Seismic data compression using deep neural network predictors
Author:
Affiliation:
1. King Fahd University of Petroleum and Minerals
2. Georgia Institute of Technology
Publisher
Society of Exploration Geophysicists
Link
https://library.seg.org/doi/pdf/10.1190/segam2019-3205392.1
Reference21 articles.
1. Al-Moohimeed, M. A., 2004, Towards an efficient compression algorithm for seismic data: Proceedings of the Asia-Pacific Radio Science Conference, 550–553, 10.1109/aprasc.2004.1422555.
2. Low bit-rate efficient compression for seismic data
3. Efficient data compression for seismic-while-drilling applications
4. Seismic Data Compression Using 2D Lifting-Wavelet Algorithms
5. Harnik, D., E. Khaitzin, D. Sotnikov, and S. Taharlev, 2014, A fast implementation of deflate: Data Compression Conference, 223–232, 10.1109/dcc.2014.66.
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1. Seismic data compression: an overview;Multimedia Systems;2024-01-23
2. CSCNN: A Compressive Sensing Method Based on Convolutional Neural Networks for Seismic Data;IEEE Transactions on Geoscience and Remote Sensing;2024
3. Deep Seismic CS: A Deep Learning Assisted Compressive Sensing for Seismic Data;IEEE Transactions on Geoscience and Remote Sensing;2023
4. 1-ADM-CNN: A Lightweight In-field Compression Method for Seismic Data;IEEE Transactions on Circuits and Systems II: Express Briefs;2022-12
5. Clustering of Experimental Seismo-Acoustic Events Using Self-Organizing Map (SOM);Frontiers in Earth Science;2021-01-28
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