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    瞬时谱相位分解方法及隐蔽河道砂体识别应用

    Instantaneous spectral phase decomposition and its application to concealed channel sand body identification

    • 摘要: 传统地震储层预测,尤其是针对薄互层河道砂体,频率域属性分析方法已取得了显著进展和广泛应用。然而,在四川盆地蓬莱镇组等浅层沉积环境中,面对厚度接近地震分辨率极限、多期叠置且横向变迁频繁的薄层砂体,频率域属性分析方法对背景噪声敏感,横向不连续边界刻画能力有限,其薄层砂体识别能力逐渐显现瓶颈。在此基础上,聚焦于地震信号中蕴含丰富地质信息的相位属性,提出了一种基于改进广义S变换的瞬时谱相位分解方法。该方法通过高精度时频分析提取瞬时振幅与相位谱,利用相位域分解技术将地震信号按特定相位区间进行重构,有效分离出被强背景噪声掩盖的、与薄层砂体相关的弱响应信号。在川西地区蓬莱镇组的应用结果表明,瞬时谱相位分解方法通过压制泥岩背景干扰,显著改善了窄河道与多期叠置河道边界的识别效果,克服了常规分频RGB属性融合方法对薄层预测精度不足的问题,该方法提取的分相位振幅属性为薄层砂体精细预测提供了相位域信息补充,具有显著的实际应用效果。

       

      Abstract: In traditional seismic reservoir prediction, especially for thin interbedded channel sand bodies, frequency-domain attribute analysis methods have achieved significant progress and extensive application. However, in shallow sedimentary environments such as the Penglaizhen Formation in the Sichuan Basin, where thin sand bodies approach the seismic resolution limit and feature multi-stage superposition and frequent lateral migration, frequency-domain attribute analysis methods are sensitive to background noise and limited in characterizing laterally discontinuous boundaries, and their thin-bed identification capability is gradually reaching a bottleneck. To address this issue, focusing on phase attributes that contain abundant geological information in seismic signals, we propose an instantaneous spectral phase decomposition method based on the improved generalized S-transform. Through high-precision time–frequency analysis, this method extracts instantaneous amplitude and phase spectra, and uses phase-domain decomposition to reconstruct seismic signals within specific phase intervals, effectively separating weak thin-sand responses masked by strong background noise. Applications in the Penglaizhen Formation of the western Sichuan Basin show that the instantaneous spectral phase decomposition method, by suppressing mudstone background interference, significantly improves the identification of narrow channels and multi-stage superimposed channel boundaries, overcoming the insufficient thin-bed prediction accuracy of conventional frequency-division RGB attribute fusion methods. The phase-divided amplitude attribute extracted by this method provides a phase-domain supplement for fine thin-sand prediction, demonstrating significant practical value.

       

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