Source deghosting of coarsely sampled common-receiver data using machine learning
Author:
Affiliation:
1. Delft University of Technology
Publisher
Society of Exploration Geophysicists
Link
https://library.seg.org/doi/pdf/10.1190/segam2020-3413112.1
Reference7 articles.
1. On seismic deghosting by spatial deconvolution
2. Beasley, C. J., R. Coates, Y. Ji, and J. Perdomo, 2013, Wave equation receiver deghosting: A provocative example: SEG Technical Program, Expanded Abstracts, 4226–4230, doi: 10.1190/segam2013-0693.1.
3. Robertsson, J. O. A., L. Amundsen, and O. Pedersen, 2014, Deghosting of arbitrarily depth-varying marine hydrophone streamer data by time-space domain modelling: 84th Annual International Meeting, SEG, Expanded Abstracts, 4248–4252, doi: 10.1190/segam2014-0221.1.
4. Soubaras, R., 2010, Deghosting by joint deconvolution of a migration and a mirror migration: 80th Annual International Meeting, SEG, Expanded Abstracts, 3406–3410, doi: 10.1190/1.3513556.
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1. Deghosting dual‐component streamer data using demigration‐based supervised learning;Geophysical Prospecting;2023-08-10
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