To better understand the influence of stress changes over floating ice shelves on grounded ice streams, we develop a Bayesian method for inferring time‐dependent 3‐D surface velocity fields from synthetic aperture radar (SAR) and optical remote sensing data. Our specific goal is to observe ocean tide‐induced variability in vertical ice shelf position and horizontal ice stream flow. Thus, we consider the special case where observed surface displacement at a given location can be defined by a 3‐D secular velocity vector, a family of 3‐D sinusoidal functions, and a correction to the digital elevation model used to process the SAR data. Using nearly 9 months of SAR data collected from multiple satellite viewing geometries with the COSMO‐SkyMed 4‐satellite constellation, we infer the spatiotemporal response of Rutford Ice Stream, West Antarctica, to ocean tidal forcing. Consistent with expected tidal uplift, inferred vertical motion over the ice shelf is dominated by semidiurnal and diurnal tidal constituents. Horizontal ice flow variability, on the other hand, occurs primarily at the fortnightly spring‐neap tidal period (Msf). We propose that periodic grounding of the ice shelf is the primary mechanism for translating vertical tidal motion into horizontal flow variability, causing ice flow to accelerate first and most strongly over the ice shelf. Flow variations then propagate through the grounded ice stream at a mean rate of ∼29 km/d and decay quasi‐linearly with distance over ∼85 km upstream of the grounding zone.

Great earthquakes rarely occur within active accretionary prisms, despite the intense long-term deformation associated with the formation of these geologic structures. This paucity of earthquakes is often attributed to partitioning of deformation across multiple structures as well as aseismic deformation within and at the base of the prism (Davis et al., 1983). We use teleseismic data and satellite optical and radar imaging of the 2013 M w 7.7 earthquake that occurred on the southeastern edge of the Makran plate boundary zone to study this unexpected earthquake. We first compute a multiple point-source solution from W-phase waveforms to estimate fault geometry and rupture duration and timing. We then derive the distribution of subsurface fault slip from geodetic coseismic offsets. We sample for the slip posterior probability density function using a Bayesian approach, including a full description of the data covariance and accounting for errors in the elastic structure of the crust. The rupture nucleated on a subvertical segment, branching out of the Chaman fault system, and grew into a major earthquake along a 50°north-dipping thrust fault with significant along-strike curvature. Fault slip propagated at an average speed of 3:0 km=s for about 180 km and is concentrated in the top 10 km with no displacement on the underlying décollement. This earthquake does not exhibit significant slip deficit near the surface, nor is there significant segmentation of the rupture. We propose that complex interaction between the subduction accommodating the Arabia-Eurasia convergence to the south and the Ornach Nal fault plate boundary between India and Eurasia resulted in the significant strain gradient observed prior to this earthquake. Convergence in this region is accommodated both along the subduction megathrust and as internal deformation of the accretionary wedge.

Constellations of Synthetic Aperture Radar (SAR) satellites with short repeat time acquisitions allow exploration of active faults behavior with unprecedented temporal resolution. Along the North Anatolian Fault (NAF) in Turkey, an 80 km long section has been creeping at least since the 1944, Mw 7.3 earthquake near Ismetpasa, with a current Interferometric Synthetic Aperture Radar (InSAR)‐derived average creep rate of 8 ± 3 mm/yr (i.e., a third of the NAF long‐term slip rate). We use a dense set of SAR images acquired by the COSMO‐SkyMed constellation to quantify the spatial distribution and temporal evolution of creep over 1 year. We identify a major burst of aseismic slip spanning 31 days with a maximum slip of 2 cm, between the surface and 4 km depth. This result shows that fault creep along this section of the NAF does not occur at a steady rate as previously thought, highlighting a need to revise our understanding of the underlying fault mechanics.

The subduction zone in northern Chile is a well‐identified seismic gap that last ruptured in 1877. On 1 April 2014, this region was struck by a large earthquake following a two week long series of foreshocks. This study combines a wide range of observations, including geodetic, tsunami, and seismic data, to produce a reliable kinematic slip model of the Mw=8.1 main shock and a static slip model of the Mw=7.7 aftershock. We use a novel Bayesian modeling approach that accounts for uncertainty in the Green's functions, both static and dynamic, while avoiding nonphysical regularization. The results reveal a sharp slip zone, more compact than previously thought, located downdip of the foreshock sequence and updip of high‐frequency sources inferred by back‐projection analysis. Both the main shock and the Mw=7.7 aftershock did not rupture to the trench and left most of the seismic gap unbroken, leaving the possibility of a future large earthquake in the region.

Interferometric synthetic aperture radar (InSAR) has become an important geodetic tool for measuring deformation of Earth's surface due to various geophysical phenomena, including slip on earthquake faults, subsurface migration of magma, slow‐moving landslides, movement of shallow crustal fluids (e.g., water and oil), and glacier flow. Airborne and spaceborne synthetic aperture radar (SAR) instruments transmit microwaves toward Earth's surface and detect the returning reflected waves. The phase of the returned wave depends on the distance between the satellite and the surface, but it is also altered by atmospheric and other effects. InSAR provides measurements of surface deformation by combining amplitude and phase information from two SAR images of the same location taken at different times to create an interferogram. Several existing open‐source analysis tools [Rosen et al., 2004; Rosen et al., 2011; Kampes et al., 2003; Sandwell et al., 2011] enable scientists to exploit observations from radar satellites acquired at two different epochs to produce a surface displacement map.

Lying below Vatnajökull ice cap in Iceland, Bár arbunga stratovolcano began experiencing wholesale caldera collapse in 2014 August 16, one of the largest such events recorded in the modern instrumental era. Simultaneous with this collapse is the initiation of a plate boundary rifting episode north of the caldera. Observations using the international constellation of radar satellites indicate rapid 50 cm d −1 subsidence of the glacier surface overlying the collapsing caldera and metre-scale crustal deformation in the active rift zone. Anomalous earthquakes around the rim of the caldera with highly nondouble-couple focal mechanisms provide a mechanical link to the dynamics of the collapsing magma chamber. A model of the collapse consistent with available geodetic and seismic observations suggests that the majority of the observed subsidence occurs aseismically via a deflating sill-like magma chamber.

Citation:Riel, B., M. Simons, P. Agram, and Z. Zhan (2014) Abstract We present a new method for automatically detecting transient deformation signals from geodetic time series. We cast the detection problem as a least squares procedure where the design matrix corresponds to a highly overcomplete, nonorthogonal dictionary of displacement functions in time that resemble transient signals of various timescales. The addition of a sparsity-inducing regularization term to the cost function limits the total number of dictionary elements needed to reconstruct the signal. Sparsity-inducing regularization enhances interpretability of the resultant time-dependent model by localizing the dominant timescales and onset times of the transient signals. Transient detection can then be performed using convex optimization software where detection sensitivity is dependent on the strength of the applied sparsity-inducing regularization. To assess uncertainties associated with estimation of the dictionary coefficients, we compare solutions with those found through a Bayesian inference approach to sample the full model space for each dictionary element. In addition to providing uncertainty bounds on the coefficients and confirming the optimization results, Bayesian sampling reveals trade-offs between dictionary elements that have nearly equal probability in modeling a transient signal. Thus, we can rigorously assess the probabilities of the occurrence of transient signals and their characteristic temporal evolution. The detection algorithm is applied on several synthetic time series and real observed GPS time series for the Cascadia region. For the latter data set, we incorporate a spatial weighting scheme that self-adjusts to the local network density and filters for spatially coherent signals. The weighting allows for the automatic detection of repeating slow slip events.

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