To handle faults, I will have to leave the Fourier domain behind. The Fourier based method will estimate erroneous dips across faults. It will then try to honor these erroneous dips creating a result that behaves erratically. However, in the time-space domain, I should be able to handle all faults that have at least one half of the fault tip-line within the data cube. My approach is to create a masking operator () that will throw out dip estimates along faults. The method will try to remove all deformation except at the faults where it will allow complete slippage.

I want to find the time shifts, , such that their gradient () is the dip, . This sums across time-slices and is similar to equation (5). A time-space equivalent of equation (7) has also been implemented. I assume the dip, , is not a function of the unknown and write the fitting goal:

(8) |

(9) |

(10) |

7/8/2003