In late August 2005, a swarm of more than a thousand earthquakes between magnitudes 1 and 5.1 occurred at the Obsidian Buttes, near the southern San Andreas Fault. This swarm provides the best opportunity to date to assess the mechanisms driving seismic swarms along transform plate boundaries. The recorded seismicity can only explain 20% of the geodetically observed deformation, implying that shallow, aseismic fault slip was the primary process driving the Obsidian Buttes swarm. Models of earthquake triggering by aseismic creep can explain both the time history of seismic activity associated with the 2005 swarm and the ∼1 km/h migration velocity exhibited by this and several other Salton Trough earthquake swarms. A combination of earthquake triggering models and denser geodetic data should enable significant improvements in time‐dependent forecasts of seismic hazard in the key days to hours before significant earthquakes in the Salton Trough.

Geoscientists increasingly rely on coseismic slip distributions inferred from geodetic observations to drive sophisticated models of the seismic cycle. To date, little work has been done on optimizing the parameterization of these fault models so that they reflect the resolving power of observed surface displacements. The locations of noisy surface displacement observations are often widely scattered far from features we wish to analyze in the subsurface and result in highly variable model resolution with depth. The few attempts to produce variably discretized fault planes are arduously constructed by hand and may not correctly reflect the ability of the data to resolve slip features. Motivated by the increasing size of geodetic data pools and the need for distributed slip models that accurately represent features the data can resolve, we present a fully automated algorithm that iteratively adjusts the sizes of dislocations in a fault model. We use the concept of smoothing scales, derived from the model resolution matrix, to resize dislocations so that each dislocation is sized appropriately given the area over which slip in that region of the fault would be smoothed. We present a series of synthetic tests that utilize both sparse and dense data sets, and we compare our variably discretized inversions to traditional regularly discretized inversions. We also use our approach to invert for slip from geodetic observations of the 2004 M_w 6.0 Parkfield, California, earthquake and the 1995 M_w 8.1 Antofagasta, Chile, earthquake.

Abstarct: Constraints on soil moisture can guide agricultural practices, act as input into weather, flooding and climate models and inform water resource policies. Space-based interferometric synthetic aperture radar (InSAR) observations provide near-global coverage, even in the presence of clouds, of proxies for soil moisture derived from the amplitude and phase content of radar imagery. We describe results from a 1.5 year-long InSAR time series spanning the March, 2015 extreme precipitation event in the hyperarid Atacama desert of Chile, constraining the immediate increase in soil moisture and drying out over the following months, as well as the response to a later, smaller precipitation event. The inferred temporal evolution of soil moisture is remarkably consistent between independent, overlapping SAR tracks covering a region ~100 km in extent. The unusually large rain event, combined with the extensive spatial and temporal coverage of the SAR dataset, present an unprecedented opportunity to image the time-evolution of soil characteristics over different surface types. Constraints on the timescale of shallow water storage after precipitation events are increasingly valuable as global water resources continue to be stretched to their limits and communities continue to develop in flood-prone areas.