Investigation of ground movements induced by underground gas storages via unsupervised ML methodology applied to InSAR data

Abstract

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.The proposed methodology was validated using two independent case studies in the Po Plain (northern Italy), a highly urbanized area affected by ground movements induced by several natural and anthropogenic causes, including underground gas storage facilities. For each case study, the methodology localizes one well-defined and confined area as the most affected by storage operations: this area corresponds to a cluster characterized by a high cohesion and by a seasonality phase coherent with the storage injection/withdrawal periods. The other clusters group areally wide-spread measurement points; the phase of their sinusoidal curves shows no time-coherency (or even phase opposition) with the seasonal storage operations. The results were verified via available independent information about the storage locations and were compared with the findings of previous research.