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<p>To address the issue of low imaging accuracy in deep layers for Least-Squares Reverse Time Migration (LSRTM), a gradient preconditioning algorithm based on the background wavefield bidirectional illumination operator is proposed. This algorithm eliminates the influence of migration results on the gradient, which is a common limitation in conventional gradient preconditioning methods. As a result, the gradient preconditioning operator more closely approximates the Hessian matrix of LSRTM, thereby enhancing the accuracy of deep-layer imaging. Multiples can introduce artifacts during the migration process, which interfere with imaging and degrade its quality. Data-weighted LSRTM overcomes this challenge by introducing a weighting matrix to separate primary waves and interbed multiples from seismic residuals. This separation allows the backpropagated wavefields of primary waves and interbed multiples to be distinguished when they are used as sources, effectively suppressing migration artifacts. The data-weighted LSRTM proposed in this study, which is based on the background wavefield bidirectional illumination operator, uses the seismic records of the background velocity model as reverse-time perturbations to compute the reverse-time wavefield energy. This approach accurately achieves deep-layer amplitude compensation for LSRTM. By incorporating the predicted interbed multiples from the high-order Born modeling into the data-weighted LSRTM, interbed multiple imaging is realized. The effectiveness of the proposed method is validated through numerical modeling experiments.</p>

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LSRTM with data weighting based on the background wavefield bidirectional illumination operator

  • 期刊论文

  • 发表日期：

    2026年5月21日

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  • Yu-chen Luo,
  • Zhang-qing Luo,
  • Teng-yun Dang,
  • Hamza Ali,
  • Tian-jie Chen,
  • Ming-chun Chen,
  • Sheng Liu,
  • Tian-ji Xu

摘要

To address the issue of low imaging accuracy in deep layers for Least-Squares Reverse Time Migration (LSRTM), a gradient preconditioning algorithm based on the background wavefield bidirectional illumination operator is proposed. This algorithm eliminates the influence of migration results on the gradient, which is a common limitation in conventional gradient preconditioning methods. As a result, the gradient preconditioning operator more closely approximates the Hessian matrix of LSRTM, thereby enhancing the accuracy of deep-layer imaging. Multiples can introduce artifacts during the migration process, which interfere with imaging and degrade its quality. Data-weighted LSRTM overcomes this challenge by introducing a weighting matrix to separate primary waves and interbed multiples from seismic residuals. This separation allows the backpropagated wavefields of primary waves and interbed multiples to be distinguished when they are used as sources, effectively suppressing migration artifacts. The data-weighted LSRTM proposed in this study, which is based on the background wavefield bidirectional illumination operator, uses the seismic records of the background velocity model as reverse-time perturbations to compute the reverse-time wavefield energy. This approach accurately achieves deep-layer amplitude compensation for LSRTM. By incorporating the predicted interbed multiples from the high-order Born modeling into the data-weighted LSRTM, interbed multiple imaging is realized. The effectiveness of the proposed method is validated through numerical modeling experiments.