Traditional coherence algorithms are often based on the assumption that seismic traces are stationary and Gaussian. However, seismic traces are actually non‐stationary and non‐Gaussian. A constant time window and the canonical correlation analysis in traditional coherence algorithms are not optimal for non‐stationary seismic traces and cannot describe the similarity between adjacent seismic traces in detail. To overcome this problem, a new coherence algorithm using the high‐resolution time–time transform and the feature matrix is designed. The high‐resolution time–time transform used to replace the constant time window can produce a frequency‐dependent time local series to analyse non‐stationary seismic traces. The feature matrix, constructed by the frequency‐dependent time local series and the related local gradients, defines a new correlation metric that enhances more details of the geological discontinuities in seismic images than does the canonical correlation analysis. Additionally, the Riemannian metric is introduced for related calculations because the feature matrices are not defined in a Euclidean space but rather in a manifold space. Application to field data illustrates that the proposed method reveals more details of structural and stratigraphic features.

中文翻译：

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具有高分辨率时-时变换和地震数据特征矩阵的相干算法

传统的相干算法通常基于地震迹线是平稳和高斯的假设。但是，地震痕迹实际上是非平稳且非高斯的。传统相干算法中的恒定时间窗和规范相关性分析对于非平稳地震迹线不是最佳的，并且无法详细描述相邻地震迹线之间的相似性。为了克服这个问题，设计了一种使用高分辨率时间-时间变换和特征矩阵的新相干算法。用来替换恒定时间窗口的高分辨率时间-时间变换可以产生与频率相关的时间局部序列，以分析非平稳地震道。由频率相关的时间局部序列和相关的局部梯度构成的特征矩阵，定义了一种新的相关度量，该度量比规范相关分析增强了地震图像中地质不连续性的更多细节。另外，引入黎曼度量用于相关计算，因为特征矩阵不是在欧几里得空间中定义的，而是在流形空间中定义的。对现场数据的应用表明，所提出的方法揭示了更多的结构和地层特征细节。