English

Does 3D frequency-domain FWI of full-azimuth/long-offset OBN data feasible? The Gorgon case study

Optimization and Control 2023-01-13 v2

Abstract

Frequency-domain Full Waveform Inversion (FWI) is potentially amenable to efficient processing of full-azimuth long-offset stationary-recording seabed acquisition carried out with sparse layout of ocean bottom nodes (OBNs) and broadband sources because the inversion can be performed with a few discrete frequencies. However, computing efficiently the solution of the forward (boundary-value) problem in the frequency domain with linear algebra solvers remains a challenge for large computational domains involving tens to hundreds of millions of parameters. We illustrate the feasibility of 3D frequency-domain FWI with the 2015/16 Gorgon OBN case study in the NorthWestern shelf, Australia. We solve the forward problem with the massively-parallel multifrontal direct solver MUMPS, which includes four key features to reach high computational efficiency: An efficient parallelism combining message-passing interface and multithreading, block low-rank compression, mixed precision arithmetic and efficient processing of sparse sources. The Gorgon subdataset involves 650 OBNs that are processed as reciprocal sources and 400,000 sources. Mono-parameter FWI for vertical wavespeed is performed in the visco-acoustic VTI approximation with a classical frequency continuation approach proceeding from a starting frequency of 1.7 Hz to a final frequency of 13 Hz. The target covers an area ranging from 260 km2 (frequency > 8.5 Hz) to 705 km2 (frequency < 8.5 Hz) for a maximum depth of 8 km. Compared to the starting model, FWI dramatically improves the reconstruction of the bounding faults of the Gorgon horst at reservoir depths as well as several intra-horst faults and several horizons of the Mungaroo formation down to a depth of 7 km.

Keywords

Cite

@article{arxiv.2210.16767,
  title  = {Does 3D frequency-domain FWI of full-azimuth/long-offset OBN data feasible? The Gorgon case study},
  author = {S. Operto and P. Amestoy and H. S. Aghamiry and S. Beller and A. Buttari and L. Combe and V. Dolean and M. Gerest and G. Guo and P. Jolivet and J. -Y. L'Excellent and F. Mamfoumbi and T. Mary and C. Puglisi and A. Ribodetti and P. -H. Tournier},
  journal= {arXiv preprint arXiv:2210.16767},
  year   = {2023}
}