ABSTRACT

Geological energy storage and carbon sequestration activities should consider the stability of surrounding faults and induced seismicity potential. In order to ensure the efficacy of storage medium, it is crucial to possess a comprehensive understanding of the movement of pressure plumes within geological features by monitoring the potential impact on the deformation of geological layers as well as the ground surface. In this study, we propose a new tool (machine learning inversion solution, MLIS) capable of identifying opening (dilation) and shearing behaviour of faults and fractures pressurised by a fluid plume. While geo-storage of energy and CO₂ is mainly dominant with the dilational deformation, any fault slippage generates shear deformation. Combination of the two creates a mixed-mode deformation detectable via an array of tiltmeters, fibre-optic strain sensors, or Interferometric Synthetic Aperture Radar (InSAR). MLIS utilises surrogate models trained specifically for dilation and shear deformations, together with Bayesian inversion and differential evolution optimisation to identify the set of unknown parameters that gives the best fit to the observed data.