barber", "year": "1998", "journal-title": "Nato ASI Series F Computer and Systems Sciences"}, {"key": "10.1016/j.petrol.2021.109288_bib5", "doi-asserted-by": "crossref", "first-page": "1005", "DOI": "10.1016/j.petrol.2018.06.075", "article-title": "Applications of machine learning for facies and fracture prediction using Bayesian Network Theory and Random Forest: case studies from the Appalachian basin, USA", "volume": "170", "author": "Bhattacharya", "year": "2018", "journal-title": "J. Petrol. Sci. Eng."}, {"issue": "3", "key": "10.1016/j.petrol.2021.109288_bib6", "doi-asserted-by": "crossref", "first-page": "SE161", "DOI": "10.1190/INT-2018-0236.1", "article-title": "Prestack and poststack inversion using a physics-guided convolutional neural network", "volume": "7", "author": "Biswas", "year": "2019", "journal-title": "Interpretation"}, {"key": "10.1016/j.petrol.2021.109288_bib7", "doi-asserted-by": "crossref", "first-page": "108361", "DOI": "10.1016/j.petrol.2021.108361", "article-title": "Bayesian deep networks for absolute permeability and porosity uncertainty prediction from image borehole logs from brazilian carbonate reservoirs", "volume": "201", "author": "Bom", "year": "2021", "journal-title": "J. Petrol. Sci. Eng."}, {"key": "10.1016/j.petrol.2021.109288_bib8", "doi-asserted-by": "crossref", "first-page": "185", "DOI": "10.1190/1.1543206", "article-title": "Bayesian linearized AVO inversion", "volume": "68", "author": "Buland", "year": "2003", "journal-title": "Geophysics"}, {"key": "10.1016/j.petrol.2021.109288_bib9", "doi-asserted-by": "crossref", "first-page": "106161", "DOI": "10.1016/j.petrol.2019.06.025", "article-title": "Petrofacies characterization using best combination of multiple elastic properties", "volume": "181", "author": "Choi", "year": "2019", "journal-title": "J. Petrol. Sci. Eng."}, {"key": "10.1016/j.petrol.2021.109288_bib10", "series-title": "Convolutional Neural Network for Seismic Impedance Inversion, 88th Ann. Internat. Mtg", "first-page": "2071", "author": "Das", "year": "2018"}, {"key": "10.1016/j.petrol.2021.109288_bib11", "first-page": "853", "article-title": "Transforming neural-net output levels to probability distributions", "author": "Denker", "year": "1991", "journal-title": "Adv. Neural Inf. Process. Syst."}, {"key": "10.1016/j.petrol.2021.109288_bib12", "doi-asserted-by": "crossref", "first-page": "107382", "DOI": "10.1016/j.petrol.2020.107382", "article-title": "Bernstein copula-based spatial cosimulation for petrophysical property prediction conditioned to elastic attributes", "volume": "193", "author": "Díaz-Viera", "year": "2020", "journal-title": "J. Petrol. Sci. Eng."}, {"issue": "9", "key": "10.1016/j.petrol.2021.109288_bib13", "doi-asserted-by": "crossref", "first-page": "1362", "DOI": "10.1190/1.1443695", "article-title": "Detection of gas in sandstone reservoirs using AVO analysis: a 3-D seismic case history using the Geostack technique", "volume": "59", "author": "Fatti", "year": "1994", "journal-title": "Geophysics"}, {"key": "10.1016/j.petrol.2021.109288_bib47", "doi-asserted-by": "crossref", "first-page": "107237", "DOI": "10.1016/j.petrol.2020.107237", "article-title": "Unsupervised learning elastic rock properties from pre-stack seismic data", "volume": "192", "author": "Feng", "year": "2020", "journal-title": "J. Petrol. Sci. Eng."}, {"key": "10.1016/j.petrol.2021.109288_bib14", "doi-asserted-by": "crossref", "DOI": "10.1016/j.petrol.2021.108816", "article-title": "Uncertainty analysis in well log classification by bayesian long short-term memory networks", "author": "Feng", "year": "2021", "journal-title": "J. Petrol. Sci. Eng."