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Seismic Reflection: Processing and Interpretation PowerPoint PPT Presentation


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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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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


Defining Geometry


Processing Steps


Killing Traces

  • Vibroseis signal had to be eliminated

  • Channels showing large amounts of noise were deleted


Processing Steps


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


Deconvolution Equation

Robertson 1998


To Conceptualize. . .

INPUT

OUTPUT

FILTER

=

*


Deconvolution Results

Location 109

Location 109

Time (ms)

Time (ms)

NO DECONVOLUTION

DECONVOLVED


Processing Steps


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


Butterworth Filtering

Location 109

Location 109

Time (ms)

Time (ms)

No Filter

Butterworth Filter


Processing Steps


Processing Steps


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


Processing Steps


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


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


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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