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首页> 外文期刊>Journal of Seismic Exploration >APPLICATION OF TRACE-BASED SPECTRAL PRINCIPAL COMPONENT ANALYSIS METHOD FOR SEISMIC THIN-LAYER THICKNESS DELINEATION
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APPLICATION OF TRACE-BASED SPECTRAL PRINCIPAL COMPONENT ANALYSIS METHOD FOR SEISMIC THIN-LAYER THICKNESS DELINEATION

机译：基于迹线的谱主成分分析方法在地震薄层厚度划分中的应用

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摘要

Spectral decomposition of a 3-dimensional reflection seismic volume generates large volumes of spectral data in the form of time-frequency analysis at every seismic signal location. Conventional spectral principal component analysis (PCA) compresses the multi-dimensional spectral data exclusively on amplitude maps at interpreted seismic horizon. This overlooks the time-variant nature of spectral amplitudes. Hence, it is difficult to estimate thin-layer thickness variations directly from the conventional horizon-based spectral PCA (HSPCA) results. A trace-based spectral principal component analysis (TSPCA) method is proposed for seismic thin-layer thickness delineation. Compared to HSPCA, TSPCA calculates spectral principal components (PCs) within a time window over the targeted seismic event on a trace-by-trace basis. Trace-based spectral PCs are assumed independent, i.e., as amplitude responses from reflection events with different frequency characteristics. A rotation of PC coefficients following the Varimax criterion is proposed to automatically interpret the three most significant spectral PCs as related to (1) reflection amplitude determined by rock impedances, (2) tuning of a pure even-reflection pair, or (3) tuning from a pure odd-reflection pair. The latter two types of tuning-related amplitude are both governed by thin-layer thickness and have different frequency responses. Results on synthetic wedge models of pure odd- and even-reflection pair thin layers show that the trace-based spectral PCs show a distinct relationship to thin-layer thickness. Comparing spectral PC images calculated on a geologically complex 3D model after HSPCA and TSPCA methods, we conclude that TSPCA has superior capability for precise thickness delineation, especially for subtle thickness variations in the model.
机译：3维反射地震体的频谱分解会在每个地震信号位置以时频分析的形式生成大量频谱数据。常规频谱主成分分析（PCA）仅在解释的地震层位的振幅图上压缩多维频谱数据。这忽略了频谱幅度的时变性质。因此，难以直接从常规的基于水平的光谱PCA（HSPCA）结果估计薄层厚度变化。提出了一种基于迹线的谱主成分分析方法（TSPCA），用于地震薄层厚度的描绘​​。与HSPCA相比，TSPCA在逐条迹线的基础上，在目标地震事件的时间窗口内计算频谱主成分（PC）。假定基于迹线的频谱PC是独立的，即作为来自具有不同频率特性的反射事件的幅度响应。建议按照Varimax准则旋转PC系数，以自动解释与（1）岩石阻抗确定的反射幅度，（2）纯偶数反射对的调谐或（3）调谐有关的三个最重要的光谱PC。来自纯奇数反射对。后两种类型的与调谐相关的振幅均由薄层厚度控制，并且具有不同的频率响应。纯奇数和偶数反射对薄层的合成楔形模型的结果表明，基于迹线的光谱PC显示出与薄层厚度的明显关系。比较通过HSPCA和TSPCA方法在地质复杂的3D模型上计算得到的光谱PC图像，我们得出的结论是，TSPCA具有出色的精确轮廓描绘能力，尤其是模型中细微的厚度变化。

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  • 来源
    《Journal of Seismic Exploration》 |2019年第6期|551-576|共26页
  • 作者

    Zhou Jian; Ba Jing; Castagna John P.; Carcione Jose M.;

  • 作者单位

    Hohai Univ Nanjing 211100 Peoples R China|Univ Houston Houston TX 77204 USA|Nanjing Foreign Language Sch Fangshan Campus Nanjing 211199 Peoples R China;

    Hohai Univ Nanjing 211100 Peoples R China;

    Univ Houston Houston TX 77204 USA;

    Hohai Univ Nanjing 211100 Peoples R China|Ist Nazl Oceanog & Geofis Sperimentale Borgo Grotta Gigante 42c I-34010 Trieste Italy;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    spectral decomposition; HSPCA; TSPCA;

    机译：光谱分解HSPCA;TSPCA;

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