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<p>Due to insufficient compaction of the overlying strata, near-surface layers are classified as unconsolidated formations. These layers exhibit complex velocity structures and significant attenuation, which severely impair the resolution of deep seismic data. In practical seismic data processing, obtaining a detailed near-surface Q field is essential for improving the imaging resolution of medium- to deep-depth layers. Currently, seismic data from uphole surveys are commonly used to generate near-surface Q fields. However, this approach faces challenges such as high per-well costs and low well density, making it insufficient for constructing a detailed near-surface Q field. To address these issues, this study employs a BP neural network, leveraging its powerful nonlinear fitting capabilities to establish a functional relationship between input data—including velocity, position, and travel time derived from seismic data—and Q values. This method offers an efficient and high-precision approach for predicting near-surface Q fields, overcoming the limitations of conventional methods that typically suffer from low fitting accuracy and high data requirements. By implementing this approach, we successfully derived the near-surface Q field from a field dataset and performed absorption compensation. The results of this application validate the effectiveness of the proposed method.</p>

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Establishment of near-surface Q model using a shallow neural network

  • 期刊论文

  • 发表日期：

    2025年3月12日

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  • Jizhong Wu,
  • Jing Gao,
  • Kexin Wang,
  • Ying Shi

摘要

Due to insufficient compaction of the overlying strata, near-surface layers are classified as unconsolidated formations. These layers exhibit complex velocity structures and significant attenuation, which severely impair the resolution of deep seismic data. In practical seismic data processing, obtaining a detailed near-surface Q field is essential for improving the imaging resolution of medium- to deep-depth layers. Currently, seismic data from uphole surveys are commonly used to generate near-surface Q fields. However, this approach faces challenges such as high per-well costs and low well density, making it insufficient for constructing a detailed near-surface Q field. To address these issues, this study employs a BP neural network, leveraging its powerful nonlinear fitting capabilities to establish a functional relationship between input data—including velocity, position, and travel time derived from seismic data—and Q values. This method offers an efficient and high-precision approach for predicting near-surface Q fields, overcoming the limitations of conventional methods that typically suffer from low fitting accuracy and high data requirements. By implementing this approach, we successfully derived the near-surface Q field from a field dataset and performed absorption compensation. The results of this application validate the effectiveness of the proposed method.