FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891111066> ?p ?o ?g. }

• W2891111066 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2018Convolutional neural networks for fault interpretation in seismic imagesAuthors: Xinming WuYunzhi ShiSergey FomelLuming LiangXinming WuThe University of Texas at AustinSearch for more papers by this author, Yunzhi ShiThe University of Texas at AustinSearch for more papers by this author, Sergey FomelThe University of Texas at AustinSearch for more papers by this author, and Luming LiangUberSearch for more papers by this authorhttps://doi.org/10.1190/segam2018-2995341.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWe propose an automatic fault interpretation method by using convolutional neural networks (CNN). In this method, we construct a 7-layer CNN to first estimate fault orientations (dips and strikes) within small image patches that are extracted from a full seismic image. With the estimated fault orientations, we then construct anisotropic Gaussian functions that mainly extend along the estimated fault dips and strikes. We finally stack all the locally fault-oriented Gaussian functions to generate a fault probability image. Although trained by using only synthetic seismic images, the CNN model can accurately estimate fault orientations within real seismic images. The fault probability image, computed from the estimated fault orientations, displays cleaner, more accurate, and more continuous fault features than those in the conventional fault attribute images.Presentation Date: Tuesday, October 16, 2018Start Time: 8:30:00 AMLocation: 204B (Anaheim Convention Center)Presentation Type: OralKeywords: machine learning, faults, interpretation, artificial intelligence, seismic attributesPermalink: https://doi.org/10.1190/segam2018-2995341.1FiguresReferencesRelatedDetailsCited byFault detection using a convolutional neural network trained with point-spread function-convolution-based samplesJiankun Jing, Zhe Yan, Zheng Zhang, Hanming Gu, and Bingkai Han6 January 2023 | GEOPHYSICS, Vol. 88, No. 1Ensemble Deep Learning-Based Porosity Inversion From Seismic AttributesIEEE Access, Vol. 11Reflection and diffraction separation in the dip-angle common-image gathers using convolutional neural networkJiaxing Sun, Jidong Yang, Zhenchun Li, Jianping Huang, Jie Xu, and Subin Zhuang28 December 2022 | GEOPHYSICS, Vol. 88, No. 1Mineral exploration modeling by convolutional neural network and continuous genetic algorithm: a case study in Khorasan Razavi, Iran28 October 2022 | Arabian Journal of Geosciences, Vol. 15, No. 21Physics-directed unsupervised machine learning: Quantifying uncertainty in seismic inversionSagar Singh, Yu Zhang, David Thanoon, Pandu Devarakota, Long Jin, and Ilya Tsvankin15 August 2022Seismic data augmentation for automatic faults picking using deep learningNam Pham and Sergey Fomel15 August 2022Recurrent autoencoder model for unsupervised seismic facies analysisYanhui Zhou and Wenchao Chen15 June 2022 | Interpretation, Vol. 10, No. 3Fault enhancement comparison among coherence enhancement, probabilistic neural networks, and convolutional neural networks in the Taranaki Basin area, New ZealandJosé P. Mora, Heather Bedle, and Kurt J. Marfurt19 May 2022 | Interpretation, Vol. 10, No. 3Synthetic seismic data for training deep learning networksTom P. Merrifield, Donald P. Griffith, S. Ahmad Zamanian, Stephane Gesbert, Satyakee Sen, Jorge De La Torre Guzman, R. David Potter, and Henning Kuehl17 May 2022 | Interpretation, Vol. 10, No. 33D fault detection: Using human reasoning to improve performance of convolutional neural networksDonglin Zhu, Lei Li, Rui Guo, Chunfeng Tao, and Shifan Zhan23 June 2022 | GEOPHYSICS, Vol. 87, No. 4Automated fault detection in the Arabian BasinRuaa Alohali, Fatimah Alzubaidi, Martin van Kranendonk, and Stuart Clark9 May 2022 | GEOPHYSICS, Vol. 87, No. 4Synthetic seismic data generation for automated AI-based procedures with an example application to high-resolution interpretationFernando Vizeu, Joao Zambrini, Anne-Laure Tertois, Bruno de Albuquerque da Graça e Costa, André Queiroz Fernandes, and Anat Canning1 June 2022 | The Leading Edge, Vol. 41, No. 6Double-phase-shift filtering method for harmonic elimination based on AR2U-Net23 July 2022 | Applied Geophysics, Vol. 19, No. 2Fault enhancement using probabilistic neural networks and Laplacian of a Gaussian filter: A case study