1. Processing of the Field Data using Predictive Deconvolution
Yuqing Chen Aydar Zaripov Dias Urozayev
King Abdullah University of Science and Technology
08/12/2015

2. Outline 1) Experiment Description 2) Objective
3) Processing Methodology
4) Results and Interpretation
5) Conclusions

3. Outline 1) Experiment Description 2) Objective
3) Processing Methodology
4) Results and Interpretation
5) Conclusions

4. Area of Study
Seismic profile AB is located along the road between the stadium and the construction site. B A
Construction
Construction

5. Outline 1) Experiment Description 2) Objective
3) Processing Methodology
4) Results and Interpretation
5) Conclusions

6. Objectives: Problem : noise (harmonic)
Solution : Use Prediction deconvolution to eliminate harmonic noise meanwhile compress wavelet (improve resolution)

7. Outline 1) Experiment Description 2) Objective
3) Processing Methodology
4) Results and Interpretation
5) Conclusions

8. Deconvolution Convolutional model (The forward problem):
x(t) – recorded seismogram
w(t) – seismic wavelet
r(t) – reflectivity
Deconvolution (The inverse problem):
e(t) – Earth’s impulse respoones

9. Least Square Deconvolution
Minimize the
misfit function!
x(t) – Recorded seismogram
a(t) – Filter
y(t) – Actually output
d(t) – Desired output

10. Predictive Deconvolution
Noise attenuated
Periodic event (multiple,
harmonic noise)
Prediction Filter 10

11. Predictive Deconvolution
: Predicted time-advanced seismogram
: Actual time-advanced seismogram
x(t) : current and past seismogram
a(t) : Predict Filter
We can predict the predictable part of the seismogram like multiples and harmonic noise, etc.

12. Outline 1) Experiment Description 2) Objective
3) Processing Methodology
4) Results and Interpretation
5) Conclusions

13. Results and Interpretation
3 critical parameter in predictive deconvolution:
(1) Predict length (2) Filter length (3) Whiten noise
A cosine harmonic noise
His autocorrelation map

14. Results and Interpretation
Use the second maximum value of autocorrelation map.

15. Results and Interpretation
Second
maximum

16. Results and Interpretation
Shot120 (Raw data)

17. Results and Interpretation
Shot120 (After bandpass)

18. Results and Interpretation
Shot120 (After PEF_Predict length: 30)

19. Results and Interpretation
Shot120 (After PEF_Predict length: 2)

20. Results and Interpretation
Shot120 (After PEF_Predict length: 60)

21. Results and Interpretation
Shot 40 (After Bandpass)

22. Results and Interpretation
Shot 40 (After PEF)

23. Results and Interpretation
3 critical parameter in predictive deconvolution:
(1) Predict length (2) Filter length (3) Whiten noise
The filter length should at least longer than the predict length
2. We chose the filter length also from the spectrum.
Seismic trace

24. Results and Interpretation50
200
100
300
150
400

25. Results and Interpretation
3 critical parameter in predictive deconvolution:
(1) Predict length (2) Filter length (3) Whiten noise
Whitening 0.01%

26. Results and Interpretation
3 critical parameter in predictive deconvolution:
(1) Predict length (2) Filter length (3) Whiten noise
Whitening 0.05%

27. Conclusion Predictable noise is mitigated Improved temporal resolution
Minimum phase assumption
Random reflectivity assumption
High level of noise restricts the implementation

28. Recommendations 2D predictive deconvolution
Compute different PEF’s for different segments of a seismogram
Change seismogram to minimum phase before using PEF