Scholarlink

<p>Seismic data reconstruction is a critical step in seismic exploration, for which the high-resolution hyperbolic Radon transform (HRT) serves as a key processing tool. This study addresses the challenge of preserving Amplitude Variation with Offset (AVO) characteristics during high-resolution hyperbolic Radon transforms for seismic data reconstruction. Conventional sparse Radon transforms enhance resolution but degrade AVO information due to offset averaging. To overcome this limitation, we propose a novel framework integrating: Fast Iterative Shrinkage Thresholding Algorithm (FISTA) to solve the hyperbolic Radon transform with mixed-norm regularization, significantly improving computational efficiency and Radon panel resolution compared to the Conjugate Gradient (CG) method. Polynomial fitting of AVO attributes within the inverse Radon transform, enabling accurate recovery of amplitude-offset relationships. Synthetic data and field data demonstrates that FISTA achieves superior sparsity in the Radon domain. The polynomial-augmented inverse transform preserves AVO features and reduces reconstruction residuals versus conventional methods. This method provides robust technical support for the accurate interpretation of seismic data and for subsequent hydrocarbon exploration. Consequently, it holds significant practical value and shows extensive prospects for future application.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Seismic Data Interpolation by AVO-Preserving Hyperbolic Radon Transform Using FISTA and Polynomial Fitting

  • 期刊论文

  • 发表日期：

    2025年9月2日

查看全文
  • Sheng-chao Wang,
  • Han-feng Liu,
  • De-fu Zhang,
  • Hui Sun,
  • Wei Zhang,
  • Song-da Ji

摘要

Seismic data reconstruction is a critical step in seismic exploration, for which the high-resolution hyperbolic Radon transform (HRT) serves as a key processing tool. This study addresses the challenge of preserving Amplitude Variation with Offset (AVO) characteristics during high-resolution hyperbolic Radon transforms for seismic data reconstruction. Conventional sparse Radon transforms enhance resolution but degrade AVO information due to offset averaging. To overcome this limitation, we propose a novel framework integrating: Fast Iterative Shrinkage Thresholding Algorithm (FISTA) to solve the hyperbolic Radon transform with mixed-norm regularization, significantly improving computational efficiency and Radon panel resolution compared to the Conjugate Gradient (CG) method. Polynomial fitting of AVO attributes within the inverse Radon transform, enabling accurate recovery of amplitude-offset relationships. Synthetic data and field data demonstrates that FISTA achieves superior sparsity in the Radon domain. The polynomial-augmented inverse transform preserves AVO features and reduces reconstruction residuals versus conventional methods. This method provides robust technical support for the accurate interpretation of seismic data and for subsequent hydrocarbon exploration. Consequently, it holds significant practical value and shows extensive prospects for future application.