33 research outputs found
Target-oriented least-squares reverse-time migration using Marchenko double-focusing: reducing the artifacts caused by overburden multiples
Geophysicists have widely used Least-squares reverse-time migration (LSRTM)
to obtain high-resolution images of the subsurface. However, LSRTM needs an
accurate velocity model similar to other migration methods. Otherwise, it
suffers from depth estimation errors and out of focus images. Moreover, LSRTM
is computationally expensive and it can suffer from multiple reflections.
Recently, a target-oriented approach to LSRTM has been proposed, which focuses
the wavefield above the target of interest. Remarkably, this approach can be
helpful for imaging below complex overburdens and subsalt domains. Moreover,
this approach can significantly reduce the computational burden of the problem
by limiting the computational domain to a smaller area. Nevertheless,
target-oriented LSRTM still needs an accurate velocity model of the overburden
to focus the wavefield accurately and predict internal multiple reflections
correctly. In recent years, Marchenko redatuming has emerged as a novel
data-driven method that can predict Green's functions at any arbitrary depth,
including all orders of multiples. The only requirement for this method is a
smooth background velocity model of the overburden. Moreover, with Marchenko
double-focusing, one can make virtual sources and receivers at a boundary above
the target and bypass the overburden. This paper proposes a new algorithm for
target-oriented LSRTM, which fits the double-focused data with modeled data at
a boundary above the target of interest. Consequently, our target-oriented
LSRTM algorithm correctly accounts for all orders of overburden-related
multiples, resulting in a significant reduction of the artifacts caused by
overburden internal multiple reflections in the target image compared to
conventional LSRTM.Comment: This preprint is submitted to Geophysical Journal International and
is under review as of this momen
Target-Enclosed Least-Squares Seismic Imaging
Least-Squares Reverse-Time Migration (LSRTM) is a method that seismologists
utilize to compute a high-resolution subsurface image. Nevertheless, LSRTM is a
computationally demanding problem. One way to reduce the computational costs of
the LSRTM is to choose a small region of interest and compute the image of that
region. However, finding representations that account for the wavefields
entering the target region from the surrounding boundaries is necessary. This
paper confines the region of interest between two boundaries above and below
this region. The acoustic reciprocity theorem is employed to derive
representations for the wavefields at the upper and lower boundaries of the
target region. With the help of these representations, a target-enclosed LSRTM
algorithm is developed to compute a high-resolution image of the region of
interest. Moreover, the possibility of using virtual receivers created by
Marchenko redatuming is investigated
Target-oriented least-squares reverse-time migration with Marchenko redatuming and double-focusing: Field data application
Recently, the focus of reflection seismologists has shifted to applications
where a high-resolution image of the subsurface is required. Least-Squares
Reverse-Time Migration (LSRTM) is a common tool used to compute such images.
Still, its high computational costs have led seismologists to use
target-oriented LSRTM for imaging only a small target of interest within a
larger subsurface block. Redatuming the data to the upper boundary of the
target of interest is one approach to target-oriented LSRTM. Still, many
redatuming methods cannot account for multiple scatterings within the
overburden. This paper presents a target-oriented least-squares reverse time
migration algorithm which integrates Marchenko redatuming and double-focusing.
This special redatuming method accounts for all orders of multiple scattering
in the overburden for target-oriented LSRTM. Additionally, the paper
demonstrates that a double-focusing algorithm can further reduce the size of
the data by reducing both spatial and temporal dimensions. This algorithm is
applied to field data acquired in the Norwegian Sea.Comment: This preprint has been submitted to Geophysics journal for
peer-revie