Facies classification with weak and strong supervision: A comparative study

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Facies classification with weak and strong supervision: A comparative studyAuthors: Yazeed AlaudahMoamen SolimanGhassan AlRegibYazeed AlaudahCenter for Energy and Geo Processing (CeGP) at Georgia Tech and KFUPMSearch for more papers by this author, Moamen SolimanCenter for Energy and Geo Processing (CeGP) at Georgia Tech and KFUPMSearch for more papers by this author, and Ghassan AlRegibCenter for Energy and Geo Processing (CeGP) at Georgia Tech and KFUPMSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3216766.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe growing interest in using deep learning for facies classification has been facing a significant obstacle, namely the absence of large publicly available annotated datasets for training and testing models. Recently, we released a large facies classification dataset and provided benchmark results for fully-supervised CNNs based on an encoder-decoder style architecture. We also open-sourced all our training and testing codes for the research community. In this work, we investigate the results of using fully-supervised facies classification models versus weakly-supervised ones that do not use manually annotated data. We compare the two approaches, and we show results on our facies classification dataset. Finally, we share our conclusions on how weakly-supervised facies classification models can be improved.Presentation Date: Tuesday, September 17, 2019Session Start Time: 8:30 AMPresentation Time: 9:45 AMLocation: 221BPresentation Type: OralKeywords: machine learning, neural networks, facies, interpretation, artificial intelligencePermalink: https://doi.org/10.1190/segam2019-3216766.1FiguresReferencesRelatedDetailsCited byReliable uncertainty estimation for seismic interpretation with prediction switchesRyan Benkert, Mohit Prabhushankar, and Ghassan AlRegib8 August 2022Volumetric supervised contrastive learning for seismic semantic segmentationKiran Kokilepersaud, Mohit Prabhushankar, and Ghassan AlRegib15 August 2022Explainable machine learning for hydrocarbon prospect riskingAhmad Mustafa and Ghassan AlRegib15 August 2022Deep learning for automated seismic facies classificationEkaterina Tolstaya and Anton Egorov14 March 2022 | Interpretation, Vol. 10, No. 2Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic InterpretationIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Self-supervised delineation of geologic structures using orthogonal latent space projectionOluwaseun Joseph Aribido, Ghassan AlRegib, and Yazeed Alaudah30 September 2021 | GEOPHYSICS, Vol. 86, No. 6Explainable seismic neural networks using learning statisticsRyan Benkert, Oluwaseun Joseph Aribido, and Ghassan AlRegib1 September 2021Joint learning for spatial context-based seismic inversion of multiple data sets for improved generalizability and robustnessAhmad Mustafa, Motaz Alfarraj, and Ghassan AlRegib2 July 2021 | GEOPHYSICS, Vol. 86, No. 4Accelerating seismic fault and stratigraphy interpretation with deep CNNs: A case study of the Taranaki Basin, New ZealandHaibin Di, Leigh Truelove, Cen Li, and Aria Abubakar1 October 2020 | The Leading Edge, Vol. 39, No. 10Spatiotemporal modeling of seismic images for acoustic impedance estimationAhmad Mustafa, Motaz Alfarraj, and Ghassan AlRegib30 September 2020Joint learning for seismic inversion: An acoustic impedance estimation case studyAhmad Mustafa and Ghassan AlRegib30 September 2020 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Yazeed Alaudah, Moamen Soliman, and Ghassan AlRegib, (2019), "Facies classification with weak and strong supervision: A comparative study," SEG Technical Program Expanded Abstracts : 1868-1872. https://doi.org/10.1190/segam2019-3216766.1 Plain-Language Summary Keywordsmachine learningneural networksfaciesinterpretationartificial intelligencePDF DownloadLoading ...