Estimation of dynamic friction and movement history of large landslides

Yamada, Masumi; Mangeney, Anne; Matsushi, Yuki; Matsuzawa, Takanori

doi:10.1007/s10346-018-1002-4

Cited by 42 publications

(71 citation statements)

References 40 publications

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“…As a result, comparing the full force history inverted from seismic data to the catalog of forces calculated with our model may provide a way to determine the iceberg characteristics (ice mass loss) from the seismic signal as done for landslides. Indeed, for landslides, combining seismic inversion and numerical modeling makes it possible to determine the characteristics of the released mass and the friction coefficient and to quantify physical processes acting during the flow (e.g., erosion; Moretti et al, ; Moretti et al, ; Yamada, Mangeney, Matsushi & Moretti, ; Yamada, Mangeney, Matsushi & Matsuzawai, ). To reduce the number of possible ( ε , H ) combinations, one could possibly often assume that the iceberg heights are close to the glacier thickness in the margin of the calving front.…”

Section: Discussioncontrasting

confidence: 99%

“…The seismic source characteristics (amplitude, duration and evolution with time) are related to the dynamic processes that are involved. They should depend on rheological and dimensional parameters as has been shown for landslide events (Ekström & Stark, 2013;Favreau et al, 2010;Moretti et al, 2012;Moretti et al, 2015;Yamada, Mangeney, Matsushi & Moretti, 2018;Yamada, Mangeney, Matsushi & Matsuzawai, 2018;Zhao et al, 2014). Detailed comparison of the force history inverted from seismic data with the force calculated by landslide models provides a unique way to determine the characteristics and dynamics of natural landslides.…”

Section: Introductionmentioning

confidence: 99%

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Numerical Modeling of Iceberg Capsizing Responsible for Glacial Earthquakes

et al. 2018

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The capsizing of icebergs calved from marine‐terminating glaciers generate horizontal forces on the glacier front, producing long‐period seismic signals referred to as glacial earthquakes. These forces can be estimated by broadband seismic inversion, but their interpretation in terms of magnitude and waveform variability is not straightforward. We present a numerical model for fluid drag that can be used to study buoyancy‐driven iceberg capsize dynamics and the generated contact forces on a calving face using the finite‐element approach. We investigate the sensitivity of the force to drag effects, iceberg geometry, calving style, and initial buoyancy. We show that there is no simple relationship between force amplitude and iceberg volume, and similar force magnitudes can be reached for different iceberg sizes. The force history and spectral content varies with the iceberg attributes. The iceberg aspect ratio primarily controls the capsize dynamics, the force shape, and force frequency, whereas the iceberg height has a stronger impact on the force magnitude. Iceberg hydrostatic imbalance generates contact forces with specific frequency peaks that explain the variability in glacial earthquake dominant frequency. For similar icebergs, top‐out and bottom‐out events have significantly different capsize dynamics leading to larger top‐out forces especially for thin icebergs. For realistic iceberg dimensions, we find contact‐force magnitudes that range between 5.6 × 1011 and 2 × 1014 kg·m, consistent with seismic observations. This study provides a useful framework for interpreting glacial earthquake sources and estimating the ice mass loss from coupled analysis of seismic signals and modeling results.

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Section: Discussioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Numerical Modeling of Iceberg Capsizing Responsible for Glacial Earthquakes

et al. 2018

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“…For different large landslides that occurred in Japan and the ones reported by Ekström and Stark (), Yamada et al () found a scaling law

\max false(V_{X}^{C O M} false) \approx 2 {false(normalΔ H^{C O M} false)}^{0.5}

, relating

\max false(V_{X}^{C O M} false)

, the maximum speed of the center of mass in the X ‐direction and Δ H COM , the height difference of the center of mass before and after the collapse. We observe a similar scaling law in our experiments for d =2 mm and different masses M and aspect ratios a , with a power about 0.53 when we fit all of our data for all slope angles θ (Figure c).…”

Section: Discussionmentioning

confidence: 90%

“…However, for a given slope angle , the maximum bulk speed seems to increase as max(V COM X ) ≈ 2(ΔH COM ) 1.2 . Therefore, the relation reported by Yamada et al (2018) may be due to the fact that the different landslides occurred at different slope angles . This illustrates an advantage of the laboratory experiments of granular flows such as the ones conducted in the present study: we can separate the different controlling parameters and better understand the link between different flow characteristics.…”

