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Slow earthquakes, like regular earthquakes, result from unstable frictional slip. They produce little slip and can therefore repeat frequently. We assess their predictability using the slip history of the Cascadia subduction between 2007 and 2017, during which slow earthquakes have repeatedly ruptured multiple segments. We characterize the system dynamics using embedding theory and extreme value theory. The analysis reveals a low-dimensional (<5) nonlinear chaotic system rather than a stochastic system. We calculate properties of the underlying attractor like its correlation and instantaneous dimension, instantaneous persistence, and metric entropy. We infer that the system has a predictability horizon of the order of days weeks. For the better resolved segments, the onset of large slip events can be correctly forecasted by high values of the instantaneous dimension. Longer-term deterministic prediction seems intrinsically impossible. Regular earthquakes might similarly be predictable but with a limited predictable horizon of the order of their durations. 

Deformation of the Earth's surface associated with redistributions of continental water mass explains, to first order, the seasonal signals observed in geodetic position time series. Discriminating these seasonal signals from other sources of deformation in geodetic measurements is essential to isolate tectonic signals and to monitor spatio‐temporal variations in continental water storage. We propose a new methodology to identify and extract these seasonal signals. The approach uses a variational Bayesian Independent Component Analysis (vbICA) to extract the seasonal signals and a gravity‐based deformation model to identify which of these signals are caused by surface loading. We test the procedure on two study areas, the Arabian Peninsula and the Nepal Himalaya, and find that the technique successfully extracts the seasonal signals with one or two independent components, depending on whether the load is stationary or moving. The approach is robust to spatial heterogeneities inherent to geodetic measurements and can help extract systematic errors in geodetic products (e.g., draconitic errors). We also discuss how to handle the degree‐1 deformation field present in the geodetic data set but not captured by the gravity‐based model.

 

We report Global Positioning System (GPS) measurements of postseismic deformation following the 2015 M_w7.8 Gorkha (Nepal) earthquake, including previously unpublished data from 13 continuous GPS stations installed in southern Tibet shortly after the earthquake. We use variational Bayesian Independent Component Analysis (vbICA) to extract the signal of postseismic deformation from the GPS time series, revealing a broad displacement field extending >150 km northward from the rupture. Kinematic inversions and dynamic forward models show that these displacements could have been produced solely by afterslip on the Main Himalayan Thrust (MHT) but would require a broad distribution of afterslip extending similarly far north. This would require the constitutive parameter(a − b)σto decrease northward on the MHT to ≤0.05 MPa (an extreme sensitivity of creep rate to stress change) and seems unlikely in light of the low interseismic coupling and high midcrustal temperatures beneath southern Tibet. We conclude that the northward reach of postseismic deformation more likely results from distributed viscoelastic relaxation, possibly in a midcrustal shear zone extending northward from the seismogenic MHT. Assuming a shear zone 5–20 km thick, we estimate an effective shear‐zone viscosity of ~3·10¹⁶–3·10¹⁷ Pa·s over the first 1.12 postseismic years. Near‐field deformation can be more plausibly explained by afterslip itself and implies(a − b)σ ~ 0.5–1 MPa, consistent with other afterslip studies. This near‐field afterslip by itself would have re‐increased the Coulomb stress by ≥0.05 MPa over >30% of the Gorkha rupture zone in the first postseismic year, and deformation further north would have compounded this reloading.