An l2 solution for the reflection angles can be estimated directly
from the reflection data D by substituting the solution (16)
into the normal equation
.
Unfortunately, I have not yet completed the required analysis.
A difficulty arises from the fact that the kernel functions of (5)
are nonlinearly related to
.
However, in the meantime an
efficient ad hoc solution is available based on physical intuition.
Consider a fixed point in the subsurface. As we migrate a constant
offset section into
, the angle
between the source
and receiver rays ranges from
at
, to
near the specular midpoint, and back to
at
. Analogously, the differential
reflection coefficient
varies from
(diffraction) to
(specular reflection), to
(diffraction) again, over the same midpoint integration range.
Hence, it is apparent that
will
attain a maximal peak amplitude at the specular midpoint, whereupon
and
.
This physical argument suggests performing a first moment weighted estimate
of
as follows:
![]() |
(17) |
![]() |
(18) |
![]() |
(19) |
where is another damping parameter, and
is a function
which can be arbitrarily chosen to optimize the estimate. In practice,
choosing f to be a low power of the cosine function works well, such that
. I use
.It should be noted that the estimate (17) is very similar to the
result of Bleistein (1987) for
, except that the slightly
different WKBJ weighting and the absolute value signs may add a certain
robustness advantage, especially at subsurface points
where
tends to be small or zero.
Finally, the two estimates and
can
be mapped uniquely to the desired output
, which
completes the least-squares angle-dependent reflectivity estimation process.