}, {"issue": "3", "key": "10.1016/j.petrol.2021.109288_bib15", "doi-asserted-by": "crossref", "first-page": "1", "DOI": "10.1190/geo2020-0609.1", "article-title": "Variational inference in Bayesian neural network for well log prediction", "volume": "86", "author": "Feng", "year": "2021", "journal-title": "Geophysics"}, {"issue": "3", "key": "10.1016/j.petrol.2021.109288_bib16", "doi-asserted-by": "crossref", "first-page": "M41", "DOI": "10.1190/geo2020-0424.1", "article-title": "Uncertainty quantification in fault detection using convolutional neural networks", "volume": "86", "author": "Feng", "year": "2021", "journal-title": "Geophysics"}, {"key": "10.1016/j.petrol.2021.109288_bib17", "series-title": "Uncertainty in Deep Learning: PhD Thesis", "author": "Gal", "year": "2016"}, {"key": "10.1016/j.petrol.2021.109288_bib18", "first-page": "1050", "article-title": "Dropout as a bayesian approximation: representing model uncertainty in deep learning", "author": "Gal", "year": "2016", "journal-title": "International conference on machine learning"}, {"key": "10.1016/j.petrol.2021.109288_bib19", "series-title": "76th Ann. Internat. Mtg.", "first-page": "1801", "article-title": "Facies probability from mixture distributions with nonstationary impedance errors", "author": "Gallop", "year": "2006"}, {"issue": "3", "key": "10.1016/j.petrol.2021.109288_bib21", "doi-asserted-by": "crossref", "first-page": "O21", "DOI": "10.1190/1.3386676", "article-title": "Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion", "volume": "75", "author": "Grana", "year": "2010", "journal-title": "Geophysics"}, {"key": "10.1016/j.petrol.2021.109288_bib22", "doi-asserted-by": "crossref", "first-page": "1", "DOI": "10.1007/s11004-016-9671-9", "article-title": "Bayesian Gaussian mixture linear inversion for geophysical inverse problems", "volume": "49", "author": "Grana", "year": "2017", "journal-title": "Math. Geosci."}, {"key": "10.1016/j.petrol.2021.109288_bib23", "first-page": "2348", "article-title": "Practical variational inference for neural networks", "author": "Graves", "year": "2011", "journal-title": "Adv. Neural Inf. Process. Syst."}, {"key": "10.1016/j.petrol.2021.109288_bib24", "doi-asserted-by": "crossref", "first-page": "220", "DOI": "10.1190/1.1444899", "article-title": "Use of multi-attribute transforms to predict log properties from seismic data", "volume": "66", "author": "Hampson", "year": "2001", "journal-title": "Geophysics"}, {"key": "10.1016/j.petrol.2021.109288_bib25", "doi-asserted-by": "crossref", "DOI": "10.1145/168304.168306", "article-title": "Keeping the neural networks simple by minimizing the description length of the weights", "volume": "5–13", "author": "Hinton", "year": "1993", "journal-title": "Proceedings of the Sixth Annual Conference on Computational Learning Theory"}, {"key": "10.1016/j.petrol.2021.109288_bib26", "series-title": "Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction", "author": "Hüllermeier", "year": "2019"}, {"key": "10.1016/j.petrol.2021.109288_bib27", "first-page": "5574", "article-title": "What uncertainties do we need in bayesian deep learning for computer vision?", "author": "Kendall", "year": "2017", "journal-title": "Adv. Neural Inf. Process. Syst."