Least-squares Reverse Time Migration (LS-RTM) provides amplitude recovery, high resolution and artifacts attenuation, at the cost of one migration/demigration pair for each iteration. Hence, the so called single-iteration approaches represent a cost-effective approximation of LS-RTM. We propose a single-iteration LS-RTM, where the approximate Hessian operator is estimated as a matching filter. Based on the consideration that subsurface reflectivity is sparse, the estimated filter is given as input to a sparse inversion. The proposed method is then illustrated through synthetic and field data examples.

Approximate Least Squares RTM via matching filters and regularized inversion

Lipari V.;Picetti F.;Panizzardi J.;Bienati N.;Tubaro S.
2020

Abstract

Least-squares Reverse Time Migration (LS-RTM) provides amplitude recovery, high resolution and artifacts attenuation, at the cost of one migration/demigration pair for each iteration. Hence, the so called single-iteration approaches represent a cost-effective approximation of LS-RTM. We propose a single-iteration LS-RTM, where the approximate Hessian operator is estimated as a matching filter. Based on the consideration that subsurface reflectivity is sparse, the estimated filter is given as input to a sparse inversion. The proposed method is then illustrated through synthetic and field data examples.
SEG International Exposition and Annual Meeting 2019
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1146094
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