FUDLInter: Frequency-Space-Dependent Unsupervised Deep Learning Framework for 3-D and 5-D Seismic Data Interpolation
Author:
Affiliation:
1. National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum-Beijing, Beijing, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/36/10354519/10613894.pdf?arnumber=10613894
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1. Wave‐equation trace interpolation
2. Reducing 3-D acquisition footprint for 3-D DMO and 3-D prestack migration
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4. Seismic data reconstruction using multidimensional prediction filters
5. Seismic data interpolation beyond aliasing using regularized nonstationary autoregression
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