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UID:194@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20140328T140000
DTEND;TZID=Europe/Paris:20140328T150000
DTSTAMP:20240524T072505Z
URL:https://www.i2m.univ-amu.fr/evenements/m-quyen-pham-ifpen-paris-proxim
 al-methods-for-multiple-removal-in-seismic-data-2/
SUMMARY: (...): M. Quyen Pham (IFPEN\, Paris): Proximal methods for multipl
 e removal in seismic data
DESCRIPTION:: Proximal methods for multiple removal in seismic data by Mai 
 Quyen Pham (IFPEN\, Paris.nnJoint work Caroline Chaux\, Laurent Duval and 
 Jean-Christophe PesquetnnAbstract:nDuring the acquisition of seismic data\
 , undesirable coherent seismic events such as multiples\, are also recorde
 d\, often resulting in a degradation of the signal of interest [1]. The co
 mplexity of these data has historically contributed to the development of 
 several efficient signal processing tools\; for instance complex wavelet t
 ransforms [2\,3] or robust l1-based sparse restoration [4]. The objective 
 of this work is to propose an original approach to the multiple removal pr
 oblem. A variational framework is adopted here\, but instead of assuming s
 ome knowledge on the kernel\, we assume that templates are available. Cons
 equently\, it turns out that the problem reduces to estimate Finite Impuls
 e Response filters\, the latter ones being assumed to vary slowly along ti
 me.nWe assume that the characteristics of the signal of interest are appro
 priately described through a prior statistical model in a frame of signals
 \, e.g. a wavelet basis. The data fidelity term thus takes into account th
 e statistical properties of the frame coefficients (one can choose an l1-n
 orm to induce sparsity)\, the regularization term models prior information
 s that are available on the filters and a last constraint modelling the sm
 ooth variations of the filters along time is added.nIn particular\, the pr
 oblem is completely reformulated as a constrained minimization problem\, i
 n order to simplify the determination of data-based parameters\, as compar
 ed with our previous regularized approach involving hyper-parameters.nThe 
 resulting minimization is achieved by using recent primal-dual proximal ap
 proaches [5].nnReferencesn[1] S. Ventosa\, S. Le Roy\, I. Huard\, A. Pica\
 , H. Rabeson\, P. Ricarte\, and L. Duval. Adaptive multiple subtraction wi
 th wavelet-based complex unary Wiener filters. Geophysics\, 77(6):V183–V
 192\, Nov.-Dec. 2012.n[2] J. Morlet\, G. Arens\, E. Fourgeau\, and D. Giar
 d. Wave propagation and sampling theory\, part I: Complex signal and scatt
 ering in multilayered media. Geophysics\, 47(2):203–221\, 1982.n[3] J. M
 orlet\, G. Arens\, E. Fourgeau\, and D. Giard. Wave propagation and sampli
 ng theory\, part II: Sampling theory and complex waves. Geophysics\, 47(2)
 :222–236\, 1982.n[4] J. F. Claerbout and F. Muir. Robust modeling with e
 rratic data. Geophysics\, 38(5):826–844\, Oct. 1973.n[5] P. L. Combettes
  and J.-C. Pesquet. Primal-dual splitting algorithm for solving inclusions
  with mixtures of composite\, Lipschitzian\, and parallel-sum type monoton
 e operators. Set-Valued Var. Anal.\, 20(2):307–330\, Jun. 2012.
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