Full-Waveform Inversion
Unlock better decisions, faster, with FWI for high-resolution velocity model building
Viridien’s Full-Waveform Inversion (FWI) technology delivers highly accurate velocity models essential for advanced seismic imaging and interpretation. From exploration to field development, our global FWI expertise helps reduce geophysical uncertainty, especially in geologically complex environments.
Whether it’s marine or land, streamer or OBN, 3D or 4D, our FWI workflows are optimized to provide reliable velocity updates and clearer images of the subsurface—making your investments smarter at every stage of the asset lifecycle.
Time-Lag FWI
Time-lag FWI (TLFWI) is a proprietary Viridien technology that delivers dramatic improvements in seismic imaging. Compared to conventional FWI, this robust new approach minimizes classic FWI cycle-skipping issues related to an inaccurate initial model, amplitude mismatches and poor signal-to-noise ratio.
TLFWI has been highly successful in velocity model building for complex geological settings around the world, including sub-salt, pre-salt, carbonates, thrust belts and shallow gas. It is regularly used in conjunction with newly acquired ocean bottom node (OBN) data sets and as a way of improving the image on legacy seismic data.
FWI Imaging
Our FWI workflow can output an image of subsurface reflectivity directly, often providing a superior result in complex areas where migration algorithms struggle. Compared with a conventional migration image, the reflectivity image directly output from FWI often provides additional structural information with better illumination and higher signal-to-noise ratio naturally as a result of many iterations of least-squares fitting of the full-wavefield data, including diving waves and multiples.
We have successfully applied FWI imaging to varied data types in different geologic settings, solving some of the most challenging imaging problems that have not been resolved with conventional migrations.
Elastic Time-Lag FWI
To overcome limitations imposed by acoustic assumptions, we have developed an elastic FWI algorithm that combines an elastic modeling engine with the time-lag cost function.
FWI images derived from E-TLFWI show considerable benefits, such as:
- Reduced salt halo
- Sharpened salt boundaries
- Improved event focusing
- Better structural continuity
- Higher signal-to-noise ratio
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