Numerical simulations of granular dam break: Comparison between discrete element, Navier-Stokes, and thin-layer models

Granular flows occur in various contexts, including laboratory experiments, industrial processes, and natural geophysical flows. To investigate their dynamics, different kinds of physically based models have been developed. These models can be characterized by the length scale at which dynamic processes are described. Discrete models use a microscopic scale to individually model each grain, Navier-Stokes models use a mesoscopic scale to consider elementary volumes of grains, and thin-layer models use a macroscopic scale to model the dynamics of elementary columns of fluids. In each case, the derivation of the associated equations is well-known. However, few studies focus on the extent to which these modeling solutions yield mutually coherent results. In this article, we compare the simulations of a granular dam break on a horizontal or inclined planes for the discrete model convex optimization contact dynamics (COCD), the Navier-Stokes model Basilisk, and the thin-layer depth-averaged model SHALTOP. We show that, although all three models allow reproducing the temporal evolution of the free surface in the horizontal case (except for SHALTOP at the initiation), the modeled flow dynamics are significantly different, and, in particular, during the stopping phase. The stresses measured at the flow's bottom, reflecting the flow dynamics, are in relatively good agreement, but significant variations are obtained with the COCD model due to complex and fast-varying granular lattices. Similar conclusions are drawn using the same rheological parameters to model a granular dam break on an inclined plane. This comparison exercise is essential for assessing the limits and uncertainties of granular flow modeling.

A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya

On 7 February 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. More than 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27 × 10⁶ cubic meters of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders greater than 20 meters in diameter and scoured the valley walls up to 220 meters above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

Reclassification of Microseismic Events through Hypocenter Location: Case Study on an Unstable Rock Face in Northern Italy

Passive seismic methods are increasingly used in monitoring unstable rock slopes that are likely to cause rockfalls. Event classification is a basic step in microseismic monitoring. However, the classification of events generated by the propagation of fractures and rockfalls is still uncertain due to their similar features in the time and frequency domains. Hypocenter localization might be a powerful tool to distinguish events generated by fracture propagation from those caused by rockfalls. In this study, a classification procedure based on hypocenter location was validated using a selected subset of high-quality data recorded by a five-geophone network installed on a steep rock slope in Northern Italy. Considering the complexity and heterogeneity of the rock mass, a 3D velocity model that was derived from a tomographic experiment was used. We performed the localization using the equal differential time method. The location results fairly fit our expectations on suspected rockfall events because most signals were located near the rock face. However, only 4 out of 20 suspected fracture events were unquestionably confirmed as fractures being located inside the rock mass and far enough from the rock face. Further improvements in location accuracy are still necessary to distinguish suspected fracture events located close to the rock face from rockfalls. This study demonstrates that hypocenter location is a promising method to improve the final classification of microseismic events.

Observations on the May 2019 Joffre Peak landslides, British Columbia

Two catastrophic landslides occurred in quick succession on 13 and 16 May 2019, from the north face of Joffre Peak, Cerise Creek, southern Coast Mountains, British Columbia. With headscarps at 2560 m and 2690 m elevation, both began as rock avalanches, rapidly transforming into debris flows along middle Cerise Creek, and finally into debris floods affecting the fan. Beyond the fan margin, a flood surge on Cayoosh Creek reached bankfull and attenuated rapidly downstream; only fine sediment reached Duffey Lake. The toe of the main debris flow deposit reached 4 km from the headscarp, with a travel angle of 0.28, while the debris flood phase reached the fan margin 5.9 km downstream, with a travel angle of 0.22. Photogrammetry indicates the source volume of each event is 2-3 Mm³, with combined volume of 5 Mm³. Lidar differencing, used to assess deposit volume, yielded a similar total result, although error in the depth estimate introduced large volume error masking the expected increase due to dilation and entrainment. The average velocity of the rock avalanche-debris flow phases, from seismic analysis, was ~ 25-30 m/s, and the velocity of the 16 May debris flood on the upper fan, from super-elevation and boulder sizes, was 5-10 m/s. The volume of debris deposited on the fan was ~ 10⁴ m³, 2 orders of magnitude less than the avalanche/debris flow phases. Progressive glacier retreat and permafrost degradation were likely the conditioning factors; precursor rockfall activity was noted at least ~6 months previous; thus, the mountain was primed to fail. The 13 May landslide was apparently triggered by rapid snowmelt, with debuttressing triggering the 16 May event.

Weka Trainable Segmentation Plugin in ImageJ: A Semi-Automatic Tool Applied to Crystal Size Distributions of Microlites in Volcanic Rocks

Crystals within volcanic rocks record geochemical and textural signatures during magmatic evolution before eruption. Clues to this magmatic history can be examined using crystal size distribution (CSD) studies. The analysis of CSDs is a standard petrological tool, but laborious due to manual hand-drawing of crystal margins. The trainable Weka segmentation (TWS) plugin in ImageJ is a promising alternative. It uses machine learning and image segmentation to classify an image. We recorded back-scattered electron (BSE) images of three volcanic samples with different crystallinity (35, 50 and ≥85 vol. %), using scanning electron microscopes (SEM) of variable image resolutions, which we then tested using TWS. Crystal measurements obtained from the automatically segmented images are compared with those of the manual segmentation. Samples up to 50 vol. % crystallinity are successfully segmented using TWS. Segmentation at significantly higher crystallinities fails, as crystal boundaries cannot be distinguished. Accuracy performance tests for the TWS classifiers yield high F-scores (>0.930), hence, TWS is a successful and fast computing tool for outlining crystals from BSE images of glassy rocks. Finally, reliable CSD's can be derived using a low-cost desktop SEM, paving the way for a wide range of research to take advantage of this new petrological method.

Note: Localization based on estimated source energy homogeneity

Acoustic signal localization is a complex problem with a wide range of industrial and academic applications. Herein, we propose a localization method based on energy attenuation and inverted source amplitude comparison (termed estimated source energy homogeneity, or ESEH). This inversion is tested on both synthetic (numerical) data using a Lamb wave propagation model and experimental 2D plate data (recorded with 4 accelerometers sensitive up to 26 kHz). We compare the performance of this technique with classic source localization algorithms: arrival time localization, time reversal localization, and localization based on energy amplitude. Our technique is highly versatile and out-performs the conventional techniques in terms of error minimization and cost (both computational and financial).

Seismology-based early identification of dam-formation landquake events

Flooding resulting from the bursting of dams formed by landquake events such as rock avalanches, landslides and debris flows can lead to serious bank erosion and inundation of populated areas near rivers. Seismic waves can be generated by landquake events which can be described as time-dependent forces (unloading/reloading cycles) acting on the Earth. In this study, we conduct inversions of long-period (LP, period ≥20 s) waveforms for the landquake force histories (LFHs) of ten events, which provide quantitative characterization of the initiation, propagation and termination stages of the slope failures. When the results obtained from LP waveforms are analyzed together with high-frequency (HF, 1–3 Hz) seismic signals, we find a relatively strong late-arriving seismic phase (dubbed Dam-forming phase or D-phase) recorded clearly in the HF waveforms at the closest stations, which potentially marks the time when the collapsed masses sliding into river and perhaps even impacting the topographic barrier on the opposite bank. Consequently, our approach to analyzing the LP and HF waveforms developed in this study has a high potential for identifying five dam-forming landquake events (DFLEs) in near real-time using broadband seismic records, which can provide timely warnings of the impending floods to downstream residents.