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Guest post: Photos and Preliminary Observations from an Overview Flight of the 6 December 2025 Hubbard Glacier Earthquake, Yukon Territory, Canada

EOS - Mon, 01/05/2026 - 07:34

Yukon Geological Survey

Contributors: Derek Cronmiller, Theron Finley, Panya Lipovsky, Jan Dettmer

A guest post featuring images and a commentary of landslides in Yukon Territory in Canada triggered by the 6 December 2025 M=7.0 Hubbard Glacier Earthquake.

The 6 December 2025 Mw=7.0 Hubbard Glacier Earthquake in the St. Elias Mountains of Yukon caused widespread mass wasting activity in an area near Mt. Logan, Canada. On 12 December 2025, the Yukon Geological Survey completed an overview flight of the area to collect photographs and document seismically induced activity in the region. Based on the preliminary USGS finite fault model, the earthquake rupture appears to have been shallow, with approximately 2 m of slip occurring at ~6 km depth but no evidence of earthquake surface rupture was identified. However, we documented extensive surface effects including more than 200 landslides, many snow and ice avalanches, and widespread damage to glaciers throughout the area.

View southwest towards Mt King George. Snow avalanche and serac collapse scars are visible on the ridge in the foreground, large rock avalanche scars on the NE face of Mt. King George are visible in the background, triggered by the Landslides from the 6 Dec 2025 Hubbard Glacier Earthquake.

Landslide activity was concentrated on the Mt King George massif, where rock–ice avalanches and rockfall were the most common failure types. Based on preliminary earthquake relocations, the King George massif directly overlies a portion of the fault rupture and rises to a height of 3,741 m, approximately 1,900 m above the surrounding Hubbard Glacier.

Landslide scars and debris on the NW end of the King George massif, looking toward Mt Logan (5,959 m). Note the lack of snow cover here due to concentrated avalanche and landslide activity, as compared to distant peaks.

Landslide activity continued for several days after the main earthquake, likely due to a combination of aftershocks and progressive failure of slopes that were damaged by the earthquake or destabilized by earlier landsliding. At least one rock avalanche occurred between 11 and 12 December as constrained by Landsat imagery. At the time of the overview flight, slide scars on the east and northeast sides of Mt King George remained active, with ongoing rockslides and rockfall producing large dust clouds. The large slide scar on the east face of Mt King George appeared to have liquid water flowing down its centre, suggesting either discontinuous permafrost within the massif or significant heating associated with slope failure.

A large landslide scar on the east face of Mt King George appeared to have liquid water running out from approximately halfway down the scar (arrow). This scar was producing active rockfall at the time of the overview flight and filling adjacent valleys with dust.

The largest observed landslide was a rock and ice avalanche produced by a partial collapse of the southwest ridge of Mt King George. The basal failure surface occurred along a southwest-dipping planar discontinuity oriented subparallel with the pre-existing slope of the south flank of the ridge . The crown of the slide originated at approximately 3,000 m above sea level and descended roughly 1,300 m along a tributary glacier before coming to rest on the Hubbard Glacier, approximately 7.4 km from the source area. This corresponds to an overall travel angle of approximately 10 degrees. Such high mobility is typical of rock avalanches on glaciers (c.f. Evans and Clague 1988), where movement is enhanced by the low-friction surface of the glacier, entrainment of snow and ice, and by water inputs generated through frictional melting (Sosio et al., 2012).

Source area and runout of the largest (9 square km) landslide triggered by the Mw=7.0 6 December 2025 Hubbard Glacier Earthquakeearthquake on the southwest ridge of Mt King George. The planar surface of failure for the largest rock and ice avalanche on the southern flank of the SW ridge of Mt King George.

Snow avalanches were common on all aspects and were triggered in a more extensive region than landslides. Some of the largest snow avalanches occurred on the north and east aspects of Mt King George and McArthur Peak (a sub-peak of Mt Logan) and produced plumes that in some cases extended 2-3 km across the glacier at the base of the slopes where they initiated. Damage to glaciers was also extensive; the collapse of snow bridges and seracs on icefalls was widespread on the Hubbard Glacier and adjacent tributaries. A partial collapse of a glacier occurred between Mt King George and Mt Queen Mary, an area immediately above many of the most intense aftershocks. This portion of glacier is covered by debris from a rock avalanche off the NE face on Mt King George which may have contributed to its failure. The most intensely affected area is in a popular recreation zone of Kluane National Park and may have significant impact on the objective hazards skiers and mountaineers face in the region over the coming years.

Partial collapse of a glacier between Mt King George and Mt Queen Mary. The length of the failure is approximately 2.4 km. Widespread collapse of seracs and snow bridges on the south side of Mt Queen Mary. The field of view mid-photo is approximately 5 km across. Widespread collapse of seracs and snow bridges on the south side of Mt Queen Mary. The field of view mid-photo is approximately 5 km across. Rock avalanche below Mt King George triggered by the 6 December 2025 Hubbard Glacier Earthquake. The runout distance is 1.4 km.

All photos provided courtesy of the Government of Yukon.

References:

Evans, S. G., & Clague, J. J. 1988. Catastrophic rock avalanches in glacial environments. In Proceedings of the Fifth International Symposium on Landslides, 2, pp. 1153–1158. Lausanne, Switzerland.

