Yuyun Yang

and 1 more

It is widely recognized that fluid injection can trigger fault slip. However, the processes by which the fluid-rock interactions facilitate or inhibit slip are poorly understood and some are neglected or oversimplified in most models of injection-induced slip. In this study, we perform a 2D antiplane shear investigation of aseismic slip that occurs in response to fluid injection into a permeable fault governed by rate-and-state friction. We account for pore dilatancy and permeability changes that accompany slip, and quantify how these processes affect pore pressure diffusion, which couples to aseismic slip. The fault response to injection has two phases. In the first phase, slip is negligible and pore pressure closely follows the standard linear diffusion model. Pressurization of the fault eventually triggers aseismic slip in the immediate vicinity of the injection site. In the second phase, the aseismic slip front expands outward and dilatancy causes pore pressure to depart from the linear diffusion model. Aseismic slip front overtakes pore pressure contours, with both subsequently advancing at constant rate along fault. We quantify how prestress, initial state variable, injection rate, and frictional properties affect the migration rate of the aseismic slip front, finding values ranging from less than 50 to 1000 m/day for typical parameters. Additionally, we compare to the case when porosity and permeability evolution are neglected. In this case, the aseismic slip front migration rate and total slip are much higher. Our modeling demonstrates that porosity and permeability evolution, especially dilatancy, fundamentally alters how faults respond to fluid injection.

Yuyun Yang

and 3 more

Recent research in real-time tsunami early warning can be broadly classified into two approaches. The first involves the use of seismic and regional geodetic data to calculate the tsunami wavefield indirectly through the estimation of earthquake source parameters. The second directly reconstructs the tsunami wavefield using data assimilation of ocean-bottom pressure sensor data such as those from DONET and S-NET (Maeda et al. 2015, Gusman et al. 2016). Data assimilation interpolates between the numerical solution and the observations to make the forecast more consistent with real data. Currently, the most popular method for forecasting the waveform is optimal interpolation, which uses a Kalman filter (KF) like approach, but holds the Kalman gain matrix fixed to reduce the runtime. This approach, coupled with tsunami Green’s functions, is very efficient and generates useful predictions. Here, we demonstrate that more accurate and stable forecasts can be obtained using the ensemble KF (enKF), a more computationally efficient variant of KF, in which the gain matrix is updated according to the physical model and the evolution of the error covariance matrix. The ensemble representation is a form of dimensionality reduction, in that only a small ensemble is propagated, instead of the joint distribution including the full covariance matrix. This method also provides a means to obtain the probability distribution of the forecast at each grid point location. We use a scenario tsunami in the Cascadia subduction zone, generated from a 2D fully-coupled dynamic rupture simulation (Lotto et al., submitted 2018). Randomly perturbed tsunami wave height data is used in the assimilation process, as we propagate the wave using a 1D linear shallow water code on a staggered grid. Better waveform agreement is achieved even in the early stages of assimilation, with much less fluctuation compared to optimal interpolation. We also explore spatial and temporal aliasing effects, in terms of the relation between observation station spacing and wavelength, as well as between assimilation and forecast time intervals. Although enKF is computationally more expensive, we are working on a fast, parallelized GPU implementation, which will significantly reduce the runtime, taking us a step closer to reliable real-time tsunami early warning.