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S-Cube delivers its breakthrough waveform inversion through the XWITM toolbox which now integrates the AWI and RWI objective functions.  This latest result on the SEG14 blind test dataset demonstrates why this combination is theoretically and practically potentially the most accurate and efficient algorithm in existence to determine the final velocity model deep below the diving wave limit from a highly inaccurate starting model.

Original Starting Model 

Original starting velocity model

Original starting velocity model

Simplified Starting Model 

Change relative to original start

Change relative to original start

Simplified starting velocity model

Simplified starting velocity model

Full Waveform Inversion

Vanilla Technique

Single-Pass FWI

 

Cumulative update after 1 pass of FWI

Cumulative update after 1 pass of FWI

Velocity model after 1 pass of FWI

Velocity model after 1 pass of FWI

Triple-Pass FWI

 

Cumulative update after 1st pass with smoothed FWI

Cumulative update after 1st pass with smoothed FWI

Velocity model after 1st pass with smoothed FWI

Velocity model after 1st pass with smoothed FWI

Cumulative update after 2nd pass with lightly smoothed FWI

Cumulative update after 2nd pass with lightly smoothed FWI

Velocity model after 2nd pass with lightly smoothed FWI

Velocity model after 2nd pass with lightly smoothed FWI

Cumulative update after 3rd pass FWI

Cumulative update after 3rd pass FWI

Velocity model after 3rd pass of FWI

Velocity model after 3rd pass of FWI

Adaptive Waveform Inversion

Overcomes Cycle Skipping

Single-Pass AWI

 

Cumulative update after 1 pass of AWI

Cumulative update after 1 pass of AWI

Velocity model after 1 pass of AWI

Velocity model after 1 pass of AWI

Triple-Pass AWI

 

Cumulative update after 1st pass with smoothed AWI

Cumulative update after 1st pass with smoothed AWI

Velocity model after 1st pass with smoothed AWI

Velocity model after 1st pass with smoothed AWI

Cumulative update after 2nd pass with lightly smoothed AWI

Cumulative update after 2nd pass with lightly smoothed AWI

Velocity model after 2nd pass with lightly smoothed AWI

Velocity model after 2nd pass with lightly smoothed AWI

Cumulative update after 3rd pass with FWI

Cumulative update after 3rd pass with FWI

Velocity model after 3rd pass with FWI

Velocity model after 3rd pass with FWI

Reflection Waveform  Inversion

 Extends Update Deeper

Harnessing reflection moveout

During RWI, near offset inversion is used to image reflectors generating temporary virtual sources in the model. Offsets are then opened up to update background velocity values with a scattered wavefield objective function. This misfit minimisation isolates and inverts the moveout misfit between the observed and predicted traces of the reflected events.

Virtual source data

Virtual source data

Initial background data

Initial background data

Updated background data

Updated background data

AWI-RWI integrated

Cumulative update after 1st pass integrated AWI-RWI

Cumulative update after 1st pass integrated AWI-RWI

Velocity model after 1st pass integrated AWI-RWI

Velocity model after 1st pass integrated AWI-RWI

Cumulative update after 2nd pass AWI-RWI

Cumulative update after 2nd pass AWI-RWI

Velocity model after 2nd pass AWI-RWI

Velocity model after 2nd pass AWI-RWI

Cumulative update after 3rd pass with final FWI

Cumulative update after 3rd pass with final FWI

Velocity model after 3rd pass with final FWI

Velocity model after 3rd pass with final FWI

Fitting deep reflected events

Deep reflected events in the predicted data shifting into positions through the inversion. 

Interleaved predicted (start) and field data

Interleaved predicted (start) and field data

Interleaved predicted (iteration 160) and field data

Interleaved predicted (iteration 160) and field data

Observed field data

Observed field data

S-Cube Cloud

Run Full Waveform Inversion on the Cloud

Use XWITM on AWS to discover an unprecedented increase in the resolution of your velocity model.