Automatic Waveform Inversion based Salt Flooding

Full-waveform inversion (FWI) attempts to resolve an ill-posed nonlinear optimization problem to retrieve the unknown subsurface model parameters from seismic data. In general, FWI fails to obtain an adequate representation of models with large high-velocity structures over a wide region, such as salt bodies and the sediments beneath them, in the absence of low frequencies in the recorded seismic signal, due to nonlinearity and non-uniqueness. We alleviate the ill-posedness of FWI associated with data sets affected by salt bodies using model regularization.

We have split the optimization problem into two parts:

1. Minimize the data misfit and the total variation in the model, seeking to achieve an inverted model with sharp interfaces; 
2. Minimize sharp velocity drops with depth in the model.

Unlike conventional industrial salt flooding, our technique requires minimal human intervention and no information about the top of the salt. Those features are demonstrated on data sets of the BP 2004 and Sigsbee2A models, synthesized from a Ricker wavelet of dominant frequency 5.5 Hz and minimum frequency 3 Hz. We initiate the inversion process with a simple model in which the velocity increases linearly with depth. The model is well-retrieved when the same constant density acoustic code is used to simulate the observed data, which is still one of the most common FWI tests. Moreover, our technique allows us to reconstruct a reasonable depiction of the salt structure from the data synthesized independently with the BP 2004 model with variable density. In the Sigsbee2A model, we manage to even capture some of the fine layering beneath the salt. In addition, we evaluate the versatility of our method on a field data set from the Gulf of Mexico.

The evolution of salt flooding as part of full waveform inversion

References

Kalita M., Kazei V., Choi Y., and Alkhalifah T., (2019), "Regularized full-waveform inversion with automated salt flooding", GEOPHYSICS, 84(4): R569–R582. DOI:10.1190/GEO2018-0146.1