Full Waveform Inversion

DUG Wave: The future of seismic data processing and imaging.

Least-squares imaging powered by FWI.

Built for research and production at high frequency with huge datasets on HPC. Leverage superior physics and reduce turnaround time for subsurface characterisation.

A section through an RTM using conventionally processed data versus FWI imaging directly from field data. Least-squares, full wavefield imaging means that FWI is able to better illuminate target events in the sub-salt region as well as in the sedimentary sections.

A section and depth slice through a conventional processed Kirchhoff image versus FWI imaging. FWI delivers a clear resolution increase thanks to the full-wavefield least-squares imaging. (BEX MC3D data courtesy of Multi-Client Resources)

A depth slice through a conventional processed Kirchhoff image versus FWI imaging. FWI delivers a clear resolution increase thanks to the full-wavefield least-squares imaging. (BEX MC3D data courtesy of Multi-Client Resources)

The initial velocity model and FWI updated model in 3D. Note the clear increase in detail introduced by FWI. (BEX MC3D data courtesy of Multi-Client Resources)

High-frequency imaging with FWI

Our unique augmented acoustic wave equation isolates the “roo ears” to deliver simultaneous high-resolution velocity updates and least-squares imaging. At high frequency, this revolutionary approach provides reflectivity images for both structural and quantitative interpretation (including angle stacks for AVA analysis), without the need for a conventional processing and imaging workflow. A full-wavefield least-squares imaging solution using field-data input that simultaneously handles deghosting, demultiple and designature.

  • High-frequency reflectivity for interpretation
  • Simultaneous model building and least-squares imaging
  • Joint inversion for velocity and reflectivity
  • Full offset and angle stack outputs

Model building with FWI

FWI inverts for high-resolution earth models using the entire seismic wavefield. It is an integral part of our depth model-building strategies for conventional imaging workflows.

  • Model updates using diving waves (bananas) and reflections (roo ears)
  • Invert for source wavelets, velocity, anisotropy, Q

Starting velocity model and final model after FWI. Depth slice and inline with migrated stack overlaid with the respective velocity model. In this OBN example the shallow channels are well resolved after FWI, correcting the imaging distortions at depth. (Data courtesy of AGS and TGS)

Before and after FWI. Smooth starting velocity model prior to FWI (left) and after FWI, co-rendered with the seismic data (right). (Data courtesy of Shell NZ).

FWI using both high-resolution streamer data and sparse OBN data. Velocity updates beyond 5 km depth are achieved thanks to the long offset diving wave penetration of the OBN data. (LumiSeisTM data courtesy of MCG)

The engine room

Designed for geoscience, not computer science. Backed by some of the greenest HPC on the planet.

  • Loop-skipping mitigation
  • Integrated footprint removal
  • Multi-survey and multi-acquisition geometry compatible
  • Accelerated convergence using machine learning techniques
  • Domain decomposition
  • Bespoke functionality for marine (both shallow and deep water), land and ocean bottom surveys
  • Designed for HPC – computer science and hardware interactions are managed by the software for maximum robustness and efficiency

Quality control

  • Integrated QC products and data manipulation with complete control of dataflow pipeline
  • QC maps (including quantitative measures of objective function and phase)
  • Synthetics-only (forward modelling) mode
  • Independent source-wavelet inversion
  • Diving-wave depth of penetration

Diving wave penetration QC. The white “bananas” demonstrate the maximum depth of update expected from diving wave FWI.

Ready to power up to high-frequency FWI?

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