Deep learning

Electrical engineering

Physics

Geology

Engineering

Recurrent neural network

Artificial intelligence

Seismology

Workflow

Electrical impedance

Inversion (geology)

Acoustics

Computer science

Artificial neural network

Database

Tectonics

Geophysics

Convolutional neural network

Acoustic impedance

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Semi-supervised learning for acoustic impedance inversionAuthors: Motaz AlfarrajGhassan AlRegibMotaz AlfarrajCenter for Energy and Geo Processing (CeGP), Georgia Institute of TechnologySearch for more papers by this author and Ghassan AlRegibCenter for Energy and Geo Processing (CeGP), Georgia Institute of TechnologySearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3215902.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractRecent applications of deep learning in the seismic domain have shown great potential in different areas such as inversion and interpretation. Deep learning algorithms, in general, require tremendous amounts of labeled data to train properly. To overcome this issue, we propose a semi-supervised framework for acoustic impedance inversion based on convolutional and recurrent neural networks. Specifically, seismic traces and acoustic impedance traces are modeled as time series. Then, a neural-network-based inversion model comprising convolutional and recurrent neural layers is used to invert seismic data for acoustic impedance. The proposed workflow uses well log data to guide the inversion. In addition, it utilizes a learned seismic forward model to regularize the training and to serve as a geophysical constraint for the inversion. The proposed workflow achieves an average correlation of 98% between the estimated and target elastic impedance using 20 AI traces for training.Presentation Date: Tuesday, September 17, 2019Session Start Time: 8:30 AMPresentation Time: 9:45 AMLocation: 221DPresentation Type: OralKeywords: inversion, machine learning, impedance, acousticPermalink: https://doi.org/10.1190/segam2019-3215902.1FiguresReferencesRelatedDetailsCited byPhysics-driven self-supervised learning system for seismic velocity inversionBin Liu, Peng Jiang, Qingyang Wang, Yuxiao Ren, Senlin Yang, and Anthony G. 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Semi-supervised learning for acoustic impedance inversion