Summary Knowledge of the thermal state of steep alpine rock faces is crucial to assess potential geohazards associated with the degradation of permafrost. Temperature measurements at the rock surface or in boreholes are however expensive, invasive, and provide spatially-limited information. Electrical conductivity and induced polarization tomography can detect permafrost. We test here a recently developed petrophysical model based on the use of an exponential freezing curve applied to both electrical conductivity and normalized chargeability to infer the distribution of temperature below the freezing temperature. We then apply this approach to obtain the temperature distribution from electrical conductivity and normalized chargeability field data obtained across a profile extending from the SE to NW faces of the lower Cosmiques ridge (Mont Blanc massif, Western European Alps, 3613 m a.s.l., France). The geophysical datasets were acquired both in 2016 and 2019. The results indicate that the only NW face of the rock ridge is frozen. To evaluate our results, we model the bedrock temperature across this rock ridge using CryoGRID2, a 1D MATLAB diffusive transient thermal model and surface temperature time series. The modelled temperature profile confirms the presence of permafrost in a way that is consistent with that obtained from the geophysical data. Our study offers a promising low-cost approach to monitor temperature distribution in Alpine rock walls and ridges in response to climate change.

Periglacial rock walls are affected by an increase in rockfall activity attributed to permafrost degradation. While recent laboratory tests have asserted the role of permafrost in bedrock stability, linking experimental findings to field applications is hindered by the difficulty in assessing bedrock temperature at observed rockfall locations and time. In this study, we simulated bedrock temperature for 209 rockfalls inventoried in the Mont Blanc massif between 2007 and 2015 and 209,000 random events artificially created at observed rockfall locations. Real and random events are then compared in a statistical analysis to determine their significance. Permafrost conditions (or very close to 0°C) were consistently found for all events with failure depth > 6 m, and for some events affecting depths from 4 to 6 m. Shallower events were probably not related to permafrost processes. Surface temperatures were significantly high up to at least 2 months prior to failure, with the highest peaks in significance 1.5–2 months and 1–5 days before rockfalls. Similarly, temperatures at scar depths were significantly high, but steadily decreasing, 1 day to 3 weeks before failure. The study confirms that warm permafrost areas (> −2°C) are particularly prone to rockfalls, and that failures are a direct response to extraordinary high bedrock temperature in both frozen and unfrozen conditions. The results are promising for the development of a rockfall susceptibility index, but uncertainty analysis encourages the use of a greater rockfall sample and a different sample of random events.

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Ultrasound imaging is a very versatile and fast medical imaging modality, however it can suffer from serious image quality degradation. The origin of such loss of image quality is often difficult to identify in detail, therefore it makes it difficult to design probes and tools that are less impacted. The objective of this manuscript is to present an endto-end simulation pipeline that makes it possible to generate synthetic ultrasound images while controlling every step of the pipeline, from the simulated cardiac function, to the torso anatomy, probe parameters, and reconstruction process. Such a pipeline enables to vary every parameter in order to quantitatively evaluate its impact on the final image quality. We present here first results on classical ultrasound phantoms and a digital heart. The utility of this pipeline is exemplified with the impact of ribs on the resulting cardiac ultrasound image.