Deciphering seismic and normal-force fluctuation signatures of debris flows: an experimental assessment of effects of flow composition and dynamics

Data and movies of experiments described in De Haas et al. (2021): Deciphering seismic and normal-force fluctuation signatures of debris flows: an experimental assessment of effects of flow composition and dynamics Abstract summarizing main findings: Debris flows are gravity-driven mass movements that are common natural hazards in mountain regions worldwide. Previous work has shown that measurements of ground vibrations are capable of detecting the timing, speed, and location of landslides and debris flows. A remaining question is whether or not additional flow properties, such as grain-size distribution, flow depth, and impact stress can be inferred reliably from seismic data. Here, we experimentally explore the relation of seismic vibrations and normal-force fluctuations with debris-flow composition and dynamics. We show that seismic vibrations and normal-force fluctuations induced by debris flows are strongly correlated, and that both are affected by debris-flow composition. We find that the effects of the large-particle distribution on seismic vibrations and normal-force fluctuations are substantially more pronounced than the effects of water fraction, clay fraction, and flow volume, especially when normalized by flow depth. We further show that for flows with similar coarse-particle distributions seismic vibrations and normal-force fluctuations can be reasonably-well related to flow depth, even if total flow volume, water fraction, and the size distribution of fines varies. Our experimental results shed light on how changes in large-particle, clay, and water fractions affect the seismic and force-fluctuation signatures of debris flows, and provide important guidelines for their interpretation. The data is provided in 2 subfolders for 56 experiments. Detailed information about the files in these subfolders as well as information on how the data is processed is given in the explanatory files ReadMe.txt and DeHaasetal2021_ExperimentalDetails.csv. Contact person is Tjalling de Haas - researcher - T.deHaas@uu.n