DIG-Kaust/VolveSynthetic: A Volve-alike synthetic dataset for testing of seismic processing and imaging algorithms

Type
Software
Authors
Ravasi, Matteo cc
Luiken, Nick
Selvan, Tamin
KAUST Department
School of Earth Science and Engineering, Physical Science and Engineering Division, and KAUST Extreme Computing Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
Physical Science and Engineering (PSE) Division
Extreme Computing Research Center
Earth Science and Engineering Program
Date
2021-10-31
Permanent link to this record
http://hdl.handle.net/10754/678675

Metadata
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Abstract
A Volve-alike synthetic dataset for testing of seismic processing and imaging algorithms
Publisher
Github
Additional Links
https://github.com/DIG-Kaust/VolveSynthetic
Relations
Is Supplement To:
  • [Article]
    Ravasi, M., Selvan, T., & Luiken, N. (2022). Stochastic Multi-Dimensional Deconvolution. IEEE Transactions on Geoscience and Remote Sensing, 1–1. https://doi.org/10.1109/tgrs.2022.3179626. DOI: 10.1109/tgrs.2022.3179626 Handle: 10754/677969
  • [Preprint]
    Title: Stochastic Multi-Dimensional Deconvolution. Publication Date: 2022-02-09. Handle: 10754/677969 arXiv: 2202.04486
Collections
Physical Science and Engineering (PSE) Division; Extreme Computing Research Center; Earth Science and Engineering Program; Software