}, {"key": "10.1016/j.petrol.2021.109288_bib28", "series-title": "Information Theory and Statistics", "author": "Kullback", "year": "1959"}, {"key": "10.1016/j.petrol.2021.109288_bib29", "doi-asserted-by": "crossref", "first-page": "107412", "DOI": "10.1016/j.petrol.2020.107412", "article-title": "Direct estimation of discrete fluid facies and fluid indicators via a Bayesian Seismic Probabilistic Inversion and a novel exact PP-wave reflection coefficient", "volume": "196", "author": "Li", "year": "2021", "journal-title": "J. Petrol. Sci. Eng."}, {"key": "10.1016/j.petrol.2021.109288_bib30", "doi-asserted-by": "crossref", "first-page": "233", "DOI": "10.1071/AJ14018", "article-title": "Technologies that have transformed the Exmouth into Australia's premier oil producing basin", "volume": "55", "author": "Loro", "year": "2015", "journal-title": "The APPEA Journal"}, {"key": "10.1016/j.petrol.2021.109288_bib31", "doi-asserted-by": "crossref", "first-page": "252", "DOI": "10.1016/j.petrol.2017.04.026", "article-title": "Uncertainty assessment of volumes of investigation to enhance the vertical resolution of well-logs", "volume": "154", "author": "Masoudi", "year": "2017", "journal-title": "J. Petrol. Sci. Eng."}, {"issue": "2", "key": "10.1016/j.petrol.2021.109288_bib32", "doi-asserted-by": "crossref", "first-page": "T455", "DOI": "10.1190/INT-2018-0153.1", "article-title": "High-resolution water-saturation prediction using geostatistical inversion and neural network methods", "volume": "7", "author": "Mohamed", "year": "2019", "journal-title": "Interpretation"}, {"key": "10.1016/j.petrol.2021.109288_bib33", "article-title": "Probabilistic seismic interpretation using bayesian neural networks", "volume": "1–5", "author": "Mosser", "year": "2019", "journal-title": "81st EAGE Conference and Exhibition"}, {"key": "10.1016/j.petrol.2021.109288_bib34", "series-title": "89th Ann. Internat. Mtg.", "first-page": "2488", "article-title": "Bayesian deep learning for seismic facies classification and its uncertainty estimation", "author": "Mukhopadhyay", "year": "2019"}, {"key": "10.1016/j.petrol.2021.109288_bib35", "series-title": "90th Ann. Internat. Mtg.", "first-page": "3462", "article-title": "Uncertainty estimation using Bayesian convolutional neural network for automatic channel detection", "author": "Pham", "year": "2020"}, {"issue": "3", "key": "10.1016/j.petrol.2021.109288_bib36", "doi-asserted-by": "crossref", "first-page": "SE215", "DOI": "10.1190/INT-2018-0234.1", "article-title": "A Boltzmann machine for high-resolution prestack seismic inversion", "volume": "7", "author": "Phan", "year": "2019", "journal-title": "Interpretation"}, {"key": "10.1016/j.petrol.2021.109288_bib37", "doi-asserted-by": "crossref", "first-page": "43", "DOI": "10.1016/j.spasta.2014.08.001", "article-title": "Skew-Gaussian random fields", "volume": "10", "author": "Rimstad", "year": "2014", "journal-title": "Spatial Statistics"}, {"key": "10.1016/j.petrol.2021.109288_bib38", "doi-asserted-by": "crossref", "first-page": "6753", "DOI": "10.1029/93JB01563", "article-title": "Neural networks and inversion of seismic data", "volume": "99", "author": "Röth", "year": "1994", "journal-title": "J. Geophys. Res.: Solid Earth"}, {"issue": "3", "key": "10.1016/j.petrol.2021.109288_bib39", "doi-asserted-by": "crossref", "first-page": "1", "DOI": "10.1190/geo2011-0340.1", "article-title": "Fast probabilistic petrophysical mapping of reservoirs from 3D seismic data", "volume": "77", "author": "Shahraeeni", "year": "2012", "journal-title": "Geophysics"}, {"issue": "1", "key": "10.1016/j.petrol.2021.109288_bib40", "first-page": "1929", "article-title": "Dropout: a simple way to prevent neural networks from overfitting", "volume": "15", "author": "Srivastava", "year": "2014", "journal-title": "J. Mach. Learn. Res."