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The Experimental Comparison of Conventional and Differential Semblance on several data sets. Jintan Li Rice University. Outline. Project Goal Experiments and Results Conclusion. Project Goal. - PowerPoint PPT Presentation
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The Experimental Comparison of Conventional and Differential
Semblance on several data sets
Jintan LiRice University
Project Goal
Experiments and Results
Conclusion
Outline
To assess factors affecting ease of use, accuracy, and reliability of NMO-based DSO using synthetic and field data sets
Project Goal
In this talk, I am going to:
* overview use of package* illustrate effect of multiple reflections on DSO velocity estimate* demonstrate package ability to use multiple CMPs (2D data)
General Idea
Conventional velocity analysis method* Manually or automatically picked a collection of trial
velocities; allowing one to seek picks in the data
DS velocity analysis method* Automated: stops when the iteration goes to the
final interval velocity, whose corresponding RMS velocity flattens the hyperbola; one does not have to seek picks
manually
NMO DSO requires
data in SU format with critical header words correctly defined (cdp,offset,sx,gx…)
• initial interval velocity in PIGrid format ( 1, 2 or 3 D)• velocity bin radius - defines cell size in velocity/CMP
grid all traces with CMP in given cell moved out with interval velocity at center of cell (SMPL output)
• upper and lower bound velocities defining feasible set for search, also in PIGrid format (optional - defaults are +/- 10% of initial velocity)
• various other optional parameters, can usually be left at default values
How to build an initial estimated interval velocity for DS method
i. PIGrid velocityii. Choose the right grid and the presumably ri
ght controlling points with reasonable depth and interval velocity
iii.Choose reasonable velocity variation rangeiv.A linear velocity model is a first good guess
Experiments with synthetic data, single CMP
use acoustic 2D constant density linearized simulator program to obtain a shot gather
CMP gather
use DS method to find the most accurate reference velocity model given the initial estimated velocity within a certain variation range until the hyperbolic reflection is flattened
Model 1 Marmousi Velocity Model
We sliced the velocity model at the offset 5600m
Born vs. full waveform data for v(z) from Marmousi
After NMO correction using DS
We can see the difference of these two clearly. Some part of the CMP gather on the right is over-corrected and some is
under-corrected .
Comparison of DSO-estimated RMS velocity with velocity scan
. slow events, likely pegleg multiples, are present and that DSO-based RMS velocity appears to seek compromise between primary and multiple moveout velocities, just as with synthetics
Tim
e(s)
From this experiment, we can get:
Synthetic experiments: NMO DSO very accurate with primaries-only data, accuracy degrades as multiple reflection energyincreases.General pattern: with conflicting moveout peaks, DSO finds intermediate path, overcorrecting some events and under correcting others
Model 2 — a typical CMP
Choose the first CMP gather as followings:
After DSO
A typical DSO-based NMO corrected CMP
The DS estimated RMS velocity compared to the conventional velocity analysis:
Conclusion:
* differential semblance works as well with field data as with synthetic data;
* in both cases multiple reflection energy degrades accuracy
* use of standard data formats eases problem setup, manipulation of data before and after inversion using SU.