Seismic data interpolation through convolutional autoencoder

Abstract

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2018Seismic data interpolation through convolutional autoencoderAuthors: Sara MandelliFederico BorraVincenzo LipariPaolo BestaginiAugusto SartiStefano TubaroSara MandelliPolitecnico di Milano, ItalySearch for more papers by this author, Federico BorraPolitecnico di Milano, ItalySearch for more papers by this author, Vincenzo LipariPolitecnico di Milano, ItalySearch for more papers by this author, Paolo BestaginiPolitecnico di Milano, ItalySearch for more papers by this author, Augusto SartiPolitecnico di Milano, ItalySearch for more papers by this author, and Stefano TubaroPolitecnico di Milano, ItalySearch for more papers by this authorhttps://doi.org/10.1190/segam2018-2995428.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract A common issue of seismic data analysis consists in the lack of regular and densely sampled seismic traces. This problem is commonly tackled by rank optimization or statistical features learning algorithms, which allow interpolation and denoising of corrupted data. In this paper, we propose a completely novel approach for reconstructing missing traces of pre-stack seismic data, taking inspiration from computer vision and image processing latest developments. More specifically, we exploit a specific kind of convolutional neural networks known as convolutional autoencoder. We illustrate the advantages of using deep learning strategies with respect to state-of-the-art by comparing the achieved results over a well-known seismic dataset. Presentation Date: Wednesday, October 17, 2018 Start Time: 1:50:00 PM Location: 204C (Anaheim Convention Center) Presentation Type: Oral Keywords: interpolation, machine learning, neural networks, processing, data reconstructionPermalink: https://doi.org/10.1190/segam2018-2995428.1FiguresReferencesRelatedDetailsCited byFault2SeisGAN: A method for the expansion of fault datasets based on generative adversarial networks20 January 2023 | Frontiers in Earth Science, Vol. 11Depthwise separable convolution Unet for 3D seismic data interpolation11 January 2023 | Frontiers in Earth Science, Vol. 10Simultaneous reconstruction and denoising for DAS-VSP seismic data by RRU-net9 January 2023 | Frontiers in Earth Science, Vol. 10Regeneration-Constrained Self-Supervised Seismic Data InterpolationIEEE Transactions on Geoscience and Remote Sensing, Vol. 61Inverse-Scattering Theory Guided U-Net Neural Networks for Internal Multiple EliminationIEEE Transactions on Geoscience and Remote Sensing, Vol. 61DIPPAS: a deep image prior PRNU anonymization scheme14 February 2022 | EURASIP Journal on Information Security, Vol. 2022, No. 1Simple framework for the contrastive learning of visual representations-based data-driven tight frame for seismic denoising and interpolationJinghe Li and Xiangling Wu1 August 2022 | GEOPHYSICS, Vol. 87, No. 5Improved Unet in Lithology Identification of Coal Measure Strata24 August 2022 | Lithosphere, Vol. 2022, No. Special 12Internal multiple elimination with an inverse-scattering theory guided deep neural networkZhiwei Gu, Liurong Tao, Haoran Ren, Ru-Shan Wu, and Jianhua Geng15 August 2022Equivariant imaging for self-supervised regularly undersampled seismic data interpolationWeiwei Xu, Vincenzo Lipari, Paolo Bestagini, Politecnico di Milano, Wenchao Chen, and Stefano Tubaro15 August 2022Deep learning decomposition for null and active space estimation for thin-bed reflectivity inversionKristian Torres and Mauricio D. 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