in the Great South Basin, New ZealandJose P. Mora, Heather Bedle, and Kurt J. Marfurt22 February 2022 | Interpretation, Vol. 10, No. 2A Deep Learning Method for Denoising Based on a Fast and Flexible Convolutional Neural NetworkIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Example Forgetting: A Novel Approach to Explain and Interpret Deep Neural Networks in Seismic InterpretationIEEE Transactions on Geoscience and Remote Sensing, Vol. 60MD Loss: Efficient Training of 3-D Seismic Fault Segmentation Network Under Sparse Labels by Weakening Anomaly AnnotationIEEE Transactions on Geoscience and Remote Sensing, Vol. 60Rotated-UNet: A seismic fault identification network based on inverse sampling block constructionMachine Learning as a Silent Observer of Advanced Geoscience Interpretation9 December 2021Self-supervised delineation of geologic structures using orthogonal latent space projectionOluwaseun Joseph Aribido, Ghassan AlRegib, and Yazeed Alaudah30 September 2021 | GEOPHYSICS, Vol. 86, No. 6An introduction to distributed training of deep neural networks for segmentation tasks with large seismic data setsClaire Birnie, Haithem Jarraya, and Fredrik Hansteen1 October 2021 | GEOPHYSICS, Vol. 86, No. 6Concealed-Fault Detection in Low-Amplitude Tectonic Area—An Example of Tight Sandstone Reservoirs13 October 2021 | Minerals, Vol. 11, No. 10Harnessing the Power of Artificial Intelligence and Machine Learning in Mineral Exploration—Opportunities and Cautionary NotesSEG Discovery, Vol. 12, No. 127Metallic deposits imaging based on U-net deep learning methodXiaojie Wan, Xiangbo Gong, Zhuo Xu, and Minghao Yu1 September 2021Application of lightweight network on seismic facies interpretation of buried hill in Bongor Basin, ChadTao Liu, Yaojun Wang, Bangli Zou, Wenhui Du, and Lideng Gan1 September 2021VSP wavefiled separation using GAN base on asymmetric convolution blocksDanping Cao, Yan Jia, and Rongang Cuia1 September 20213D scattering wavelet transform CNN for seismic fault detectionShi’an Shen, Xiaokai Wang, Yanhui Zhou, Zhensheng Shi, Wenchao Chen, and Cheng Wang1 September 2021Seismic Stratum Segmentation Using an Encoder–Decoder Convolutional Neural Network12 February 2021 | Mathematical Geosciences, Vol. 53, No. 6Research on fault recognition method combining 3D Res-UNet and knowledge distillation8 November 2021 | Applied Geophysics, Vol. 18, No. 2Inversion of vehicle-induced signals based on seismic interferometry and recurrent neural networksLu Liu, Yujin Liu, Tao Li, Yi He, Yue Du, and Yi Luo8 April 2021 | GEOPHYSICS, Vol. 86, No. 3Automatic seismic facies interpretation using supervised deep learningHaoran Zhang, Tiansheng Chen, Yang Liu, Yuxi Zhang, and Jiong Liu11 January 2021 | GEOPHYSICS, Vol. 86, No. 1Interactively tracking seismic geobodies with a deep-learning flood-filling networkYunzhi Shi, Xinming Wu, and Sergey Fomel14 December 2020 | GEOPHYSICS, Vol. 86, No. 1Summarized Applications of Machine Learning in Subsurface Geosciences4 May 2021Overview of Seismic Attributes and Seismic Object Extraction21 February 2022Residual Learning of Cycle-GAN for Seismic Data DenoisingIEEE Access, Vol. 9Inversion of Oceanic Parameters Represented by CTD Utilizing Seismic Multi-Attributes Based on Convolutional Neural Network15 November 2020 | Journal of Ocean University of China, Vol. 19, No. 6Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production DataGeofluids, Vol. 2020Predicting Magnetization Directions Using Convolutional Neural Networks12 October 2020 | Journal of Geophysical Research: Solid Earth, Vol. 125, No. 10Microseismic event or noise: Automatic classification with convolutional neural networksBenjamin Consolvo and Michael Thornton30 September 2020Seismic fault detection based on 3D Unet++ modelDun Yang, Yufei Cai, Guangmin Hu, Xingmiao Yao, and Wen Zou30 September 2020Building realistic structure models to train convolutional neural networks for seismic structural interpretationXinming Wu, Zhicheng Geng, Yunzhi Shi, Nam Pham, Sergey Fomel, and Guillaume Caumon16 January 2020 | GEOPHYSICS, Vol. 85, No. 4Poststack Seismic Data Denoising Based on 3-D Convolutional Neural NetworkIEEE Transactions on Geoscience and Remote Sensing, Vol. 58, No. 3Automatic velocity analysis using convolutional neural network and transfer learningMin Jun Park and Mauricio D. Sacchi22 November 2019 | GEOPHYSICS, Vol. 85, No. 1CNN Prediction Enhancement by