Automatic salt geometry update using deep learning in iterative FWI-RTM workflows-Reference-Cited by-同舟云学术

Automatic salt geometry update using deep learning in iterative FWI-RTM workflows

Published:2021-09-01 Issue: Volume: Page:
ISSN:
Container-title:First International Meeting for Applied Geoscience & Energy Expanded Abstracts
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Author:

Zhao Tao¹,Zhao Chunpeng¹,Kaul Anisha¹,Abubakar Aria¹

Affiliation:

1. Schlumberger

Publisher

Society of Exploration Geophysicists

Link

https://library.seg.org/doi/pdf/10.1190/segam2021-3582052.1

Reference12 articles.

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2. Adjustive full waveform inversion

3. Salt Reshaping with Template-Matching Full-Waveform Inversion

4. Deep learning prior models from seismic images for full-waveform inversion

5. U-Net: Convolutional Networks for Biomedical Image Segmentation

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1. Wasserstein Distance-Based Full-Waveform Inversion With a Regularizer Powered by Learned Gradient;IEEE Transactions on Geoscience and Remote Sensing;2023

2. Integrating U-Nets Into a Multiscale Full-Waveform Inversion for Salt Body Building;IEEE Transactions on Geoscience and Remote Sensing;2023

3. Integrating U-net with full-waveform inversion for an efficient salt body construction;Second International Meeting for Applied Geoscience & Energy;2022-08-15

4. Predicting horizons for salt body models using machine learning from neighboring seismic surveys: A case study from the northern Gulf of Mexico;Second International Meeting for Applied Geoscience & Energy;2022-08-15