Calibrating high-dimensional rock creep constitutive models for geological disaster prevention: An application of data assimilation methods

The study of rock creep phenomena is of paramount importance due to its potential to trigger geological disasters, such as landslides. To predict and prevent such disasters, creep constitutive models are widely employed to comprehend the time-dependent deformation of rocks. These models encompass various mechanical parameters that describe the intricate stress-strain behaviors. Nevertheless, significant challenges persist in achieving accurate and consistent parameter estimation and state prediction. In this study, we introduce three advanced data assimilation (DA) methods, including one Markov chain Monte Carlo method, DREAM_(KZS), and two ensemble smoother methods, ESMDA and ILUES. This marks the first application of such methods for calibrating rock creep models in the scenario of geological disaster prevention. We conducted numerical simulations under both low- and high-dimensional conditions to assess the performance of these DA methods. For the single partition model, all three DA methods demonstrated promising results. In the high-dimensional case, DREAM_(KZS) displayed inefficiency, while both ESMDA and ILUES proved to be still effective. ESMDA offered improved data matching but tended to underestimate parameter uncertainties, whereas ILUES excelled in addressing the issue of equifinality. In a real-world case focusing on characterizing creep deformation at the Mogu tilting deformation body near the Lianghekou Dam, China, we employed all three DA methods, and they collectively demonstrated satisfactory performance. Particularly noteworthy is the enhanced performance of the DREAM_(KZS) method during the accelerated creep phase, even in the presence of limited data. The findings of this research bear significant importance in reducing uncertainties associated with model parameters in the realm of rock mechanics, thereby advancing our capabilities in predicting and preventing disasters.