SEG Workshop: W-18 New Advances in Migration Making the most out of the least (squares migration)

Abstract: Standard migration images can suffer from migration artifacts due to 1) poor source-receiver sampling, 2) weak amplitudes caused by geometric spreading, 3) attenuation, 4) defocusing, 5) poor resolution caused by a limited source-receiver aperture, 6) a ringy source wavelet, and 7) an inaccurate migration velocity model. To partly remedy these problems, least-squares migration (LSM), also known as linearized seismic inversion or migration deconvolution (MD), proposes to linearly invert seismic data for the reflectivity distribution (Nemeth et al, 1999; Duquet et al; 2000). This algorithm is equivalent to linearized waveform inversion (Lailly, 1984) where the smooth background velocity model does not change with iterations. If the background velocity model is sufficiently accurate then LSM can mitigate many of the above cited problems and lead to a more resolved migration image as show