Conventional Kirchhoff migration often suffers from artifacts such as aliasing and acquisition footprint, which come from sub-optimal seismic acquisition. The footprint can masks faults and fractures, while aliased noise can focus into false coherent events which affect interpretation and contaminate AVO, AVAz and elastic inversion. Preconditioned least-squares migration minimizes these artifacts.

We can implement least-squares migration by minimizing the difference between the original data and the modeled demigrated data using an iterative conjugate gradient scheme. Unpreconditioned least-squares migration better estimates the subsurface amplitude, but does not suppress aliasing. In this paper, we precondition the results by applying a structure-oriented prestack LUM filter to each common offset and common azimuth gather at each iteration. The preconditioned algorithm suppresses aliasing of both signal and noise, and improves the convergence rate.

We apply the new preconditioned least-squares migration to a survey acquired over a new resource play in the Mid-Continent, USA. Acquisition footprint in shallow targets is attenuated and the signal-to-noise ratio is enhanced. To demonstrate the impact on interpretation, we generate a suite of seismic attributes to image the Mississippian limestone, and show that karst-enhanced fractures in the Mississippian limestone can be better illuminated.

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