This book describes the theory and practice of inverting seismic data for the subsurface rock
properties of the earth. The primary application is for inverting reflection and/or transmission …

By fitting observed data with predicted seismograms, least‐squares migration (LSM)
computes a generalized inverse for a subsurface reflectivity model, which can improve …

Least-squares migration (LSM) can be effective to mitigate the limitation of finite-seismic
acquisition, balance the subsurface illumination, and improve the spatial resolution of the …

Compared with the traditional adjoint migration, the least-squares migration (LSM) can
effectively mitigate the unbalanced illumination and limited resolution associated with finite …

Compared with traditional adjoint-based migration, least-squares migration (LSM) can
reduce finite-frequency effects, remove acquisition footprints and improve spatial resolution …

This study derives a preconditioned stochastic conjugate gradient (CG) method that
combines stochastic optimization with singular spectrum analysis (SSA) denoising to …

Standard migration images can suffer from (1) migration artifacts caused by an
undersampled acquisition geometry,(2) poor resolution resulting from a limited recording …

Assuming that an accurate background velocity is obtained, least-squares migration (LSM)
can be used to estimate underground reflectivity. LSM can be implemented in either the data …

The acquisition footprint, finite-frequency source, and unbalanced subsurface illumination
make it difficult for traditional adjoint-based migration to produce a high-quality image for …

Diffraction imaging is important for high-resolution characterization of small subsurface
heterogeneities. However, due to geometry limitations and noise distortion, conventional …