1. Migration Velocity Analysis of Multi-source Data Xin Wang January 7, 2010 01

2. Outline  Motivation Estimate an accurate background velocity model for multi-source migration Tomographic migration velocity analysis method and least squares migration of multi-source data. Synthetic tests of 2, 10 and 40 shot supergather on part of the 2D SEG/EAGE overthrust model. 02  Theory  Numerical Results  Conclusions

3. Motivation 03 Inversion methods are sensitive to the background velocity model. Seismic Data Z (km) 1.5 0 X (km) 0 6.4 True Velocity Z (km) 1.5 0 X (km)6.4 Incorrect Velocity Z (km) 1.5 0 X (km)6.4 KM image Z (km) 1.5 0 X (km) 0 6.4 KM image Z (km) 1.5 0 Offset (km) 0 2 CIG Z (km) 1.5 0 0 CIG Offset (km)2 Time (s) 3 0 Trace # 1 160 km/s 9 6 m/s 9.5 6.5 0 0

4. Motivation 04 Multi-source technique reduces the economic cost of seismic acquisition. Migration and stacking can suppress the cross-talk noise. (Hampson 2008, and Dai 2009). Single CSG 1 1 129 Trace # 0 Time (s) 8 Single CSG 2 1 129 Trace # 0 Time (s) 8 0 8 1 129 Trace # Supergather Goal: can we apply the MVA method to multi-source data ? Z (km) 1.3 0 X (km) 0 5.8 KM Image of 2 Shot Supergather Z (km) 1.3 0 X (km) 0 5.8 LSM Image of 320 Shot Supergather

5. Outline  Motivation Estimate an accurate background velocity model for multi-source migration Tomographic migration velocity analysis method and least squares migration of multi-source data. Synthetic tests of 2, 10 and 40 shot supergather on part of the 2D SEG/EAGE overthrust model. 05  Theory  Numerical Results  Conclusions

6. Theory 06 Layer stripping (Lafond, 1993) Shallow events are first flattened by updating shallow velocity layers by MVA, then deeper ones are flattened by updating deeper velocity. Hyperbolic approximation picking depth, Depth residual With the eikonal solver, convert depth residual to time residual Z (km) 1.5 0 Offset (km)1 CIG Z (km) 1.5 0 CIG Offset (km)1 Z0Z0 ZhZh h zero-offset depth Tomographic MVA reference depth

7. Theory 07 Traveltime tomography For a small slowness perturbation Parameterize the model as a grid of cells Update the slowness with a steepest descent method traveltime, raypath operator, background slowness. traveltime residual for the raypath,slowness purturbation in grid cell Back projectalong the raypaths to get

8. Theory 08 Misfit function This value is used to help to determine when the iteration will stop. With a large number of multi-source, least squares migration is used to suppress the crosstalk noise to provide a better image. picked depth residual for offset With a small number of multi-source, Kirchhoff migration is used in CIGof the iteration Multi-source data delay operator

9. 09 LSM for large N, form CIGs Migration velocity model s 0 Theory Predict travel time by eikonal solver KM for small N, form CIGs Pick the reflector position from zero-offset and near-offset Find ray paths connecting the reflector to both S and R positions Convert depth residual to travel time residual Update velocity model by back projecting the traveltime residuals along the raypaths. Start from the top curved event. Flattened ? Y Work Flow: Go to the next curved event. All events are flattened? Y MVA finished ! Migration velocity model s k Pick the depth residual automatically

10. Outline  Motivation Estimate an accurate background velocity model for multi-source migration Tomographic migration velocity analysis method and least squares migration of multi-source data. Synthetic tests of 2, 10 and 40 shot supergather on part of the 2D SEG/EAGE overthrust model. 10  Theory  Numerical Results  Conclusions

11. Numerical Results 11 Part of 2D SEG/EAGE Overthrust model Size: 200 X 100 Interval: 25m Rick wavelet: central frequency 30 Hz, total source number: 200 2D SEG/EAGE Overthrust Model 0 Depth (km) 4. 5 0 X (km)10 0.11 -0.11 True Velocity Model Inaccurate Velocity Model Reflectivity Model Velocity Difference 4.9 2.2 0 Depth (km) 2. 5 0X (km)10 0 X (km)10 0 Depth (km) 2. 5 0 Depth (km) 2. 5 0 Depth (km) 2. 5 0 X (km)10 4.9 2.2 km/s 0 -400 m/s

