Speaker: Adriano Gomes, CGG

In the last few years, several methods have been proposed to increase the significance of reflection data in the FWI workflow. A common feature in all these methods is the extraction and/or enhancement of the low-wavenumber component of the FWI gradient of reflection data, also known as tomographic term or “rabbit ears”, which is generated along the reflection wavepath.

It is clear that despite having the potential to extend the maximum update depth beyond that of diving-wave FWI, Reflection FWI (RFWI) is also subject to additional restrictions, due to the “tomographic” nature of the problem. In fact, RFWI shares many concepts with migration velocity analysis (MVA) methods, such as ray-based reflection tomography. However, since the contributions from many events are calculated simultaneously in RFWI, it is more susceptible to the effects of amplitude imbalance, which can lead to limited vertical resolution and convergence to local minima.

In this talk, the main limitations and requirements of this approach are discussed using synthetic examples. Finally, this method is applied to subsalt datasets in the Gulf of Mexico. The results suggest that, at the current stage, RFWI can be viewed as a complement, rather than a replacement, to established velocity inversion methods.  Nonetheless, the significant improvement obtained by RFWI in the real data example shows that this technique is worth understanding and improving further, as it could become a valuable tool for updating the deeper section of velocity models.

Technical Talk will start at 7:30

Event will end by 8:30

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