Abstract Near-field tsunamis can leave coastal communities with only minutes to reach safe areas, so how quickly people begin moving after the earthquake shaking is critical to evacuation success. Static evacuation maps do not represent this time dependence, whereas more complex evacuation models can be data-intensive, costly, or difficult to scale. We introduce a simple, low-cost tool that combines Global Positioning System (GPS) tracks recorded with mobile applications during evacuation drills with tsunami inundation modeling. As a proof of concept, we apply the tool in Viña del Mar (central Chile), where a large tsunami is expected and evacuation is particularly challenging because of the long distances required to reach safe areas. We analyzed 119 GPS trajectories collected during drills between 2022 and 2026 and compared them with simulated inundation for a plausible subduction earthquake scenario. For illustration, each trajectory was evaluated under three departure times measured from the earthquake origin time: 0 min (T0), 8 min (T8), and 12 min (T12). T0 represents immediate evacuation, T8 departure when a warning is issued, and T12 departure when the tsunami is already reaching the coast. A trajectory was considered feasible if the participant completed the evacuation before being reached by the simulated inundation. Of the 119 trajectories, 100 were feasible under T0, 49 under T8, and only 3 under T12, showing how strongly evacuation success depends on when people begin moving. Most trajectories followed 13 main evacuation corridors. Feasibility varied sharply among corridors, even between neighboring routes within the same departure scenario. By overlaying the trajectories on the advancing inundation, the approach produces intuitive videos that make the consequences of delayed departure visible to residents, authorities, and decision-makers. Inexpensive and easily replicable, the method complements detailed evacuation modeling and may be particularly useful in tsunami-prone communities with limited resources, supporting evacuation awareness, preparedness, and risk communication.
Abstract Characterizing the physical properties of frozen lakes, such as ice thickness and water depth, is essential for diverse applications, including ice safety assessment and climate change monitoring. Typically, these properties are measured through labor-intensive field surveys, limiting their spatial and temporal coverage. Alternatively, frozen lake environments can be investigated using seismology. Seismic waves propagating on floating ice travel at velocities that depend on physical properties of the ice–water system, including the elastic properties and thickness of the ice and the water depth. Because seismic waves are frequently generated and can propagate through entire lake systems, they have greater spatiotemporal coverage than field measurements. In this study, we investigate the use of wind-generated flexural waves recorded by a distributed acoustic sensing (DAS) system to infer ice thickness and water depth along a 1000 m fiber-optic cable installed on Lake Pääjärvi, southern Finland. We detect flexural waves in the 0.01–0.5 Hz frequency band, measure their phase-velocity dispersion curves, estimate ice thickness and water depth along four cable intervals, and compare the estimates with field measurements and the lake bathymetry. The ice-thickness estimates are comparable to field measurements, with differences ranging from 2% to 34%, whereas the water-depth estimates underestimate bathymetry values by 22%–60% while resolving the increasing water-depth trend along the cable. We find that water-depth estimates tend to become less robust as the water depth increases because the measured dispersion curves lack sufficiently low wavenumbers to resolve the water column. We illustrate that flexural waves can refract toward the shore as they approach shallow water. This effect must be considered when estimating true phase velocities from apparent velocities measured along the cable to avoid bias in the estimated ice thickness and water depth. Our results demonstrate the potential of DAS to leverage ambient signals for monitoring frozen lakes.
Abstract The 7 January 2025 Mw 7.1 Tingri earthquake ruptured the southern Xainza–Dinggye rift in the Tibetan Plateau, ∼200 km north of the Himalayan frontal thrust system. This rift lies in a network of discrete normal and strike-slip faults that accommodate gravitational collapse and lateral extrusion of the Plateau with overall India–Eurasia convergence. Subpixel image correlation and coseismically shifted digital elevation model differencing of high-resolution (∼0.5 m) satellite optical imagery reveal the full 3D coseismic displacement field of the earthquake. Rupture occurred on two west-dipping faults: the Lagoi fault in the north, cutting through mountainous terrain at relatively high elevation (∼5500 m) within granitic–gneissic rocks, and the Dingmu Co fault in the south, bounding a lower elevation (∼4200 m) basin within Tethyan metasedimentary rocks. Northern deformation consists of coherent, block-like oblique hanging wall subsidence with left-lateral slip (>2 m). Southern deformation is more distributed, with comparable magnitudes of subsidence and northwestward translation accommodated along a series of hanging wall faults. Footwall rebound is minimal and occurs over shorter distances than observed in the long-term landscape record. Between these domains, a ∼5–10 km wide east–west transition zone absorbs diffuse north–south contraction (∼1 m) and right-lateral motion (∼1.5 m), above a concave-up along-strike curved fault imaged by aftershocks that may direct slip toward its deepest center. Collectively, these observations indicate the control of topographic stresses, lithology, fault geometry, and a weak lower crust in partitioning discrete and diffuse deformation during a single earthquake, reproducing Plateau-wide kinematic patterns typically inferred from geodetic and geomorphic observations over much longer timescales.
Abstract On 6 February 2023, an earthquake doublet occurred in the East Anatolian fault zone (EAFZ), producing significant surface displacements and causing extensive damage in Türkiye and Syria. The EAFZ is known to be spatially complex, with multiple segments, fault stepovers, and fault-bend structures. How such fault complexities modulate the co- and postseismic distribution of fault slip remains an important question. We use Sentinel-1 and Advanced Land Observation Satellite-2 Synthetic Aperture Radar-derived pixel offset measurements and Global Navigation Satellite Systems (GNSS) static offsets for the two events to constrain the distribution of coseismic slip along both strands of the EAFZ. We constrain the postseismic slip distribution using Sentinel-1 interferograms and GNSS time series. We adopt a Bayesian framework to explore the solution space, relying solely on physically justifiable prior information without employing artificial regularization. We account for both observational errors and epistemic uncertainties due to imperfect knowledge of the regional elastic structure and orientation of the fault plane. We find that elastic afterslip mostly surrounds the areas of coseismic rupture. However, the geodetic data sets that span the postseismic period cannot be explained solely by elastic afterslip. We also confirm the potential seismic gap on the boundary between the Erkenek and Pütürge faults, across which neither coseismic nor postseismic slip occurs in the periods considered here.
Abstract New earthquake forecasting models are increasingly reported to outperform established models such as the epidemic-type aftershock sequence (ETAS) model. However, the scientific meaning of such improvements depends critically on how the benchmark is constructed. I use the term “under-implemented ETAS” for a benchmark that is unnecessarily restricted, weakly optimized, or otherwise disadvantaged, and propose the Parity of information, Optimization, Generality, and Assessment (POGA) test as a simple framework for assessing model comparisons. The purpose is not to protect ETAS from competition, but to ensure that improvements over established models are scientifically interpretable. A strong benchmark makes victory harder, but also reveals what a new model has genuinely learned that its competitor could not.