Section: Scaling Laws Between Flow Dynamic Parametersmentioning

confidence: 96%

Relations Between the Characteristics of Granular Column Collapses and Resultant High‐Frequency Seismic Signals

et al. 2019

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Deducing relations between the dynamic characteristics of landslides and rockfalls and the resultant high‐frequency (>1 Hz) seismic signal is challenging. To investigate relations that can be tested in the field, we conducted laboratory experiments of 3‐D granular column collapse on a rough inclined thin plate, for a large set of column masses, aspect ratios, particle diameters, and slope angles. The dynamics of the granular flows were recorded using a high‐speed camera, and the generated seismic signal was measured using piezoelectric accelerometers. Empirical scaling laws are established between the characteristics of the granular flows and deposits and that of the generated seismic signals. The radiated seismic energy scales with particle diameter as d3, column mass as M and aspect ratio as a1.1. The increase of the radiated seismic energy as slope angle increases correlates with a similar increase in particle agitation. Based on our experimental results, we revisit scaling laws reported in the field and discuss their possible physical origin. The discrepancy between field and experimental observations can be explained by the complex influence of the substrate on seismic signal and the difference of flow initiation in both cases. However, our empirical scaling laws allow us to determine which flow parameters could be inferred from a given seismic characteristic in the field. In particular, by assuming the flow average speed is known, we show that we can retrieve parameters d, M, and a within a factor of two from the seismic signal.

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“…Simulations of elliptic landslides by Martel (2004) show that the most compressive and the most tensile stresses are parallel to the major axis of the landslide, coinciding with the average landslide aspect. Yamada et al (2013) and Yamada et al (2018) show for several japanese landslides that peak forces were aligned parallel to the long side of the landslides; Allstadt (2013) shows from waveform inversion for the Mt. Meager landslide that force and acceleration were parallel to the longer side of the landslide source area.…”

Section: Topographic Analysismentioning

confidence: 99%

Effects of finite source rupture on landslide triggering: The 2016 <i>M<sub>W</sub></i> 7.1 Kumamoto earthquake

Specht¹,

Öztürk²,

Veh³

et al. 2018

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Abstract. The propagation of a seismic rupture on a fault introduces spatial variations in the seismic wavefield surrounding the fault during an earthquake. This directivity effect results in larger shaking amplitudes in the rupture propagation direction. Its seismic radiation pattern also causes amplitude variations between the strike-normal and strike-parallel components of horizontal ground motion. We investigated the landslide response to these effects during the 2016 Kumamoto earthquake (MW 7.1) in central Kyūshū (Japan). Although the distribution of some 1,500 earthquake-triggered landslides as function of rupture distance is consistent with the observed Arias intensity, the landslides are more concentrated to the northeast of the southwest-northeast striking rupture. We examined several landslide susceptibility factors: hillslope inclination, median amplification factor (MAF) of ground shaking, lithology, land cover, and topographic wetness. None of these factors can sufficiently explain the landslide distribution or orientation (aspect), although the landslide headscarps coincide with elevated hillslope inclination and MAF. We propose a new physics-based ground motion model that accounts for the seismic rupture effects, and demonstrate that the low-frequency seismic radiation pattern consistent with the overall landslide distribution. The spatial landslide distribution is primarily influenced by the rupture directivity effect, whereas landslide aspect is influenced by amplitude variations between the fault-normal and fault-parallel motion at frequencies

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Estimation of dynamic friction and movement history of large landslides

Cited by 42 publications

References 40 publications

Numerical Modeling of Iceberg Capsizing Responsible for Glacial Earthquakes

Numerical Modeling of Iceberg Capsizing Responsible for Glacial Earthquakes

Relations Between the Characteristics of Granular Column Collapses and Resultant High‐Frequency Seismic Signals

Effects of finite source rupture on landslide triggering: The 2016 <i>M<sub>W</sub></i> 7.1 Kumamoto earthquake

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Estimation of dynamic friction and movement history of large landslides

Cited by 42 publications

References 40 publications

Numerical Modeling of Iceberg Capsizing Responsible for Glacial Earthquakes

Numerical Modeling of Iceberg Capsizing Responsible for Glacial Earthquakes

Relations Between the Characteristics of Granular Column Collapses and Resultant High‐Frequency Seismic Signals

Effects of finite source rupture on landslide triggering: The 2016 &lt;i&gt;M&lt;sub&gt;W&lt;/sub&gt;&lt;/i&gt; 7.1 Kumamoto earthquake

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Effects of finite source rupture on landslide triggering: The 2016 <i>M<sub>W</sub></i> 7.1 Kumamoto earthquake