I applied ten iterations of this regularized inversion to the same part of the Sigsbee2A dataset as shown above. The result can be seen in Figure . The stacked result in the upper panel shows that we have extended the events even further under the salt edges than the result after model space weighting (Figure ). The events under the salt are stronger than the result of the migration (Figure ) but do not have the artificial amplitude variations or the increase in the strength of artifacts that are seen in the result of model space weighting. The fault underneath the salt nose is better focused. The stacked inversion result also has much cleaner shadow zones than both the migration and weighted results. In the ray parameter gathers, the inversion result shows better continuity and amplitude behavior than the migration result and is much cleaner than the weighted result. Overall, the inversion result is better than the model space weighted result. This can be seen even more clearly in Figure , which shows an enlarged view of the stacks in the area at the salt edge for the migration (left panel), the model space weighted result (center), and the regularized inversion result (right panel). The inversion result has done a better job of extending the reflectors beneath the salt and has fewer artifacts than either of the other results. However, the inversion result has a computational cost approximately seven times greater than the weighted result.