}, {"key": "10.1016/j.petrol.2021.109288_bib41", "doi-asserted-by": "crossref", "first-page": "403", "DOI": "10.1109/IJCNN.1989.118274", "article-title": "Consistent inference of probabilities in layered networks: predictions and generalizations", "volume": "2", "author": "Tishby", "year": "1989", "journal-title": "International Joint Conference on Neural Networks"}, {"key": "10.1016/j.petrol.2021.109288_bib42", "doi-asserted-by": "crossref", "first-page": "107297", "DOI": "10.1016/j.petrol.2020.107297", "article-title": "Uncertainty quantification and sensitivity analysis for relative permeability models of two-phase flow in porous media", "volume": "192", "author": "Valdez", "year": "2020", "journal-title": "J. Petrol. Sci. Eng."}, {"key": "10.1016/j.petrol.2021.109288_bib43", "article-title": "Data-driven pre-stack AVO inversion method based on fast orthogonal dictionary", "author": "Wang", "year": "2021", "journal-title": "J. Petrol. Sci. Eng."}, {"key": "10.1016/j.petrol.2021.109288_bib44", "series-title": "A Method for Determining and Displaying the Shear-Velocity Reflectivities of a Geologic Formation", "author": "Wiggins", "year": "1983"}, {"key": "10.1016/j.petrol.2021.109288_bib45", "series-title": "90th Ann. Internat. Mtg.", "first-page": "1444", "article-title": "Enrich the interpretation of seismic image segmentation by estimating epistemic uncertainty", "author": "Zhao", "year": "2020"}, {"issue": "5", "key": "10.1016/j.petrol.2021.109288_sref46", "first-page": "66", "article-title": "On the reflection and propagation of seismic waves", "volume": "1", "author": "Zoeppritz", "year": "1919", "journal-title": "Gottinger Nachrichten"}], "container-title": ["Journal of Petroleum Science and Engineering"], "language": "en", "link": [{"URL": "https://api.elsevier.com/content/article/PII:S0920410521009414?httpAccept=text/xml", "content-type": "text/xml", "content-version": "vor", "intended-application": "text-mining"}, {"URL": "https://api.elsevier.com/content/article/PII:S0920410521009414?httpAccept=text/plain", "content-type": "text/plain", "content-version": "vor", "intended-application": "text-mining"}], "deposited": {"date-parts": [[2025, 10, 12]], "date-time": "2025-10-12T15:47:36Z", "timestamp": 1760284056000}, "score": 32.140984, "resource": {"primary": {"URL": "https://linkinghub.elsevier.com/retrieve/pii/S0920410521009414"}}, "issued": {"date-parts": [[2022, 1]]}, "references-count": 46, "alternative-id": ["S0920410521009414"], "URL": "https://doi.org/10.1016/j.petrol.2021.109288", "ISSN": ["0920-4105"], "issn-type": [{"value": "0920-4105", "type": "print"}], "published": {"date-parts": [[2022, 1]]}, "assertion": [{"value": "Elsevier", "name": "publisher", "label": "This article is maintained by"}, {"value": "Uncertainty estimation in AVO inversion using Bayesian dropout based deep learning", "name": "articletitle", "label": "Article Title"}, {"value": "Journal of Petroleum Science and Engineering", "name": "journaltitle", "label": "Journal Title"}, {"value": "https://doi.org/10.1016/j.petrol.2021.109288", "name": "articlelink", "label": "CrossRef DOI link to publisher maintained version"}, {"value": "article", "name": "content_type", "label": "Content Type"}, {"value": "© 2021 Elsevier B.V. All rights reserved.", "name": "copyright", "label": "Copyright"}], "article-number": "109288"}], "items-per-page": 4, "query": {"start-index": 0, "search-terms": null}}}