Chapter introduces the LSJIMP method. The chapter is
divided into three sections. In section
I motivate the
LSJIMP inverse problem in general. Generally, as the non-regularized LSJIMP
inverse problem is underdetermined, it suffers from non-uniqueness. I describe
three model regularization operators which ``steer'' the LSJIMP minimization
toward an optimally crosstalk-free solution which still fits the data. In
Section
, I go on to outline my particular
implementation of LSJIMP. In section
, I show how, in a
laterally-homogeneous earth, to create prestack time-domain images of pegleg
multiples that are directly comparable, both in terms of kinematics and
amplitudes, to the image of the primaries. In a heterogeneous earth, peglegs
``split'' into multiple arrivals. To account for this phenomenon, in section
I introduce the HEMNO (Heterogeneous Earth NMO Operator),
which can independently image each leg of split peglegs in a moderately
heterogeneous earth. HEMNO images with a vertical stretch, sacrificing accuracy
in a complex earth for the efficiency required to make iterative solutions to the
LSJIMP inverse problem computationally tractable.
In Chapter I apply my implementation of LSJIMP to a 2-D
seismic line, donated by WesternGeco, and acquired in the deepwater Mississippi
Canyon region of the Gulf of Mexico. The data exhibit strong surface-related
multiples from a variety of multiple generators, and prove challenging for all
existing methods to attenuate. LSJIMP demonstrates the ability to cleanly
separate multiples and primaries, even in regions with moderate geologic
complexity.
In Chapter I outline the extension of the LSJIMP method to
3-D data. To minimize acquisition costs, most 3-D marine data are sampled quite
sparsely along the crossline source axis, and this sparsity severely hampers some
multiple attenuation methods which otherwise excel in 2-D. Because HEMNO images
multiples with a vertical stretch, my implementation of LSJIMP is more immune
from the crossline sparsity issue.
In Chapter I apply my implementation of LSJIMP to a real
3-D dataset from the Green Canyon region of the Gulf of Mexico. The data were
acquired by CGG and contain surface-related multiples, although they are not as
strong as those seen in the 2-D Mississippi Canyon data. However, the reflectors
in the study area contain fairly strong crossline dips, which challenge many
multiple suppression algorithms when the crossline geometry is sparse. Again, my
implementation of LSJIMP cleanly separates multiples from primaries.
Additionally, this data example showcases LSJIMP's ability to act as an
interpolation operator. Due to fast ship speed, the inline offset resolution of
common midpoint gathers is coarse. I demonstrate how LSJIMP uses the multiples
to simultaneously separate modes, interpolate missing traces, and improve
amplitude analysis. A comparison of LSJIMP with least-squares Hyperbolic Radon
demultiple illustrates that LSJIMP is an effective and computationally tractable
option for 3-D prestack multiple separation.