Flattening without picking faults

Jesse Lomask, Antoine Guitton, and Alejandro Valenciano

lomask@sep.stanford.edu, antoine@sep.stanford.edu, valencia@sep.stanford.edu

ABSTRACT

We show that iteratively re-weighted least squares (IRLS) can flatten data cubes with vertically-oriented faults without having to pick the faults. One requirement is that the faults need to have at least part of their tip-lines (fault terminations) encased within the 3D cube. We demonstrate this method's flattening ability on a faulted 3D field data-set.