Laboratory landquakes: Insights from experiments into the high-frequency seismic signal generated by geophysical granular flows

“…Experimental data are available at the Pangaea repository (Arran et al., 2020), while computations were performed and plots produced with the summary data and code at Zenodo (Arran et al., 2021), using NumPy (Harris et al., 2020), Matplotlib (Hunter, 2007), and pandas (McKinney, 2010).…”

Section: Data Availability Statementmentioning

confidence: 99%

Laboratory Landquakes: Insights From Experiments Into the High‐Frequency Seismic Signal Generated by Geophysical Granular Flows

et al. 2021

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“…However, although various models based on the Tsai et al. (2012) paradigm have been tested against laboratory experimental data (Arran et al., 2021; de Haas et al., 2021), these models have not yet been thoroughly verified against independent field data (Allstadt et al., 2020; Zhang et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Analyzing Bulk Flow Characteristics of Debris Flows Using Their High Frequency Seismic Signature

et al. 2021

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Quantifying debris‐flow characteristics remains a key challenge in natural hazard research. Recent studies suggest that seismic signals can reveal debris‐flow properties. However, the accuracy and applicability of theoretical models linking bulk flow properties to the seismic signature of a debris flow remain elusive. By extending a previously proposed model of random single‐particle impact forces to multi‐particle force chains acting on the channel bed, we describe the generation of high frequency seismic signals, and thereby estimate the bulk flow dynamics of six debris‐flow events at Illgraben, Switzerland. The theoretically predicted basal force fluctuations agree with in‐situ measurements at a basal force plate and seismic signals recorded adjacent to the channel. According to our model, both random single‐particle impacts and random impacts caused by multi‐particle force chains control basal force fluctuations. The relative contributions of single particles and of multi‐particle force chains may vary significantly for different events and different positions within events. The relative contribution of multi‐particle force chains is expected to be larger in the flow front, where particle concentrations are higher. The ratio between single‐particle and multi‐particle contributions appears to control the non‐linear relation between flow depth and the magnitude of the high frequency seismic signals. Our results suggest that fluctuating basal forces are strongly controlled by particle size and flow depth, and open new perspectives for the understanding of bulk flow characteristics, such as flow depth and weight, and of the high frequency seismic signals generated by debris flows.

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“…There are essentially two properties of the high-frequency seismic signals generated by mass movements that have been studied in correlation with the physical parameters of the source dynamics, the maximum amplitude of the seismic signal corrected for propagation effects A 0 , and the energy of the seismic signal at the source E s (e.g., Norris, 1994;Deparis et al, 2008;Dammeier et al, 2011;Schneider et al, 2011;Hibert et al, 2011;Bottelin et al, 2014;Levy et al, 2015;Farin et al, 2015Farin et al, , 2016Hibert et al, 2017b, a;Saló et al, 2018;Le Roy et al, 2019;Farin et al, 2019;Arran et al, 2020). These two quantities are usually compared to the source velocity, momentum, and its kinetic and potential energies.…”

Section: Seismic Sources Parameters Computationmentioning

confidence: 99%

“…Recent studies proposed scaling laws between high-frequency seismic signal features and source properties of rockfalls and landslides. These studies are mostly based on laboratory experiments (e.g., Farin et al, 2015Farin et al, , 2016Farin et al, , 2019Arran et al, 2020), real-scale experiments (e.g., Bottelin et al, 2014;Hibert et al, 2017b;Saló et al, 2018), and documented natural events (e.g., Norris, 1994;Deparis et al, 2008;Dammeier et al, 2011;Hibert et al, 2011;Levy et al, 2015;Hibert et al, 2017a;Le Roy et al, 2019). Among the quantities studied, several correlations between the mass and the velocity of the rockfall, and the magnitude, the maximum amplitude at the source and the seismic energy of the seismic signal have been observed and sometimes quantified.…”

Section: Introductionmentioning

confidence: 99%

Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate

Noël¹,

Toe

Talib

et al. 2022

Preprint

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Abstract. Understanding the dynamics of slope instabilities is critical to mitigate the associated hazards but their direct observation is often difficult due to their remote locations and their spontaneous nature. Seismology allows to get unique information on these events, including on their dynamics. However, the link between the properties of these events (mass and kinematics) and the seismic signals generated are still poorly understood. we conducted a controlled rockfall experiment in the Riou-Bourdoux torrent (South French Alps) to try to better decipher those links. We deployed a dense seismic network and inferred the dynamics of the block from the reconstruction of the 3D trajectory from terrestrial and airborne high-resolution stereo-photogrammetry. We propose a new approach based on machine learning to predict the mass and the velocity of each block. Our results show that we can predict those quantities with average errors of approximately 10 % for the velocity and 25 % for the mass. These accuracies are as good as or better than those obtained by other approaches, but our approach has the advantage of not requiring to localize the source and an a priori knowledge of the environment, nor of making a strong assumption on the seismic wave attenuation model. Finally, the machine learning approach allows us to explore more widely the correlations between the features of the seismic signal generated by the rockfalls and their physical properties, and might eventually lead to better constrain the physical models in the future.

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Laboratory landquakes: Insights from experiments into the high-frequency seismic signal generated by geophysical granular flows

Cited by 4 publications

References 81 publications