| Instructor: | Hossein Aghamiry |
|---|---|
| Duration: | 30 min + Q&A |
| Discipline: | Full-waveform inversion (FWI), Extended FWI |
| Main topics: | Ultra Long-Offset data |
| Language: | English |
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Ultra-long offset node acquisitions are beneficial for full waveform inversion (FWI) because the wide variety of wave types recorded by these geometries are suitable for broadband velocity model building. However,long propagation distances induced by long offsets exacerbate cycle skipping and the sparsity of the acquisition can inject wraparound artifacts into the reconstructed velocity model.In this presentation, we propose a FWI technology that is suitable for sparse long-offset OBN acquisition. Cycle skipping is mitigated through an extended search space in the framework of augmented Lagrangian and alternating direction method of multipliers (ADMM). Also, the robustness of the method for sparse acquisition is achieved with phase retrieval and sparsity-promoting regularization.
About the Lecturer
Hossein S. Aghamiry is a Postdoctoral Fellow at the University of Côte d’Azur and a visiting researcher at the Institute of Mathematics of University of Potsdam. He holds a PhD degree in seismology from the University of Tehran, and a PhD in sciences of the earth and universe from the University of Côte d’Azur, done in the framework of a joint PhD student in 2019. His research interests include linear and non-linear optimization and their applications in seismic processing, imaging, and inversion.