
TY  - JOUR
A1  - Plasterie, P. 
A1  - Gayne, M. 
A1  - Lange, M. 
A1  - Sarjono, I. 
A1  - Pica, A. 
A1  - Bril, R. 
A1  - Faulkner, C. 
A1  - Mosher, C. 
T1  - Shallow water 3D surface-related multiple modelling an Australian waters case study
JF  - ASEG Extended Abstracts
JO  - ASEG Extended Abstracts
Y1  - 2009
VL  - 2009
IS  - 1
SP  - 1
EP  - 7
KW  - 
UR  - https://doi.org/10.1071/ASEG2009AB007 
N2  - Although in theory geophysicists should not apply multiple removal before surface related multiple attenuation, modern marine processing sequences include predictive deconvolution before or after SRME / SRMM. In practice the two methods complement each other well, predictive deconvolution tackles very short period multiples in very shallow water environments while SRME, although supposed to handle any multiples in the data, will thrive in slightly longer multiple period. The reason for the failure of the SRME / SRMM techniques in shallow water environments is that the convolution process between the primary response and the total response is jeopardized because the primary response (the water bottom) is incompletely recorded. Indeed, due to missing near offsets (near offsets are in the order of 150m), most of the primary water bottom reflection is not recorded, and in addition might interfere with the direct arrival wave (Verschuur, 2006). At least it is not sufficiently accurately recorded for the SRME convolution to successfully predict multiples for small offsets. Predictive deconvolution, on the other hand, is known to be very effective as it relies only on the periodicity of the multiples wave field and not on the water bottom reflection itself. In the first part of this case study we look at the relative effectiveness of 2D SRME / 3D SRMM compared with predictive devonvolution. 
ER  -

