Applications of Time-Domain Multiscale Waveform Tomography to Marine and Land Data

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Applications of Time-DomainMultiscale Waveform
Tomographyto Marine and Land Data
C. Boonyasiriwat1, J. Sheng3, P. Valasek2, P.
Routh2, B. Macy2, W. Cao1, and G.T. Schuster1
1 Department of Geology and Geophysics,
University of Utah 2 Seismic Technology
Development, ConocoPhillips 3 Formerly University
of Utah, Currently at Nexus Geoscience
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Outline

• Introduction
• Time-domain multiscale waveform tomography
• Processing workflow
• Field data results
• Gulf of Mexico
• Saudi Arabia
• Summary

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Waveform Tomography

• Wave-equation based model building technique.

Boonyasiriwat et al., 2008
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Constant Density Acoustic Inversion Result
True Velocity
(Good) Initial Velocity Model
Reconstructed Velocity Model
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Marmousi II Key Learnings

• Acoustic inversion of elastic data can provide an
  accurate result at shallow depths.
• Kinematics are correctly predicted but amplitudes
  are not.
• The elastic effect is accumulated and degrades
  the inversion result (accuracy and resolution)
  with increasing depth.

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Problem and Solution
Problem Find a velocity model from seismic data
that minimizes the data residual
Proposed Solution - Use a gradient-based
method - Use a multiscale method in X-T domain
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Waveform Tomography
True Velocity
Observed Wavefield
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Outline

• Introduction
• Time-domain multiscale waveform tomography
• Processing workflow
• Field data results
• Gulf of Mexico
• Saudi Arabia
• Summary

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Why Use Multiscale?
Low Frequency
Coarse Scale
High Frequency
Fine Scale
Image from Bunks et al. (1995)
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Multiscale Waveform Tomography
1. Collect data d(x,t)
4. To prevent getting stuck in local minima
a). Invert early arrivals initially
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Outline

• Introduction
• Time-domain multiscale waveform tomography
• Processing workflow
• Field data results
• Gulf of Mexico
• Saudi Arabia
• Summary

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Processing Workflow
Pre-Processing of Data
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Processing Workflow
Pre-Processing of Data
3D-to-2D conversion
Attenuation compensation
Random noise removal
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Processing Workflow
Pre-Processing of Data
Generate a stacked section
Estimating Source Wavelet
Pick the water-bottom
Stack along the water-bottom
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Processing Workflow
Pre-Processing of Data
Traveltime picking
Initial model RMS velocity
Generating Initial Model
Refraction traveltime inversion
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Processing Workflow
Pre-Processing of Data
Low-pass filtering
Multiscale Waveform Tomography
Inversion from low- to high-frequency bands
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Processing Workflow
Pre-Processing of Data
Migration images
Validating Velocity Tomograms
Common image gathers
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Outline

• Introduction
• Time-domain multiscale waveform tomography
• Processing workflow
• Field data results
• Gulf of Mexico
• Saudi Arabia
• Summary

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Gulf of Mexico Data
480 Hydrophones
515 Shots
dt 2 ms Tmax 10 s
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Low-pass Filtering
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Reconstructed Velocity
Velocity (m/s)
Velocity (m/s)
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Kirchhoff Migration Images
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Kirchhoff Migration Images
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Comparing CIGs
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Comparing CIGs
CIG from Waveform Tomogram
CIG from Traveltime Tomogram
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Comparing CIGs
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Comparing CIGs
CIG from Waveform Tomogram
CIG from Traveltime Tomogram
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Comparing CIGs
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Comparing CIGs
CIG from Waveform Tomogram
CIG from Traveltime Tomogram
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Outline

• Introduction
• Time-domain multiscale waveform tomography
• Processing workflow
• Field data results
• Gulf of Mexico
• Saudi Arabia
• Summary

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Saudi Arabia Land Survey
1. 1279 CSGs, 240 traces/gather
2. 30 m station interval, max. offset 3.6km
3. Line Length 46 km
4. Pick 246,000 traveltimes
5. Traveltime tomography -gt V(x,y,z)
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Brute Stack Section
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Time (s)
2.0
3920
5070
CDP
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Traveltime Tomostatics Stacking
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Time (s)
2.0
3920
5070
CDP
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Waveform Tomostatics Stacking
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Time (s)
2.0
3920
5070
CDP
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Outline

• Introduction
• Time-domain multiscale waveform tomography
• Processing workflow
• Field data results
• Gulf of Mexico
• Saudi Arabia
• Summary

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Summary
Acoustic waveform inversion was successfully
applied to both marine and land datasets, and can
provide accurate velocity subsurface structures.

• Issues
• Cost gt 100 iterations How to reduce cost?
• Acoustic vs. Elastic How far can we go with
  acoustic?
• Anisotropy needed?
• Source wavelet important Source-independent
  inversion.
• Missing low frequencies Better initial model via
  reflection tomography.

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Acknowledgment

• We would like to thank
• UTAM sponsors for financial support.
• Amarada Hess and Saudi Aramco for providing us
  the datasets.

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