Physics-directed unsupervised machine learning: Quantifying uncertainty in seismic inversion-Reference-Cited by-同舟云学术

Physics-directed unsupervised machine learning: Quantifying uncertainty in seismic inversion

Published:2022-08-15 Issue: Volume: Page:
ISSN:
Container-title:Second International Meeting for Applied Geoscience & Energy
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Author:

Singh Sagar¹,Zhang Yu²,Thanoon David²,Devarakota Pandu³,Jin Long⁴,Tsvankin Ilya⁵

Affiliation:

1. Shell Global Solutions (US), Inc and Colorado School of Mines

2. Shell International Exploration and Production Inc

3. Shell Global Solutions (US), Inc

4. Shell Exploration and Production Company

5. Colorado School of Mines

Publisher

Society of Exploration Geophysicists and American Association of Petroleum Geologists

Link

https://library.seg.org/doi/pdf/10.1190/image2022-3730701.1

Reference20 articles.

1. Prestack and poststack inversion using a physics-guided convolutional neural network

2. Convolutional neural network for seismic impedance inversion

3. Di, H., Z. Wang, and G. AlRegib, 2018, Deep convolutional neural networks for seismic salt-body delineation: Presented at the AAPG Annual Convention and Exhibition.

4. Graves, A., 2011, Practical variational inference for neural networks: Advances in Neural Information Processing Systems, 2348–2356.

5. A blocky regularization scheme for full waveform inversion

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