TY - JOUR
T1 - Deblending using the focal transformation with an efficient greedy inversion solver
AU - Cao, Junhai
AU - Kontakis, Apostolos
AU - Verschuur, Dirk Jacob
AU - Gu, Hanming
AU - Li, Lie
PY - 2019
Y1 - 2019
N2 - Nowadays, using simultaneous source seismic acquisition is becoming popular in order to achieve a denser source spacing in an efficient manner. For further processing these data need to be deblended, meaning that they have to be separated in their non-overlapping constituents. In this work, we adopt a novel greedy inversion solver to design a faster version of the double focal transform, which we can use to eliminate blending noise in simultaneous source acquisition. The greedy inversion introduces a coherence-oriented mechanism to enhance focusing of the significant model space, leading to a sparse model space and fast convergence rate. Although the convergence rate of greedy inversion is larger than that of the Spectral Projected Gradient for ℓ1 minimization (SPGL1) solver, the greedy solver is still not fast enough. We propose to use a relative tolerance strategy to speed up the greedy solver: the LSQR (Sparse Equations and Least Squares) algorithm in the inner loop of the greedy solver is stopped when the misfit change between inner iterations is less than a pre-defined relative tolerance value or when it reaches a maximum number of iterations. Synthetics and numerically blended field data examples demonstrate the validity of its application for deblending. We also investigate the effect of random noise on the deblending process, which shows that it is better to apply a denoising process before deblending in order to get an optimum result.
AB - Nowadays, using simultaneous source seismic acquisition is becoming popular in order to achieve a denser source spacing in an efficient manner. For further processing these data need to be deblended, meaning that they have to be separated in their non-overlapping constituents. In this work, we adopt a novel greedy inversion solver to design a faster version of the double focal transform, which we can use to eliminate blending noise in simultaneous source acquisition. The greedy inversion introduces a coherence-oriented mechanism to enhance focusing of the significant model space, leading to a sparse model space and fast convergence rate. Although the convergence rate of greedy inversion is larger than that of the Spectral Projected Gradient for ℓ1 minimization (SPGL1) solver, the greedy solver is still not fast enough. We propose to use a relative tolerance strategy to speed up the greedy solver: the LSQR (Sparse Equations and Least Squares) algorithm in the inner loop of the greedy solver is stopped when the misfit change between inner iterations is less than a pre-defined relative tolerance value or when it reaches a maximum number of iterations. Synthetics and numerically blended field data examples demonstrate the validity of its application for deblending. We also investigate the effect of random noise on the deblending process, which shows that it is better to apply a denoising process before deblending in order to get an optimum result.
KW - Deblending
KW - Focal transformation
KW - Greedy inversion solver
KW - Inversion theory
UR - http://www.scopus.com/inward/record.url?scp=85073704161&partnerID=8YFLogxK
U2 - 10.1016/j.jappgeo.2019.06.007
DO - 10.1016/j.jappgeo.2019.06.007
M3 - Article
AN - SCOPUS:85073704161
SN - 0926-9851
VL - 170
JO - Journal of Applied Geophysics
JF - Journal of Applied Geophysics
M1 - 103791
ER -