Quantification of the Crack Evolution Process by Extracting Relevant Signal Components from Wave Propagation and Diffusive Transport Front Measurements
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The dynamical behavior of fractures in the earth’s subsurface influences key processes that govern the extraction of energy resources and isolation of energy wastes in the shallow crust. Fracture evolution depends on complex interactions involving mineralogy, pore structure, rock fabric, effective mechanical moduli, fluid saturation, and pre-existing microcracks in the rock, and also on the strain rate and the thermal, chemical, and stress history. Such complexities and heterogeneities are subject to substantial uncertainties, often precluding the direct translation of fracture evolution into reductionist physical models. This project will use new geophysical signals and develop new machine learning/deep learning algorithms that produce fresh mathematical/statistical frameworks describing crack evolution and that create and inform improved micromechanical models that can be used to understand and predict how fracture networks respond to external perturbations, such as those resulting from fluid injection or extraction, under shallow crustal conditions.