Conclusions

In this paper, a preconditioned constrained flattening method is introduced that exploits Discrete Cosine Transforms to invert a Laplacian operator. This method is efficient in both its memory use and computational time.

As demonstrated here, the ability to incorporate some picking allows the reconstruction of horizons across faults that cut across the entire data cube. An interpreter can pick a few points on a 2D line and then flatten the entire 3D cube. With computational improvements in both the algorithm and hardware, this method could be applied on the fly, as the interpreter adds new picks.