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

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.