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<p>Conventional deconvolution methods improve seismic resolution at the cost of reduced signal-to-noise ratio (SNR), limiting the accuracy of high-frequency signal recovery. To address this issue, this paper proposes a high-resolution processing method based on low-dimensional manifold constraints. First, data-driven manifold learning is employed to construct neighborhood relationships and characterize the distribution of high-dimensional seismic records in low-dimensional manifold space. Then, manifold information is incorporated into the regularization framework of high-resolution inversion to establish a multi-channel inversion objective function with low-dimensional manifold constraints. Finally, an iterative optimization strategy is applied for simultaneous multi-channel inversion of reflection coefficient sequences. By introducing spatial correlation of seismic signals into the high-resolution processing workflow, this method effectively suppresses noise interference in high-frequency signal recovery. Both synthetic and field data tests demonstrate that the proposed method maintains superior SNR while enhancing resolution, improving the characterization accuracy of thin-layer hydrocarbon reservoirs.</p>

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High-resolution seismic data processing based on low-dimensional manifold constraints

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Gang-lin Lei,
  • Guang-liang Zhao,
  • Suo Cheng,
  • Yang Tan,
  • Jiang-wei Shang,
  • Ze-lin Li,
  • Zi-lun Xiong,
  • Zhu-kun Wang

摘要

Conventional deconvolution methods improve seismic resolution at the cost of reduced signal-to-noise ratio (SNR), limiting the accuracy of high-frequency signal recovery. To address this issue, this paper proposes a high-resolution processing method based on low-dimensional manifold constraints. First, data-driven manifold learning is employed to construct neighborhood relationships and characterize the distribution of high-dimensional seismic records in low-dimensional manifold space. Then, manifold information is incorporated into the regularization framework of high-resolution inversion to establish a multi-channel inversion objective function with low-dimensional manifold constraints. Finally, an iterative optimization strategy is applied for simultaneous multi-channel inversion of reflection coefficient sequences. By introducing spatial correlation of seismic signals into the high-resolution processing workflow, this method effectively suppresses noise interference in high-frequency signal recovery. Both synthetic and field data tests demonstrate that the proposed method maintains superior SNR while enhancing resolution, improving the characterization accuracy of thin-layer hydrocarbon reservoirs.