Analytical flattening with adjustable regularization

Jesse Lomask and Antoine Guitton

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

ABSTRACT

We add an adjustable regularization parameter to the analytical flattening method which integrates dips in the Fourier domain. The regularization penalizes roughness in depth in the integration result which, in turn, insures that the flattening result is monotonic and continuous. This preserves the data which is necessary for multiple flattening passes or for undoing the flattening result. Because we preform the integration in the Fourier domain, this method is still highly efficient. 2D field gathers and a stacked section are provided as examples. This can easily be extended to 3D, allowing adjustable regularization along shot gathers.