The next step is to see how well our non-stationary filter regularizes
a tomography problem.
I constructed a synthetic anticline model
(Figure 11)
with six reflectors,one above the anticline, four within the anticline, and one flat reflector
representing
basement rock. For added difficulty, there is a low velocity layer
between the second and third reflector.
The model was used to do acoustic wave modeling, with the
resulting dataset having 32 meter CMP spacing and 80 offsets spaced
64 meters apart.If we use as our initial estimation of the
slowness, an *s*(*z*) function from outside the anticline,
the reflectors are pulled up
due to using too low a velocity within the anticline (Figure 12).

Figure 11

Figure 12

Following the methodology of Clapp and Biondi (1999) we will begin by considering a regularized tomography problem. We will linearize around an initial slowness estimate and find a linear operator in the vertical traveltime domain between our change in slowness and our change in traveltimes .We will write a set of fitting goals,

(5) |

However, these fitting goals don't accurately describe what we really want. Our steering filters are based on our desired slowness rather than change of slowness. With this fact in mind, we can rewrite our second fitting goal as:

(6) | ||

(7) |

(8) |

4/28/2000