Sosio R., Crosta, G.B., Chen, J.H. and Hungr, O. 2012. Modelling rock avalanche propagation onto glaciers. Quaternary Science Reviews 47, 23–40

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Transport-Map Proposals for Efficient Markov Chain Monte Carlo

Geophysical Journal International - Mon, 01/05/2026 - 00:00

SummaryEfficient Markov chain Monte Carlo (MCMC) sampling from posterior distributions remains a central challenge in Bayesian geophysical inversion. Recent developments in computational statistics and optimal transport suggest that MCMC efficiency can be improved by reparameterising the sampling problem – specifically, by learning an invertible mapping that recasts the target distribution onto a simpler reference distribution. Here, we introduce a Metropolis–Hastings framework that leverages transport maps parameterised by invertible neural networks. These maps are trained on preliminary MCMC samples from the target distribution and used to propose new samples in a fixed reference space, where proposal design is independent of the target’s structure. The proposed samples are transformed back to the target space via the inverse map, and accepted or rejected according to a modified Metropolis–Hastings criterion. As sampling proceeds, the transport maps are updated, yielding proposals increasingly well adapted to the shape of the target distribution. Across a suite of numerical tests – including a 2-D Rosenbrock distribution, a 3-D earthquake location problem, and Gaussian mixtures up to 16 dimensions – transport-map-driven samplers consistently outperform standard MCMC, reducing integrated autocorrelation times by factors of 2.5 to over 6 (or equivalently, yielding sample sets 2.5–6 times larger for the same number of forward evaluations). This improvement comes at the non-negligible cost of training one or more transport maps, which we quantify systematically. We also provide a quantitative criterion for weighing training cost against sampling speed-up. This shows that transport-map MCMC is advantageous whenever the forward problem is nontrivial, making it a promising approach for Bayesian sampling in geophysics and beyond.

Which is better: deep-learning or manual seismic arrival-time picking?

Geophysical Journal International - Mon, 01/05/2026 - 00:00

SummaryEarthquake locations and catalogs from routine earthquake monitoring are typically based on manually reviewed arrival-time picks from classical, rule-based automatic pickers. High-performance, deep-learning (DL) pickers can replace this standard approach, rapidly delivering much larger and complete catalogs. A transition to routine monitoring based on DL picks requires that resulting catalogs include all or almost all events identified by current procedures with locations of the same or higher quality. Here we verify these requirements by comparing the performance of DL and manual picking for earthquake relocation and tomographic inversion. We form a reference catalog with a subset of INGV bulletin events and picks from the 2016 Central Italy sequence. This catalog is re-picked using the DL picker PhaseNet trained on the Northern California Earthquake Data Center dataset and on the INSTANCE Italian dataset. We use these three pick sets for high-precision, non-linear earthquake relocation and for 3D tomographic inversion and relocation. Relative to the high-precision relocations using routine picks, those using DL picks show improved organization and clustering, and, in a ground-truth test, smaller hypocenter separation for event pairs with more similar waveforms. The tomographic inversions show statistically better convergence and more organized relocations using the DL picks than with the routine picks. We conclude that DL based monitoring can rapidly produce more consistent picks and higher quality catalogs than standard procedures, while freeing analyst time for improved quality control, assessment, interpretation, and dissemination of information on seismic activity, especially during significant seismic sequences.

The poroelastic stress and pore pressure effects on delayed seismicity based on fully coupled fluid-solid simulations and rate-and-state friction laws

Geophysical Journal International - Mon, 01/05/2026 - 00:00

SummaryFluid injection into the subsurface can trigger moderate-magnitude earthquakes days to months after shut-in, complicating hazard assessment. To investigate the governing mechanics, we implemented a fully coupled hydro–mechanical model that couples Darcy flow, poro-viscoelastic deformation and rate-and-state fault friction on a planar fault, allowing two-way feedbacks between pore pressure, volumetric strain and fault slip and the simulation of both aseismic and seismic transients. Compared with decoupled or one-way approaches, the fully coupled formulation generally yields longer post-injection delays, owing to poroelastic stress contributions and a more realistic evolution of volumetric strain. After shut-in, a slow poroelastic redistribution of volumetric compression broadens and migrates along the fault, constructively overlapping regions of elevated shear and reduced effective normal stress. This causes the nucleation of a delayed rupture away from the well, indicating that the point of peak instantaneous pressure does not necessarily coincide with the location of maximum coseismic slip. By scanning permeability and injection rate we construct an empirical injection-rate (IR)–permeability (k) phase diagram that delineates regimes of immediate, delayed and no induced seismicity; this diagram is offered as a conceptual, physics-informed screening tool that requires site-specific calibration. Our results indicate that two-way hydro-mechanical coupling and fault slip evolution should be considered when assessing post-injection seismic hazard and in the design of spatially distributed monitoring.

Seafloor Topography Predicted from SWOT Gravity Data by Deep Neural Network in the Northwestern Pacific Ocean

Geophysical Journal International - Mon, 01/05/2026 - 00:00

SummaryTraditional seafloor mapping relies on shipborne soundings which have limited spatial coverage. The Surface Water and Ocean Topography (SWOT) wide-swath altimetry satellite holds the potential for predicting more detailed seafloor topography. In this study, we integrate SWOT gravity data with single-beam shipborne depths to construct seafloor topography models in the Northwestern Pacific using the deep neural network (DNN) method. Compared to shipborne depth checkpoints, the root mean square (RMS) error of the differences between topography model predicted by DNN method and shipborne depths is approximately 97.5 m, improving by 19.5% and 9.9% compared to the gravity-geologic (GGM) method and the Smith and Sandwell (SAS) method respectively. Compared to traditional data, the integration of SWOT gravity data universally enhances prediction accuracy. Furthermore, the DNN method effectively demonstrates superior capability in balancing the characterization of overall structures with the retention of authentic topography features, which we demonstrated in the Mariana region of the NW Pacific Ocean. However, limited by spatial heterogeneity and physical mechanisms, accurate prediction of such complex, fine-scale topography using gravity data remains a significant challenge.