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Making Marchenko imaging work with field data and the bumpy road to 3D
Alex Jia*, Antoine Guitton, and Roel Snieder Center for Wave Phenomena, Colorado School of Mines
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RTM Marchenko Imaging pros whole model
easy implementation
target orientated remove artifacts by
internal multiples cons artifacts by internal
multiples
require wavelet learning curve (e.g.
acquisition geometry)
outline
‣ Marchenko imaging: how and why?
‣ Complications: ‣ missing near offsets
‣ data calibration
‣ Why is 3D Marchenko necessary and challenging?
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Marchenko equations
background velocity model
surface data
surface response to virtual source
(reverse VSP data) first arrival
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Marchenko equations
background velocity model
surface data
up-going and down-going
Green’s function first arrival
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Marchenko equations
background velocity model
surface data
up-going and down-going
Green’s function
Marchenko image
first arrival
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G+ 1
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3
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G-
a
b
missing near offsets
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missing near offsets: 7 traces, 214 m
11 full offset missing near offsets difference
retrieved Green’s function
missing near offset
‣ stronger for shallow events and near offset events
‣ maximum missing near offset
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D: depth of reflector T: dominant wave period assumption: hyperbolic assumption for flat multi-layered earth (details in CWP report)
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14 full offset missing near offsets RTM
data calibration
‣ input 1: surface reflection response
‣ input 2 : first arrival
‣ amplitude should match
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retrieved Green’s function
16 scale=0.5 scale=1 scale=1.5
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data calibration
18 scale=0.5 scale=1 scale=1.5
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001
0.00
1
first arrival
synthetic
field *
20 0.
001
0.00
1
first arrival
synthetic
field (scaled) *
2D can be deceptive
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Green’s function in 3D structure
x=0.5, y=1, z=0.5 x=0.5, y=1, z=0
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retrieved
x=0.5, y=1, z=0.5 x=0.5, y=1, z=0
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retrieved
x=0.5, y=1, z=0.5 x=0.5, y=1, z=0
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retrieved
modelled
x=0.5, y=1, z=0.5 x=0.5, y=1, z=0
3D Marchenko
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(http://www.prism-exploration.com/seismic-ops) (Wang et al. 2009)
ocean bottom data streamer data (dense in-line, sparse x-line)
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