Pre-stack seismic inversion using SeisInv-ResNet
Author:
Affiliation:
1. China University of Petroleum (East China)
2. Sinopec Research Institute
3. China Petroleum Logging CO.LTD.
Publisher
Society of Exploration Geophysicists
Link
https://library.seg.org/doi/pdf/10.1190/segam2019-3215750.1
Reference16 articles.
1. Estimation of fracture parameters from reflection seismic data—Part I: HTI model due to a single fracture set
2. Cao, D., P. An, and S. Liu, 2018, Elastic-parameters inversion from EI based on the deep-learning method: 88th Annual International Meeting, SEG, Expanded Abstracts, 640–644, doi: 10.1190/segam2018-2998479.1.
3. Chopra, S., and K. J. Marfurt, 2018, Seismic facies classification using some unsupervised machine-learning methods: 88th Annual International Meeting, SEG, Expanded Abstracts, 2056–2060, doi: 10.1190/segam2018-2997356.1.
4. Das, V., A. Pollack, U. Wollner, and T. Mukerji, 2018, Convolutional neural network for seismic impedance inversion: 88th Annual International Meeting, SEG, Expanded Abstracts, 2071–2075, doi: 10.1190/segam2018-2994378.1.
5. Jin, L., 2018, Machine learning approaches for seismic facies prediction and reservoir property inversion: 88th Annual International Meeting, SEG, Expanded Abstracts, 2147–2151, doi: 10.1190/segam2018-2996374.1.
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