Imaging under salt edges: A regularized least-squares inversion scheme

Marie L. Prucha, Robert G. Clapp, and Biondo L. Biondi

marie@sep.stanford.edu, bob@sep.stanford.edu, biondo@sep.stanford.edu

ABSTRACT

We introduce a method for improving the image in areas of poor illumination using least-squares inversion regularized with dip penalty filters in one and two dimensions. The use of these filters helps to emphasize the weak energy that exists in poorly illuminated areas, and fills-in gaps by assuming lateral continuity along the reflection-angle axis and/or the midpoint axes. We tested our regularized inversion method on synthetic and real data. The inversion employing one-dimensional filters along the reflection-angle axis generated prestack images significantly better than the images obtained by simple migration and unregularized inversion. The inversion employing two-dimensional filters reduced the frequency of the image but also increased reflectors' continuity and reduced noise.