making marchenko imaging work with field data and the bumpy … · 2019. 8. 31. · 5 marchenko...

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

3

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?

4

5

Marchenko equations

background velocity model

surface data

surface response to virtual source

(reverse VSP data) first arrival

6

Marchenko equations


surface data

up-going and down-going

Green’s function first arrival

7

Marchenko equations


surface data

up-going and down-going

Green’s function

Marchenko image

first arrival

8

G+ 1

2

3

9

G-

a

b

missing near offsets

10

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

12

D: depth of reflector T: dominant wave period assumption: hyperbolic assumption for flat multi-layered earth (details in CWP report)

14 full offset missing near offsets RTM

data calibration

‣ input 1: surface reflection response

‣ input 2 : first arrival

‣ amplitude should match

15

retrieved Green’s function

16 scale=0.5 scale=1 scale=1.5

data calibration

18 scale=0.5 scale=1 scale=1.5

19 0.

001

0.00

1

first arrival

synthetic

field *

Presenter

Presentation Notes

The question you may ask is how we calibrate the field data? Because we have a background velocity model and we use this model to construct our first arrival. We can also use this model to simulate surface data with amplitudes that can match the first arrival. Thus, we now have the simulated surface data that has the required amplitude, and we have the field data we need to calibrated. Hence, we can obtain a scaling factor by comparing the amplitude the field data and modelled data, and use this scaling factor to calibrate the field data.

20 0.

001

0.00

1

first arrival

synthetic

field (scaled) *

2D can be deceptive

21

22

Green’s function in 3D structure

x=0.5, y=1, z=0.5 x=0.5, y=1, z=0

Presenter

Presentation Notes

Goal: retrieve the Green’s function from a VS at (x=0.5, y=1, z=0.5) to a surface receiver at (x=0.5, y=1,z=0)

23

retrieved

x=0.5, y=1, z=0.5 x=0.5, y=1, z=0

24

retrieved

x=0.5, y=1, z=0.5 x=0.5, y=1, z=0

25

retrieved

modelled

x=0.5, y=1, z=0.5 x=0.5, y=1, z=0

3D Marchenko

26

(http://www.prism-exploration.com/seismic-ops) (Wang et al. 2009)

ocean bottom data streamer data (dense in-line, sparse x-line)

making marchenko imaging work with field data and the bumpy … · 2019. 8. 31. · 5 marchenko...

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