Interferometric Migration of Passive Data Lab
Figure 1. In this model, 10 shots are randomly deployed
at depth, and 70 geophones are evenly placed along
the surface. The sources emit random white noise
bandlimited by a 30 Hz Ricker wavelet, and the seismograms
recorded at the surface
for 1 second are shown in Figure a).
Objective:
Learn to use the interferometric imaging code for passive data and
explore effect of different parameters on the final image.
Skill Learned:
A version of 2D passive imaging code can be extended for real data imaging.
Lesson learned
- Resolution of interferometric migration of passive data increases with
respect to an increase of number of sources.
- Cross-talk noise in interferometric migration of passive data
increases with
respect to an increase of number of sources.
- Migration of passive multiples has an imaging area comparable
to that of a CDP survey.
Procedure:
- Make a directory, and load into it the file:
auto.m,
dnew.m,
mod.m,
rr1modmig.m,
xcorr.m,
xmig1.m,
source.m,
readata.m,
ex2.m, and
c1.m.
- Type "ex2" in MATLAB to begin migration of correlated
data for 10 buried point sources.
Only ghost reflections with 1 bounce
off the reflectivity interfaces are modeled
with no ray bending.
- Examine migration images for different stack values N of migration stacks.
Does the incoherent noise in the image decrease as N to the 1/2 power?
- Refer to the exercise 7 in Chapter 11. How can the summation over different window segments be changed so that the code runs more efficiently?
Questions to think about:
- What are the advantages and
disadvantages of migrating passive seismic data compared to controlled source data?
- The sources are assumed to be randomly distributed in space and
also have about the same amplitude. What is the effect of a few large
passive sources? Should these data be retained?
- How does one determine the optimal receiver interval and
receiver aperture on the surface?