The zone of interaction between the Cocos (CO), Caribbean (CA) and North America (NA) plates in Guatemala is defined by the sub-parallel Motagua and Polochic strike-slip faults, a series of north-south-trending extensional grabens immediately south of the Motagua Fault, the Middle America trench, and faults within the Middle America volcanic arc. Historical earthquakes associated with these faults include the destructive 1976 Mw 7.5 earthquake along the Motagua fault and the 2012 Mw 7.5 Champerico thrust earthquake. The latest published GPS-based present-day kinematic model of the region shows that about two-thirds of the strain accumulation from the NA/CA relative motion concentrates on the Motagua fault and one third across the Polochic fault, suggesting that slip varies with time as a result of mechanical interactions within the Motagua-Polochic fault system. As part of the efforts to quantify the present-day kinematics and slip behavior of these faults, we use interferometric synthetic aperture radar (InSAR) to measure the strain rates across faults in Guatemala and to constrain slip partitioning among them. We processed L-band ALOS-1 images spanning from 2006 to 2011, and C-band Sentinel-1 images spanning from 2015 to 2019, from ascending and descending tracks covering the Polochic and Motagua faults, the Ipala and Guatemala City grabens, and part of the volcanic arc to the south. We are using the New Small temporal and spatial baselines (NSBAS) workflow to compute the interferograms, make tropospheric and ionospheric corrections, and perform time-series analysis. We present the first InSAR-based maps of interseismic velocity for this region, which will contribute to the refinement of interseismic locking estimates across the Motagua-Polochic fault system, the subduction zone, and other nearby faults.

Seismic hazard assessment in active fault zones can benefit of strain rate measurements derived from geodetic data. Producing a continuous strain rate map from discrete data is an inverse problem traditionally tackled with standard interpolation schemes. Most algorithms require user-defined regression parameters that determine the smoothness of the recovered velocity field, and the amplitude of its spatial derivatives. This may lead to biases in the strain rates estimation which could eventually impact studies on earthquake hazard. Here we propose a transdimensional Bayesian method to estimate surface strain rates from GNSS velocities. We parameterize the velocity field with a variable number of Delaunay triangles, and use a reversible jump Monte-Carlo Markov Chain algorithm to sample the probability distribution of surface velocities and spatial derivatives. The solution is a complete probability distribution function for each component of the strain rate field. We conduct synthetic tests and compare our approach to a standard b-spline interpolation scheme. Our method is more resilient to data errors and uneven data distribution, while providing uncertainties associated with recovered velocities and strain rates. We apply our method to the Southwestern US, an extensively studied and monitored area and infer probabilistic strain rates along the main fault systems, including the San Andreas one, from the inversion of interseismic GNSS velocities. Our approach provide a full description of the strain rate tensor for zones where strain rates are highly contrasted, with no need to manually tune user-defined parameters. We recover sharp velocity gradients, without systematic artifacts.