Post-Processing for Hydrocarbon Detection in Seismic ImagesIEEE Access, Vol. 8U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV ImageryIEEE Access, Vol. 8Estimating normal moveout velocity using the recurrent neural networkReetam Biswas, Anthony Vassiliou, Rodney Stromberg, and Mrinal K. Sen20 September 2019 | Interpretation, Vol. 7, No. 4Deep learning for denoisingSiwei Yu, Jianwei Ma, and Wenlong Wang9 October 2019 | GEOPHYSICS, Vol. 84, No. 6Semiautomated seismic horizon interpretation using the encoder-decoder convolutional neural networkHao Wu, Bo Zhang, Tengfei Lin, Danping Cao, and Yihuai Lou16 October 2019 | GEOPHYSICS, Vol. 84, No. 6Building realistic structure models to train convolutional neural networks for seismic structural interpretationXinming Wu, Zhicheng Geng, Yunzhi Shi, Nam Pham, and Sergey Fomel10 August 2019Relative geologic time estimation using a deep convolutional neural networkZhicheng Geng, Xinming Wu, Yunzhi Shi, and Sergey Fomel10 August 2019Seismic stratigraphy interpretation via deep convolutional neural networksHaibin Di, Zhun Li, Hiren Maniar, and Aria Abubakar10 August 2019Innovative automatic fault detection using a volume 3D scanning methodSven Philit, Sébastien Lacaze, and Fabien Pauget10 August 2019Interactive tracking of seismic geobodies using deep learning flood-filling networkYunzhi Shi and Xinming Wu10 August 2019Pre-stack inversion using a physics-guided convolutional neural networkReetam Biswas, Mrinal K. Sen, Vishal Das, and Tapan Mukerji1 August 2019Automatic seismic facies interpretation based on an enhanced encoder-decoder structureHaoran Zhang, Yang Liu, Yuxi Zhang, and Hao Xue10 August 2019Semi-automated seismic horizon interpretation using encoder-decoder convolutional neural networkHao Wu and Bo Zhang10 August 2019FaultNet: A deep CNN model for 3D automated fault pickingQie Zhang, Anar Yusifov, Corey Joy, Yunzhi Shi, and Xinming Wu10 August 20193D convolutional neural networks for efficient fault detection and orientation estimationTao Zhao10 August 2019Machine learning-assisted seismic interpretation with geologic constraintsHaibin Di, Cen Li, Stewart Smith, and Aria Abubakar10 August 2019An enhanced fault-detection method based on adaptive spectral decomposition and super-resolution deep learningZhenyu Yuan, Handong Huang, Yuxin Jiang, Jinbiao Tang, and Jingjing Li7 August 2019 | Interpretation, Vol. 7, No. 3Attenuation of random noise using denoising convolutional neural networksXu Si, Yijun Yuan, Tinghua Si, and Shiwen Gao7 August 2019 | Interpretation, Vol. 7, No. 3Deep learning applied to seismic attribute computationDonald P. Griffith, S. Ahmad Zamanian, Jeremy Vila, Antoine Vial-Aussavy, John Solum, R. David Potter, and Francesco Menapace12 June 2019 | Interpretation, Vol. 7, No. 3Seismic image processing through the generative adversarial networkFrancesco Picetti, Vincenzo Lipari, Paolo Bestagini, and Stefano Tubaro28 May 2019 | Interpretation, Vol. 7, No. 3SaltSeg: Automatic 3D salt segmentation using a deep convolutional neural networkYunzhi Shi, Xinming Wu, and Sergey Fomel28 May 2019 | Interpretation, Vol. 7, No. 3Prestack and poststack inversion using a physics-guided convolutional neural networkReetam Biswas, Mrinal K. Sen, Vishal Das, and Tapan Mukerji15 July 2019 | Interpretation, Vol. 7, No. 3Applications of supervised deep learning for seismic interpretation and inversionYork Zheng, Qie Zhang, Anar Yusifov, and Yunzhi Shi8 July 2019 | The Leading Edge, Vol. 38, No. 7FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentationXinming Wu, Luming Liang, Yunzhi Shi, and Sergey Fomel16 April 2019 | GEOPHYSICS, Vol. 84, No. 3Quantification of uncertainty in 3-D seismic interpretation: implications for deterministic and stochastic geomodeling and machine learning5 July 2019 | Solid Earth, Vol. 10, No. 4 SEG Technical Program Expanded Abstracts 2018ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2018 Pages: 5520 publication data© 2018 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 27 Aug 2018 CITATION INFORMATION Xinming Wu, Yunzhi Shi, Sergey Fomel, and Luming Liang, (2018), Convolutional neural networks for fault interpretation in seismic images, SEG Technical Program Expanded Abstracts : 1946-1950. https://doi.org/10.1190/segam2018-2995341.1 Plain-Language Summary Keywordsmachine learningfaultsinterpretationartificial intelligenceseismic attributesPDF DownloadLoading ..." @default.