12. Numerical Results 12 2 Shot Supergather 0 Depth (km) 2. 5 05 X (km) Initial Velocity model 0 Depth (km) 2. 5 05 X (km) KM of 2 Shot Supergather 0. 6 Depth (km) 2. 5 03 Offset (km) CIG of KM with Initial Velocity 0. 6 Depth (km) 2. 5 03 Offset (km) CIG of KM with Updated Velocity 0 Depth (km) 2. 5 05 X (km) Updated Velocity after Three Iterations of MVA 0 Depth (km) 2. 5 05 X (km) KM of 2 Shot Supergather km/s 4.9 2.2 km/s 4.9 2.2

13. Numerical Results 13 2 Shot Supergather 0 Depth (km) 2. 5 05 X (km) Initial Velocity Difference 0 Depth (km) 2. 5 05 X (km) Updated Velocity Difference after Two Iterations of MVA 05 X (km) 0 Depth (km) 2. 5 05 X (km) Updated Velocity Difference after One Iterations of MVA m/s 0 500 Updated Velocity Difference after Three Iterations of MVA m/s 0 500 m/s 0 500 m/s 0 500 0 Depth (km) 2. 5

14. Numerical Results 14 10 Shot Supergather 0 Depth (km) 2. 5 05 X (km) Initial Velocity Model 0 Depth (km) 2. 5 05 X (km) KM of 10 Shot Supergather 0. 6 Depth (km) 2. 5 03 Offset (km) CIG of KM with Initial Velocity 0. 6 Depth (km) 2. 5 03 Offset (km) CIG of KM with Updated Velocity 0 Depth (km) 2. 5 05 X (km) Updated Velocity after Three Iterations of MVA 0 Depth (km) 2. 5 05 X (km) KM of 10 Shot Supergather km/s 4.9 2.2 km/s 4.9 2.2

15. Numerical Results 15 10 Shot Supergather 0 Depth (km) 2. 5 05 X (km) Initial Velocity difference 0 Depth (km) 2. 5 05 X (km) Updated Velocity Difference after Two Iterations of MVA 05 X (km) 0 Depth (km) 2. 5 05 X (km) Updated Velocity Difference after One Iterations of MVA Updated Velocity Difference after Three Iterations of MVA 0 Depth (km) 2. 5 m/s 0 500 m/s 0 500 m/s 0 500 m/s 0 500

16. 0 Depth (km) 2. 5 05 X (km) Initial Velocity Model 0 Depth (km) 2. 5 05 X (km) LSM of 40 Shot Supergather 0. 6 Depth (km) 2. 5 03 Offset (km) CIG of LSM with Initial Velocity 0. 6 Depth (km) 2. 5 03 Offset (km) CIG of LSM with Updated Velocity 0 Depth (km) 2. 5 05 X (km) Updated Velocity Model after Three Iterations of MVA 0 Depth (km) 2. 5 05 X (km) LSM of 40 Shot Supergather 16 Numerical Results 40 Shot Supergather km/s 4.9 2.2 km/s 4.9 2.2

17. Numerical Results 17 40 Shot Supergather 0 Depth (km) 2. 5 05 X (km) Initial Velocity Difference 0 Depth (km) 2. 5 05 X (km) Updated Velocity Difference after Two Iterations of MVA 05 X (km) 0 Depth (km) 2. 5 05 X (km) Updated Velocity Difference after One Iterations of MVA Updated Velocity Difference after Three Iterations of MVA 0 Depth (km) 2. 5 m/s 0 760 m/s 0 760 m/s 0 760 m/s 0 760

18. Numerical Results 18 40 Shot Supergather LSM Data Residual with Updated Velocity by MVA LSM Iteration Number Normalized Data Residual 0. 4 1. 0 1 15 Depth Residual in CIG Normalized Depth Residual MVA Iteration Number 0 1. 0 0 3

19. Outline  Motivation Estimate an accurate background velocity model for multi-source migration Tomographic migration velocity analysis method and least squares migration of multi-source data. Synthetic tests of 2, 10 and 40 shot supergather on part of the 2D SEG/EAGE overthrust model. 19  Theory  Numerical Results  Conclusions

20. 20 Tomographic MVA can be applied to multi-source seismic data. MVA by KM is efficient with a small number of multi-source. With a large number of multi-source, LSM is preferred. After several iterations of updating, the depth residual reduces to almost zero, although the updated velocity is still not completely true. Conclusions When the multi-source number becomes to even larger, the crosstalk in CIG prevents the MVA method.

21. 21 Future Work: Conclusions Improve the LSM result with proper preconditioners (deblurring filter). Apply some filters (F-K filter, slant-stacking and median filter) to remove the incoherent noise in CIG for large multi-source number. Use the reflection ray tracing method for dipping events. Develop MVA for complicated synthetic model.

22. We would like to thank the UTAM 2009 sponsors for their support. Thank You 22 Acknowledgement