[1] Repeat-pass Interferometric Synthetic Aperture Radar (InSAR) provides spatially dense maps of surface deformation with potentially tens of millions of data points. Here we estimate the actual covariance structure of noise in InSAR data. We compare the results for several independent interferograms with a large ensemble of GPS observations of tropospheric delay and discuss how the common approaches used during processing of InSAR data affects the inferred covariance structure. Motivated by computational concerns associated with numerical modeling of deformation sources, we then combine the datacovariance information with the inherent resolution of an assumed source model to develop an efficient algorithm for spatially variable data resampling (or averaging). We illustrate these technical developments with two earthquake scenarios at different ends of the earthquake magnitude spectrum. For the larger events, our goal is to invert for the coseismic fault slip distribution. For smaller events, we infer the hypocenter location and moment. We compare the results of inversions using several different resampling algorithms, and we assess the importance of using the full noise covariance matrix.

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.

[1] 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.

[1] We investigate the depths of crustal earthquakes (<80 km depth) of the central Andes (5°S to 35°S) to constrain the relationship between earthquake locations and inferred faults. We assemble parameters from 138 moderate-sized (7.0 > Mw ≳ 5.5) earthquakes from the Global CMT catalog and previous work spanning 1944-2007. For 38 well-recorded events, we use teleseismic P and SH waveforms to model the strike, dip, rake, focal depth, and source time function. We use InSAR observations of surface deformation from 9 earthquakes to compare inferred fault parameters with the waveform inversions and global catalogs to assess their accuracies. While the depths from the InSAR and waveform analyses generally agree within error, horizontal and depth errors in global catalogs are 10 to 50 km, as found elsewhere. As noted in previous work, the majority of crustal earthquakes occur in the Eastern Cordillera and foreland regions of the central Andes, although a few normal and strike-slip earthquakes occur beneath the Altiplano plateau and in the forearc in southern Peru and northernmost Chile. We propose a new interpretation of one of the basement thrusts (Shira Mountain, Peru) as a pop up block on the basis of our new earthquake depths. We confirm that earthquakes in the flat slab areas of Peru and Argentina are within the sometimes aseismic lower crust. Lower crustal earthquakes are globally found in all types of tectonic settings only when the thermal lithosphere is more than 80 km thick and the amount of recent shortening/extension is <30%.