• W2891111066 created "2018-09-27" @default.
• W2891111066 creator A5005237932 @default.
• W2891111066 creator A5051505252 @default.
• W2891111066 creator A5058640298 @default.
• W2891111066 creator A5091058254 @default.
• W2891111066 date "2018-08-27" @default.
• W2891111066 modified "2023-09-29" @default.
• W2891111066 title "Convolutional neural networks for fault interpretation in seismic images" @default.
• W2891111066 cites W1849277567 @default.
• W2891111066 cites W1901129140 @default.
• W2891111066 cites W2029081900 @default.
• W2891111066 cites W2040780298 @default.
• W2891111066 cites W2067678103 @default.
• W2891111066 cites W2102605133 @default.
• W2891111066 cites W2106921281 @default.
• W2891111066 cites W2112796928 @default.
• W2891111066 cites W2117539524 @default.
• W2891111066 cites W2118701387 @default.
• W2891111066 cites W2124386111 @default.
• W2891111066 cites W2126097500 @default.
• W2891111066 cites W2139663179 @default.
• W2891111066 cites W2147800946 @default.
• W2891111066 cites W2194775991 @default.
• W2891111066 cites W2287473451 @default.
• W2891111066 cites W2314961947 @default.
• W2891111066 cites W2588053692 @default.
• W2891111066 cites W2613245639 @default.
• W2891111066 cites W2708728779 @default.
• W2891111066 cites W2963150697 @default.
• W2891111066 cites W2963881378 @default.
• W2891111066 cites W4239510810 @default.
• W2891111066 cites W4251868378 @default.
• W2891111066 doi "https://doi.org/10.1190/segam2018-2995341.1" @default.
• W2891111066 hasPublicationYear "2018" @default.
• W2891111066 type Work @default.
• W2891111066 sameAs 2891111066 @default.
• W2891111066 citedByCount "74" @default.
• W2891111066 countsByYear W28911110662019 @default.
• W2891111066 countsByYear W28911110662020 @default.
• W2891111066 countsByYear W28911110662021 @default.
• W2891111066 countsByYear W28911110662022 @default.
• W2891111066 countsByYear W28911110662023 @default.
• W2891111066 crossrefType "proceedings-article" @default.
• W2891111066 hasAuthorship W2891111066A5005237932 @default.
• W2891111066 hasAuthorship W2891111066A5051505252 @default.
• W2891111066 hasAuthorship W2891111066A5058640298 @default.
• W2891111066 hasAuthorship W2891111066A5091058254 @default.
• W2891111066 hasConcept C127313418 @default.
• W2891111066 hasConcept C153180895 @default.
• W2891111066 hasConcept C154945302 @default.
• W2891111066 hasConcept C165205528 @default.
• W2891111066 hasConcept C175551986 @default.
• W2891111066 hasConcept C199360897 @default.
• W2891111066 hasConcept C41008148 @default.
• W2891111066 hasConcept C527412718 @default.
• W2891111066 hasConcept C81363708 @default.
• W2891111066 hasConceptScore W2891111066C127313418 @default.
• W2891111066 hasConceptScore W2891111066C153180895 @default.
• W2891111066 hasConceptScore W2891111066C154945302 @default.
• W2891111066 hasConceptScore W2891111066C165205528 @default.
• W2891111066 hasConceptScore W2891111066C175551986 @default.
• W2891111066 hasConceptScore W2891111066C199360897 @default.
• W2891111066 hasConceptScore W2891111066C41008148 @default.
• W2891111066 hasConceptScore W2891111066C527412718 @default.
• W2891111066 hasConceptScore W2891111066C81363708 @default.
• W2891111066 hasLocation W28911110661 @default.
• W2891111066 hasOpenAccess W2891111066 @default.
• W2891111066 hasPrimaryLocation W28911110661 @default.
• W2891111066 hasRelatedWork W2004013317 @default.
• W2891111066 hasRelatedWork W2022035173 @default.
• W2891111066 hasRelatedWork W2092739438 @default.
• W2891111066 hasRelatedWork W2167517487 @default.
• W2891111066 hasRelatedWork W2355282060 @default.
• W2891111066 hasRelatedWork W2767651786 @default.
• W2891111066 hasRelatedWork W2772196783 @default.
• W2891111066 hasRelatedWork W2912288872 @default.
• W2891111066 hasRelatedWork W3113596969 @default.
• W2891111066 hasRelatedWork W564581980 @default.
• W2891111066 isParatext "false" @default.
• W2891111066 isRetracted "false" @default.
• W2891111066 magId "2891111066" @default.
• W2891111066 workType "article" @default.