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Seismic Reflection: Processing and Interpretation

Seismic Reflection: Processing and Interpretation. Katie Wooddell UW Madison. Objectives. Develop a velocity model of the Santa Fe River Canyon to aid in structure interpretation Use predictive deconvolution to remove multiple reflections from the data . Processing Steps. Processing Steps.

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Seismic Reflection: Processing and Interpretation

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  1. Seismic Reflection:Processing and Interpretation Katie Wooddell UW Madison

  2. Objectives • Develop a velocity model of the Santa Fe River Canyon to aid in structure interpretation • Use predictive deconvolution to remove multiple reflections from the data

  3. Processing Steps

  4. Processing Steps

  5. Defining Geometry

  6. Processing Steps

  7. Killing Traces • Vibroseis signal had to be eliminated • Channels showing large amounts of noise were deleted

  8. Processing Steps

  9. Predictive Deconvolution What does it do? Clears seismic data by predicting and eliminating multiple reflections How does it work? A filter is designed that recognizes and eliminates repetitions in the signal

  10. Deconvolution Equation Robertson 1998

  11. To Conceptualize. . . INPUT OUTPUT FILTER = *

  12. Deconvolution Results Location 109 Location 109 Time (ms) Time (ms) NO DECONVOLUTION DECONVOLVED

  13. Processing Steps

  14. Butterworth Filter • Bandpass Filter • Passes frequencies in the range of the vibroseis sweep (30-300 Hz) • Effective in eliminating the noise amplified during predictive deconvolution

  15. Butterworth Filtering Location 109 Location 109 Time (ms) Time (ms) No Filter Butterworth Filter

  16. Processing Steps

  17. Processing Steps

  18. NMO Correction • NMO corrects for the delay in first arrival times • NMO shifts the arrival times according to a specific stacking velocity • This is an important step in the velocity analysis

  19. Processing Steps

  20. Velocity Analysis • A Constant Velocity Analysis was conducted on both deconvolved and non-deconvolved data • The following NMO stacking velocities were run for each situation: - 1000 m/s -1200 m/s - 1400 m/s -1600 m/s - 1800 m/s -2000 m/s - 2200 m/s -2400 m/s

  21. Conclusions • Predictive deconvolution is an effective tool for eliminating multiples from seismic data • A combination of the 2000 m/s and the 1600 m/s constant velocity models seems to produce a high resolution model • This implies a higher average velocity medium on the east side of the fault, giving evidence that the normal La Bajada fault is being imaged

  22. And a Special Thanks To. . . The SAGE professors for their knowledge and patience Team 5 for their hard work and some good times Michelle Herrera, my Processing partner Lauren Larkin, who was more enthusiastic about splicing together my seismic sections than I was

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