The application of a new machine learning paradigm based on pretraining and fine-tuning, StorSeismic, on field seismic data

Author:

Harsuko Randy1,Alkhalifah Tariq1

Affiliation:

1. King Abdullah University of Science and Technology

Publisher

Society of Exploration Geophysicists and American Association of Petroleum Geologists

Reference11 articles.

1. A cyclic learning approach for improving pre-stack seismic processing

2. Devlin , J. M.W. Chang , K. Lee , and K. Toutanova , 2018 , Bert: Pre-training of deep bidirectional transformers for language understanding : arXiv preprint arXiv:1810.04805.

3. Harsuko , R. , and T. Alkhalifah , 2022 , StorSeismic: An approach to pre-train a neural network to store seismic data features : 83rd Conference and Exhibition, EAGE , Extended Abstracts, doi: 10.3997/2214-4609.202210282 .

4. Liu , L. H. Jiang , P. He , W. Chen , X. Liu , J. Gao , and J. Han , 2019 , On the variance of the adaptive learning rate and beyond : arXiv preprint arXiv:1908.03265.

5. Lu , K. A. Grover , P. Abbeel , and I. Mordatch , 2021 , Pretrained transformers as universal computation engines : arXiv preprint arXiv:2103.05247.

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