A snapshot of the long-term evolution of a distributed tectonic plate boundary.

Along a plate boundary, why deformation and seismic hazard distributes across multiple active faults or along a single major structure remains unknown. The transpressive Chaman plate boundary (CPB) is a wide faulted region of distributed deformation and seismicity that accommodates the differential motion between India and Eurasia at 30 mm/year. However, main identified faults, including the Chaman fault, only accommodate 12 to 18 mm/year of relative motion and large earthquakes (Mw > 7) occurred east of them. We use Interferometric Synthetic Aperture Radar to locate the missing strain and identify active structures. The current displacement is partitioned between the Chaman fault, Ghazaband fault and a recent, immature but fast fault zone to the east. Such partitioning matches known seismic ruptures and results in the ongoing widening of the plate boundary, potentially controlled by the depth of the brittle-ductile transition. The CPB illustrates the impact of geological time scale deformation on today's seismic activity.

Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning.

Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault's location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.

Seismic and Aseismic Fault Slip During the Initiation Phase of the 2017 M W = 6.9 Valparaíso Earthquake.

Transient deformation associated with foreshocks activity has been observed before large earthquakes, suggesting the occurrence of a detectable preseismic slow slip during the initiation phase. A critical issue consists in discriminating the relative contributions from seismic and aseismic fault slip during the preparation phase of large earthquakes. We focus on the April-May 2017 Valparaíso earthquake sequence, which involved a M W  = 6.9 earthquake preceded by intense foreshock activity. To assess the relative contribution of seismic and aseismic slip, we compare surface displacement predicted from foreshocks source models with transient motion measured prior to the mainshock. The comparison between observed and predicted displacements shows that only half of the total displacement can be explained by the contribution of foreshocks. This result suggests the presence of aseismic pre-slip during an initiation phase preceding the mainshock.

Seismogenic Potential of the Main Himalayan Thrust Constrained by Coupling Segmentation and Earthquake Scaling.

Recent studies have shown that the Himalayan region is under the threat of earthquakes of magnitude nine or larger. These estimates are based on comparisons of the geodetically inferred moment deficit rate with the seismicity of the region. However, these studies did not account for the physics of fault slip, specifically the influence of frictional barriers on earthquake rupture dynamics, which controls the extent and therefore the magnitude of large earthquakes. Here we combine an improved probabilistic estimate of moment deficit rate with results from dynamic models of the earthquake cycle to more fully assess the seismogenic potential of the Main Himalayan Thrust (MHT). We propose a straightforward and efficient methodology for incorporating outcomes of physics-based earthquake cycle models into hazard estimates. We show that, accounting for uncertainties on the moment deficit rate, seismicity and earthquake physics, the MHT is prone to rupturing in M w 8.7 earthquakes every T > 200 years.

Signature of transition to supershear rupture speed in the coseismic off-fault damage zone.

Most earthquake ruptures propagate at speeds below the shear wave velocity within the crust, but in some rare cases, ruptures reach supershear speeds. The physics underlying the transition of natural subshear earthquakes to supershear ones is currently not fully understood. Most observational studies of supershear earthquakes have focused on determining which fault segments sustain fully grown supershear ruptures. Experimentally cross-validated numerical models have identified some of the key ingredients required to trigger a transition to supershear speed. However, the conditions for such a transition in nature are still unclear, including the precise location of this transition. In this work, we provide theoretical and numerical insights to identify the precise location of such a transition in nature. We use fracture mechanics arguments with multiple numerical models to identify the signature of supershear transition in coseismic off-fault damage. We then cross-validate this signature with high-resolution observations of fault zone width and early aftershock distributions. We confirm that the location of the transition from subshear to supershear speed is characterized by a decrease in the width of the coseismic off-fault damage zone. We thus help refine the precise location of such a transition for natural supershear earthquakes.

An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia.

Slow slip events result from the spontaneous weakening of the subduction megathrust and bear strong resemblance to earthquakes, only slower. This resemblance allows us to study fundamental aspects of nucleation that remain elusive for classic, fast earthquakes. We rely on machine learning algorithms to infer slow slip timing from statistics of seismic waveforms. We find that patterns in seismic power follow the 14-month slow slip cycle in Cascadia, arguing in favor of the predictability of slow slip rupture. Here, we show that seismic power exponentially increases as the slowly slipping portion of the subduction zone approaches failure, a behavior that shares a striking similarity with the increase in acoustic power observed prior to laboratory slow slip events. Our results suggest that the nucleation phase of Cascadia slow slip events may last from several weeks up to several months.