The Experimental Comparison of Conventional and Differential Semblance on several data sets

1. The Experimental Comparison of Conventional and Differential Semblance on several data sets Jintan Li Rice University

2. Outline • Project Goal • Experiments and Results • Conclusion

3. Project Goal To assess factors affecting ease of use, accuracy, and reliability of NMO-based DSO using synthetic and field data sets 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)

4. 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

5. 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

6. How to build an initial estimated interval velocity for DS method • PIGrid velocity • Choose the right grid and the presumably right controlling points with reasonable depth and interval velocity • Choose reasonable velocity variation range • A linear velocity model is a first good guess

7. 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

8. Model 1 Marmousi Velocity Model We sliced the velocity model at the offset 5600m

9. Born vs. full waveform data for v(z) from Marmousi

10. 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 .

11. Comparison of DSO-estimated RMS velocity with velocity scan Time(s) .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

12. 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

13. Model 2— a typical CMP

14. Choose the first CMP gather as followings:

15. After DSO

16. A typical DSO-based NMO corrected CMP

17. The DS estimated RMS velocity compared to the conventional velocity analysis:

18. 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.