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The XWITM optimisation toolbox powering Full Waveform Inversion now integrates the AWI and RWI 2.0 cost functions. This introduces increased sensitivity to the deepest and most subtle reflection moveout invisible to standard FWI formulations.

AWI redefines the cost function with filter coefficients penalised away from zero lag. RWI redefines the cost function with a virtual-source generated scattered wavefield. AWI avoids cycle-skipped solutions. RWI increases the penetration depth of macro-model updates.

In this demonstration (SEG14 blind synthetic) we generate a superior fit between predicted and observed data by using AWI and RWI to update the macro model below the diving wave zone.

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

X-Wave

No AWI. No RWI.

Cumulative update after 1st pass

Cumulative update after 1st pass

Velocity model after 1st pass

Velocity model after 1st pass

Cumulative update after 2nd pass

Cumulative update after 2nd pass

Velocity model after 2nd pass

Velocity model after 2nd pass

Cumulative update after 3rd pass

Cumulative update after 3rd pass

Velocity model after 3rd pass

Velocity model after 3rd pass

X-Wave + AWI

Deeper penetrating update

Cumulative update after 1st pass

Cumulative update after 1st pass

Velocity model after 1st pass

Velocity model after 1st pass

Cumulative update after 2nd pass

Cumulative update after 2nd pass

Velocity model after 2nd pass

Velocity model after 2nd pass

Cumulative update after 3rd pass

Cumulative update after 3rd pass

Velocity model after 3rd pass

Velocity model after 3rd pass

X-Wave + AWI + RWI 2.0

 Deepest penetrating update

Cumulative update after 1st pass

Cumulative update after 1st pass

Velocity model after 1st pass

Velocity model after 1st pass

Cumulative update after 2nd pass

Cumulative update after 2nd pass

Velocity model after 2nd pass

Velocity model after 2nd pass

Cumulative update after 3rd pass

Cumulative update after 3rd pass

Velocity model after 3rd pass

Velocity model after 3rd pass

Turning the remaining residual into macromodel updates

Enhanced convergence from fitting moveout curvature - deep reflected events in the predicted data shifting into position through the inversion.

Interleaved predicted (start) and field data

Interleaved predicted (start) and field data

Interleaved predicted (iteration 40) and field data

Interleaved predicted (iteration 40) 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 a step change in accuracy and resolution of your velocity model.