The research explores the definition, formalization, and validation of a sound and rigorous methodology for analyzing a vast amount of satellite-based measures to geo-localize and quantify the ground movements induced by the storage of natural gas in underground formations. Time series decomposition analysis and unsupervised machine learning algorithms (partitive and hierarchical clustering) are adopted for processing, categorizing, and interpreting ground vertical movements from InSAR acquisitions. At the surface level, storage operations induce characteristic seasonal and cyclical movements, showing uplift during the injection period and subsidence during the withdrawal one. Consequently, the analysis of the solely sinusoidal component of the vertical movements (obtained via the time-series decomposition) turns out to be the key aspect of the proposed approach for handling the superposition of different ground movement sources, and consequently for clearly and reliably identifying the effects of underground gas storage (UGS) only.

中文翻译：

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通过应用于 InSAR 数据的无监督机器学习方法来研究地下储气库引起的地面运动

该研究探索了一种健全而严格的方法的定义、形式化和验证，用于分析大量基于卫星的测量结果，以对地下地层中天然气储存引起的地面运动进行地理定位和量化。采用时间序列分解分析和无监督机器学习算法（部分聚类和分层聚类）来处理、分类和解释 InSAR 采集的地面垂直运动。在地表层面，储存作业会引起特征性的季节性和周期性运动，表现为注入期间的隆起和抽取期间的沉降。因此，对垂直运动的单一正弦分量（通过时间序列分解获得）的分析是所提出的处理不同地面运动源叠加的方法的关键方面，从而清晰可靠地识别仅地下储气库 (UGS) 的影响。