Seismic facies analysis can generate a map to describe the spatial distribution characteristics of reservoirs, and therefore plays a critical role in seismic interpretation. To analyse the characteristics of the horizon of interest, it is usually necessary to extract seismic waveforms along the target horizon using a selected time window. The inaccuracy of horizon interpretation often produces some inconsistent phases and leads to inaccurate classification. Therefore, the developed adaptive phase K-means algorithm proposed a sliding time window to extract seismic waveforms. However, setting the maximum offset of the sliding window is difficult in a real data application. A value that is too large may cause the cross-layer problem, whereas a value that is too small reduces the flexibility of the algorithm. To address this disadvantage, this paper proposes a robust K-means (R-K-means) algorithm with a Gaussian-weighted sliding window for seismic waveform classification. The used weights punish those windows distant from the interpretation horizon in the objective function, consequently producing a smaller range of horizon adjustments even when using relatively large maximum offsets and benefitting the generation of stable and reliable seismic facies maps. The application of real seismic data from the F3 block proves the effectiveness of the proposed algorithm.

中文翻译：

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用于地震相分析的具有加权窗口的稳健 K-means 算法

地震相分析可以生成图来描述储层的空间分布特征，因此在地震解释中起着至关重要的作用。为了分析感兴趣层位的特征，通常需要使用选定的时间窗口沿目标层位提取地震波形。层位解释的不准确往往会产生一些不一致的相位并导致分类不准确。因此，开发的自适应相位 K-means 算法提出了一个滑动时间窗口来提取地震波形。然而，在实际数据应用中，设置滑动窗口的最大偏移量是很困难的。过大的值可能会导致跨层问题，而过小的值会降低算法的灵活性。为了解决这个缺点，本文提出了一种用于地震波形分类的具有高斯加权滑动窗口的鲁棒 K-means (RK-means) 算法。所使用的权重会在目标函数中惩罚那些远离解释层位的窗口，因此即使在使用相对较大的最大偏移时也会产生较小范围的层位调整，并有利于生成稳定可靠的地震相图。F3区块实际地震数据的应用证明了所提算法的有效性。因此，即使在使用相对较大的最大偏移时也能产生较小范围的层位调整，并有利于生成稳定可靠的地震相图。F3区块实际地震数据的应用证明了所提算法的有效性。因此，即使在使用相对较大的最大偏移时也能产生较小范围的层位调整，并有利于生成稳定可靠的地震相图。F3区块实际地震数据的应用证明了所提算法的有效性。