Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization. The current seismic waveform clustering algorithms are predominantly based on a fixed time window, which is applicable for layers of stable thickness. When a layer exhibits variable thickness in the seismic response, a fixed time window cannot provide comprehensive geologic information for the target interval. Therefore, we propose a novel approach for a waveform clustering workflow based on a variable time window to enable broader applications. The dynamic time warping (DTW) distance is first introduced to effectively measure the similarities between seismic waveforms with various lengths. We develop a DTW distance-based clustering algorithm to extract centroids, and we then determine the class of all seismic traces according to the DTW distances from centroids. To greatly reduce the computational complexity in seismic data application, we propose a superpixel-based seismic data thinning approach. We further propose an integrated workflow that can be applied to practical seismic data by incorporating the DTW distance-based clustering and seismic data thinning algorithms. We evaluated the performance by applying the proposed workflow to synthetic seismograms and seismic survey data. Compared with the the traditional waveform clustering method, the synthetic seismogram results demonstrate the enhanced capability of the proposed workflow to detect boundaries of different lithologies or lithologic associations with variable thickness. Results from a practical application show that the planar map of seismic waveform clustering obtained by the proposed workflow correlates well with the geological characteristics of wells in terms of reservoir thickness.

地震波形聚类是用于岩性识别和储层表征的有用技术。当前的地震波形聚类算法主要基于固定的时间窗口，这适用于厚度稳定的层。当一个层在地震响应中表现出不同的厚度时，固定的时间窗口无法为目标间隔提供全面的地质信息。因此，我们提出了一种基于可变时间窗口的波形聚类工作流的新颖方法，以实现更广泛的应用。首先引入动态时间规整（DTW）距离，以有效地测量各种长度的地震波形之间的相似度。我们开发了一种基于DTW距离的聚类算法来提取质心，然后根据距质心的DTW距离确定所有地震道的类别。为了大大降低地震数据应用中的计算复杂度，我们提出了一种基于超像素的地震数据细化方法。我们进一步提出了一个集成的工作流程，通过结合基于DTW距离的聚类和地震数据细化算法，可以将其应用于实际地震数据。我们通过将拟议的工作流程应用于合成地震图和地震勘测数据来评估性能。与传统的波形聚类方法相比，合成地震图结果证明了所提出的工作流程增强了检测厚度不同的岩性或岩性联系的边界的能力。