Understanding the buildup of stress in space and time at tectonic plate interfaces is a key component of explaining sequences of earthquakes and aseismic slip (SEAS) at plate boundaries. In turn, successful modeling of coseismic slip and post- and interseismic fault creep can provide insights into mechanical properties of the fault zone material, as well as into potential seismic hazards. In the past, incomplete modeling of geodetic data to infer plate interface coupling has led to false assumptions of relative seismic quiescence. The issue of stress shadows in particular has led to the realization that improving our knowledge of the stress deficit on any given plate interface requires modeling the influence of past earthquakes (e.g., Hetland & Simons, 2010).This study aims to model the known historical sequence of earthquakes along the Northern Japan subduction zone in terms of post- and interseismic fault creep, at least to the extent it affects observed GNSS surface displacement timeseries of the last decades. We start from the framework presented by Kanda et al. (2013), who performed endmember simulations of multi-cycle SEAS on the Japanese megathrust, assuming varying rheological properties and fully-locked patches on the interface ("asperities") in order to match GNSS-derived interseismic plate velocities. We extend this model by (1) taking into account the entire GNSS network timeseries available in Northern Japan (containing co-, post-, and interseismic periods) and (2) integrating it into a Markov chain Monte Carlo (MCMC) solver to solve for the most probable rheological properties of the plate interface.We present the results of selected forward simulations, including timeseries of slip rate on the fault and surface displacement, and compare them to GNSS observations. We furthermore present preliminary results of the inferred best-fitting rheological parameters (assuming rate-dependent friction). 

Understanding the nature and behavior of the rocks in boundary zones between tectonic plates is important to improve our understanding of earthquake-associated hazard. Laboratory experiments can derive models that explain material behavior on small scales and under controlled conditions. These models can also be tested on observations of surface motion near plate boundaries: Fitting surface displacements from earthquakes (either shortterm offsets or longterm motion) yields estimates of rock properties for each model. However, using only observations from a single earthquake (from immediately after the quake and/or the subsequent years), may not allows us to confidently distinguish between models. In this study, we investigate the potential of using the displacement timeseries from multiple earthquakes, as well as the period between the quakes, to distinguish between proposed models. We use methods that enable comparison between models and parameters taking into account uncertainties, and perform our assessment on an artificial dataset.

Regional-scale, continuously-operating Global Navigation Satellite System (GNSS) networks are a powerful tool to monitor plate motion and surface deformation. Since their inception, their size, density, and length of observation record have steadily increased throughout the world. Simultaneously, researchers have had to write accompanying software to enable the analysis (especially the decomposition) of the ever-increasing amount of available timeseries in an efficient way. These codes and respective studies have individually set standards for different subsets of the following desirable qualities: portability (between locations), speed (code runtime), automation (avoiding or simplifying manual inspection of each station), use of spatial correlation (exploiting the fact that stations experience common signals), availability (open source), and documentation (of the usage and underlying methods). In this study, we present the DISSTANS Python package, which aims to combine the aforementioned achievements in a single software by offering generic (therefore portable), parallelizable (fast) methods that can exploit the spatial structure of the observation records in a user-assisted, semi-automated framework, including uncertainty propagation. The code is open source, includes an application interface documentation as well as usage tutorials, is easily extendable, and is based on the previously published and validated method of Riel et al. (2014). We also present two case studies to validate our code, one using a synthetic dataset and one using real GNSS network timeseries.