### Seismic Imaging and Velocity Analysis: Current Status and Future
Directions

Paul Stoffa

University of Texas, Austin

Seismic data are used to infer subsurface structures and reservoir properties.
Current data acquisition techniques employ 12 or more receiving arrays of up
to 6.0 km in length with sources of seismic energy activated every 50m or less.
These 3D surveying methods challenge traditional seismic data processing and
imaging methods in at least two ways.

First, the data volumes recorded require computationally efficient yet effective
algorithms. Second, the excellent survey coverage and the desire for accurate
subsurface images make wave equation based depth imaging not just possible but
a requirement. To achieve this goal, the seismic propagation velocity must be
derived from the recorded data as part of the imaging process. These challenges
can be addressed by decomposing the recorded data into its plane wave constituents.
This reduces the data volume since appropriate subsets of the recorded information
can be used in the imaging and velocity analysis.

In addition, many triplications observed in conventional offset-time domain
are unraveled in the plane wave data. Classical integral methods for seismic
imaging, known as Kirchhoff migration, have plane wave analogs that are computationally
efficient and accurate. Plane wave frequency-wavenumber perturbation methods
can be formulated that have better resolving power when lateral velocity variations
are small. Finite-difference approximations can also be employed. Velocity estimation
is a non-linear optimization problem that is well suited for directed Monte
Carlo methods such as very fast simulated annealing.

By evaluating the coherency of variable incidence angle image estimates for
each subsurface point, an objective function can be formed and used to update
the velocity model. Rapid imaging algorithms acting on selected subsets of plane
wave data are one approach to solving the velocity estimation problem, which
is a prerequisite for accurate 3D depth imaging.

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