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Optimal Seismic Reflectivity Inversion: Data-driven ℓ -loss-ℓ -regularization Sparse Regression.

Fangyu Li1, Rui Xie1, Wen-Zhan Song1

  • 1University of Georgia, Athens, GA 30602, USA.

IEEE Geoscience and Remote Sensing Letters : a Publication of the IEEE Geoscience and Remote Sensing Society
|June 28, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal seismic reflectivity inversion method using adaptive regularization (ℓp-loss-ℓq-regularization) for improved underground imaging. It also optimizes the damping factor via K-fold cross-validation, enhancing seismic inversion accuracy.

Keywords:
cross-validationdamping factoroptimal regularizationseismic reflectivity inversionℓp,q regularization

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Area of Science:

  • Geophysics
  • Seismic Data Processing
  • Inversion Techniques

Background:

  • Seismic reflectivity inversion is crucial for detailed subsurface understanding, often relying on the convolution model and optimization.
  • The L1 norm is commonly used for regularization in seismic inversion to mitigate noise, but its optimality is not definitively proven.
  • Existing methods face limitations in noise/interference vulnerability and may not represent the best choice for regularization.

Purpose of the Study:

  • To propose an optimal seismic reflectivity inversion approach that moves beyond unproven fixed regularization norms.
  • To develop a method that adaptively selects regularization parameters for more accurate and detailed reflectivity profiles.
  • To enhance seismic inversion results by optimizing the damping factor using K-fold cross-validation.

Main Methods:

  • Implementation of a non-convex constraint for seismic inversion utilizing L_p-loss-L_q-regularization (p=2, 0
  • Application of the majorization-minimization algorithm for solving the optimization problem.
  • Utilization of K-fold cross-validation to determine the optimal damping factor (λ).

Main Results:

  • The proposed method estimates a more accurate and detailed reflectivity profile by adaptively adopting L_p-loss-L_q-regularization.
  • Optimization of the damping factor through K-fold cross-validation further improves seismic inversion outcomes.
  • Validation of the approach through synthetic examples and a field dataset from the Bohai Bay Basin, China.

Conclusions:

  • The adaptive L_p-loss-L_q-regularization approach offers a superior alternative to fixed L1 norm regularization in seismic inversion.
  • The integration of K-fold cross-validation effectively optimizes the damping factor, leading to enhanced inversion performance.
  • The proposed method demonstrates significant potential for improving the accuracy and detail of subsurface imaging in geophysical exploration.