Least-Squares Migration

PGS Least-Squares Migration (LSM) is an imaging technique that can improve images obtained by traditional seismic migration methods. It aims to upgrade migration images by compensating for incomplete or sparse acquisition sampling and assumptions in the original migration algorithm.

PGS LSM can be applied to any images previously migrated using algorithms such as Separated Wavefield Imaging (SWIM), Full Wavefield Migration (FWM), Reverse Time Migration (RTM), Kirchhoff Migration (KPSDM) and Wave Equation Migration (WEM) and can minimize the presence of artifacts, mitigate illumination variations, and increase spatial resolution.

Recovering True Reflectivity with PGS LSM

Like FWI, least-squares migration is an inversion approach but LSM updates the reflectivity rather than a velocity model. It defines the imaging step as a least-squares problem. Consequently, the illumination and resolution of images can be improved by correcting for distortions to the wavefield caused by acquisition limitations and propagation effects.

LSM Fundamentals

  • LSM is an image enhancement tool
  • The seismic reflectivity is updated and high-graded
  • LSM improves amplitude fidelity and spatial resolution in the complex geological settings
  • Images are optimized for reservoir characterization

PGS LSM Benefits

  • Works with all PGS imaging algorithms, and therefore in any environment
  • Produces pre-stack angle domain LSM products to benefit Quantitative Interpretation (QI)
  • Implementation flexibility either iteratively in the data domain, or multi-dimensional deconvolution in the image domain
  • Optimizes PGS leading edge technologies Separated Wavefield Imaging (SWIM) and Full Wavefield Migration (FWM)

The use of LSM (right) in Santos, Brazil, enables improved imaging of postsalt sedimentary geometries and higher resolution of fault patterns in the presalt section.