时间序列InSAR（Interferometric Synthetic Aperture Radar）技术能够大范围获取形变，在地质灾害监测、城市基础设施安全评价、矿区边坡监测等应用中取得了大量成功的应用。然而，时序InSAR技术获取的海量形变时间序列给形变场的解译带来了巨大的挑战。海量时序形变的自动分类，对于解译形变信息、及时发现危险信号具有重要意义。本文提出了一种基于自监督对比学习的InSAR时序形变深度聚类方法。通过引入了自监督对比学习框架，增强了模型在无标签数据上的聚类能力。针对时间序列数据增强方法在捕捉时间序列变换不变性方面的不足，本文提出了一种基于旋转形状一致性的数据增强策略。该策略通过对原始时间序列数据进行不同角度的旋转，有效保持了时间序列的形态相似性，提升了聚类的准确性和鲁棒性。在验证数据集上本文提出的方法在最佳聚类精度和归一化互信息值方面分别较传统 K-means 方法提升了 25.8% 和 16.3%。与 K-shape 方法相比，本文方法在捕捉时间序列特征与时间序列相似性度量方面表现出更好的准确性。此外，我们利用2020年1月至2022年10月间覆盖云南省个旧市卡房尾矿库的哨兵1号升轨数据集所提取出的时间序列形变验证本文所提出的方法。我们对获取的时间序列形变进行了聚类分析，得到了可靠的分类结果，表明该方法可有效的对InSAR形变时间序列进行分类。

The time-series InSAR (Interferometric Synthetic Aperture Radar) technique is widely recognized for its capability to monitor large-scale deformations, making it instrumental in applications such as geologic disaster monitoring, urban infrastructure safety assessment, and mining slope evaluation. This study aims to address the challenges in deciphering large-scale deformation time-series data obtained through InSAR (Interferometric Synthetic Aperture Radar). By leveraging self-supervised contrastive learning, we enhance the classification and clustering capabilities of InSAR time-series deformation data, which is critical for identifying geohazard signals and supporting infrastructure safety evaluation. Method: We propose a novel deep clustering framework based on self-supervised contrastive learning to enhance clustering performance on unlabeled deformation time-series data. To overcome the limitations of traditional time-series data augmentation techniques, a rotational consistency-based data augmentation strategy is introduced. This strategy maintains morphological similarity by rotating the original time series at different angles, enabling the model to better capture invariance in time-series transformations. The method"s clustering performance is evaluated against traditional K-means clustering, with key metrics such as clustering accuracy and normalized mutual information (NMI) used for comparison. The framework is further validated using deformation data extracted from the Sentinel-1 ascending orbit dataset, covering the Kafang tailings pond in Gejiu City, Yunnan Province, from January 2020 to October 2022. Results: The proposed method outperformed traditional K-means clustering, achieving a 25.8% improvement in clustering accuracy and a 16.3% increase in normalized mutual information. Compared with the K-shape method, the proposed method shows better accuracy in capturing time series features and time series similarity measurement. The clustering analysis performed on the Sentinel-1 dataset successfully classified deformation time series into meaningful groups, revealing distinct deformation patterns and effectively identifying potential danger signals. Conclusion: The integration of self-supervised contrastive learning with InSAR time-series analysis enhances the interpretability and classification of deformation patterns. The proposed method provides a robust and efficient tool for geohazard monitoring, urban infrastructure evaluation, and mining slope safety assessment. This approach has potential for broader applications in large-scale remote sensing data analysis.