Mitigation of RTM Artifacts with Migration Kernel Decomposition

Mitigation of RTM Artifacts

with Migration Kernel DecompositionGe Zhan* and Gerard T. Schuster

King Abdullah University of Science and Technology

June 7, 2012

Outline

• Introduction

• Method

• Examples Two-layer model BP salt model

• Conclusions0 25

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Outline

• Introduction

• Method

• Examples Two-layer model BP salt model

• Conclusions

Introduction --- Reverse-time migration (RTM)

Benefits:Images any dipping structure;Accounts for multiple arrivals; and etc.

Problems:intensive computational costssevere migration artifacts

Introduction--- RTM artifacts

RTM artifacts usually present as strong-amplitude, low-frequency noisesin the migration image.

artifacts contaminate image

Various remedies have been proposed to suppress RTM artifacts: Smooth the velocity model before migration (Loewenthal et al., 1987); Low-cut filtering on migrated images (Mulder and Plessix, 2003);

Directional damping to non-reflection wave equation (Fletcher et al., 2005); Least-squares migration (Nemeth et al., 1999; Guitton et al., 2006); Migration deconvolution (Hu et al., 2001; Yu et al., 2006);

Poynting-vector imaging condition (Yoon and Marfurt, 2006); Wavefield decomposition using Hilbert transform (Liu et al., 2007; 2011).

Outline

• Introduction

• Method

• Examples Two-layer model BP salt model

• Conclusions

Method--- Seismic Survey

Method--- Seismic Modeling

Recorded seismic data

Method --- Reverse Time Migration (RTM)

Migration of seismic data

Method --- Reverse Time Migration (RTM)

Migration of seismic data

Method --- Reverse Time Migration (RTM)

Migration of seismic data

Method --- Reverse Time Migration (RTM)

Migration of seismic data

Method --- Reverse Time Migration (RTM)

Migration of seismic data

Method --- Reverse Time Migration (RTM)

Migration of seismic data

Method --- Generalized Diffraction Migration (GDM)

Migration of seismic data

Method--- GDM Workflow

1. Compute & save Green’s functions for a given migration velocity;

2. Filter the Green’s functions into downgoing and upgoing components in F-K domain;

3. Convolve the appropriate components of filtered Green’s function to form the migration kernel; 4. Dot product of the migration kernel with the recorded seismic data to get the migration image. T

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shotgather

T

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Migration Kernel

Outline

• Introduction

• Method

• Examples Two-layer model BP salt model

• Conclusions

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GDM

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RTM

Examples--- two-layer model

Outline

• Introduction

• Method

• Examples Two-layer model BP salt model

• Conclusions

Examples--- BP salt model

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1-shot RTM image

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Stacked RTM image

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High-pass-filtered RTM image

Examples--- BP salt model

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Standard RTM w/ filtering

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Horizontal GDM image

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Vertical GDM image

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Stacked GDM image

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Outline

• Introduction

• Method

• Examples Two-layer model BP salt model

• Conclusions

1). The kernel of RTM imaging operator is decomposed into products of downgoing and upgoing Green’s functions.2). This decomposition leads to an imaging algorithm with fewer artifacts and a higher-quality RTM image.3). Advantage: deterministic filtering of RTM kernel can be directly applied to reduce migration artifacts, mitigate multiples and eliminate aliasing artifacts.4). Drawback: significantly more storage capacity and I/O time than standard RTM.

Conclusions

5). There are still some residual artifacts, which can be further eliminated by least-squares migration.

We thank the sponsors of the Center for Subsurface Imaging and Fluid Modeling (CSIM) at KAUST for their support.

Acknowledgments

We also thank BP for making the BP 2007 salt model available.

Question or Suggestion?

Thank you for your attention!