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The XWITM optimisation toolbox now integrates the AWI and RWI 2.0 objective functions. This introduces increased sensitivity to the deepest and most subtle reflection moveout invisible to standard Full Waveform Inversion. In this demonstration (SEG14 blind synthetic) we apply X-Wave + AWI + RWI to the raw data.

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 avoids macro-model updates diminishing below the diving wave limit.

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.