Estimating rates of debris flow entrainment from ground vibrations

Kean, Jason W.; Coe, Jeffrey A.; Coviello, Velio; Smith, Joel B.; McCoy, S. W.; Arattano, M.

doi:10.1002/2015gl064811

Cited by 88 publications

(147 citation statements)

References 30 publications

(55 reference statements)

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“…Furthermore, the indirect correlation between flow height and amplitude peak of the main front and surge VI of the 2014 debris flow is successfully reproduced (Figures c and d). The discrepancies observed for some surges are likely due to the variation of the wetted perimeter (area of seismic noise generation), to the overlook of the friction reduction related to the momentum growth during debris flow entrainment of wet bed sediment (Iverson et al, ), and to the variation of channel bed conditions during each debris flow resulting from erosion/deposition processes (Kean et al, ).…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the distance sensor channel can have a relevant impact on the early detection of the seismic waves generated by a debris flow front. The first arrival picking of the signal can be significantly anticipated installing the sensor at an appropriate distance from the channel and also specific channel conditions (i.e., sediment filling), and the method of installation of GVDs (buried in the ground and fixed on a boulder or on a concrete structure) strongly affects the intensity of the output signal (Abancó et al, ; Coviello et al, ; Kean et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…The signal amplitude, obtained through the transformation of the analog voltage signal of the geophone, is a convenient measure of the intensity of the seismic signal of a debris flow. The amplitude rise starts several tens of seconds before the passage of the debris flow front at the cross section (Figure ), especially if GVDs are installed directly into the ground and not in the body of a check dam or on large boulders, which would cause a significant damping of the signal (Coviello et al, ; Kean et al, ). Therefore, a further advantage of a GVD is its capability to detect the occurrence of a debris flow tens of seconds earlier than other types of devices (i.e., stage sensors, pendulums, and tripwires).…”

Section: Debris Flow Seismic Detectionmentioning

confidence: 99%

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Seismic Characterization of Debris Flows: Insights into Energy Radiation and Implications for Warning

et al. 2019

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Debris flows represent a major hazard in mountainous areas, due to their rapid motion along steep channels and to the transport of large sediment volumes, including large boulders. In this paper, we present data of channelized debris flows characterized by different velocities and sediment concentrations recorded in an instrumented channel reach of the Gadria basin (eastern Italian Alps). From the analysis of the seismic energy produced by the interaction of solid particles with channel boundaries, we show that (i) the peak amplitudes are representative of the kinetic energy of each surge and (ii) most energy transfer occurs during the passage of the surge fronts. Then, we propose a debris flow detection algorithm based on the amplitude information gathered from a linear array of geophones installed along the channel. The short time average over long time average ratio of the seismic signal is used to early detect the debris flow occurrence in a continuous stream of seismic data. The algorithm recognizes moving, long‐lasting sources of ground vibration (i.e., debris flows) and filters out different seismic sources (i.e., anthropic noise, earthquakes, and rockfalls). The alarm is triggered when the short time average/long time average threshold is exceeded on two geophones, progressively with time from upstream to downstream. The algorithm is employed in the early warning system installed for research purposes at Gadria. Complementary data (rainfalls, flow stage measurements, and video recordings) permitted a detailed event characterization and alarm validation. During three monitoring seasons, all debris flows were successfully detected, with the alarm lasting for their entire duration, and no false positives were produced.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Debris Flow Seismic Detectionmentioning

confidence: 99%

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Seismic Characterization of Debris Flows: Insights into Energy Radiation and Implications for Warning

et al. 2019

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“…Alternative concepts for quantifying erosion rates in steep channels include channel gradient (Abanco and Hurlimann, 2014) and discharge (Weichert et al, 2009). Recent research also includes ground-vibration measurements with geophones (Kean et al, 2015), measured pressure fluctuations (e.g. Recent research also includes ground-vibration measurements with geophones (Kean et al, 2015), measured pressure fluctuations (e.g.…”

Section: Introductionmentioning

confidence: 99%

Deciphering controls for debris‐flow erosion derived from a LiDAR‐recorded extreme event and a calibrated numerical model (Roßbichelbach, Germany)

Dietrich

Krautblatter

2019

Earth Surf Processes Landf

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Debris flows are among the most destructive and hazardous mass movements on steep mountains. An understanding of debris‐flow erosion, entrainment and resulting volumes is a key requirement for modelling debris‐flow propagation and impact, as well as analysing the associated risks. As quantitative controls of erosion and entrainment are not well understood, total volume, runout and impact energies of debris flows are often significantly underestimated. Here, we present an analysis of geomorphic change induced by an erosive debris‐flow event in the German Alps in June 2015. More than 50 terrestrial laser scans of a 1.2 km long mountain torrent recorded geomorphic change in comparison to an airborne laser scan performed in 2007. Errors were calculated using a spatial variable threshold based on the point density of airborne laser scanning and terrestrial laser scanning and the slope of the digital elevation models. Highest erosion rates approach 5.0 m3/m2 (mean 0.6 m3/m2). During the event 9550 ± 1550 m3 was eroded whereas only 650 ± 150 m3 was deposited in the channel. Velocity, flow pressure, momentum and shear stress were calculated using a carefully calibrated RAMMS Debris Flow model including material entrainment. Here we present a linear regression model relating debris‐flow erosion rates to momentum and shear stress with an R2 up to 68%. Channel transitions from bedrock to loose debris sections cause excessive erosion up to 1 m3/m2 due to previously unreleased random kinetic energy now available for erosion. © 2019 John Wiley & Sons, Ltd.

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“…Establishing clear quantitative scaling relations between the generated high‐frequency seismic signal and flow parameters is difficult in the field because of numerous yet unparsed complexities. First, flows are heterogeneous, partly due to particle segregation (Iverson, ; Kean et al, ), making it challenging to deduce one flow parameter (flow thickness, speed, or particle diameter) from one seismic measurement. Then, irregularities in the bed topography such as turns and roughness and the presence of an erodible bed can cause a sudden increase or decrease in seismic amplitude along the flow path (Allstadt, ; Bachelet et al, ; Favreau et al, ; Kean et al, ; Moretti et al, ).…”

Section: Introductionmentioning

confidence: 99%

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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Estimating rates of debris flow entrainment from ground vibrations

Cited by 88 publications

References 30 publications

Seismic Characterization of Debris Flows: Insights into Energy Radiation and Implications for Warning

Seismic Characterization of Debris Flows: Insights into Energy Radiation and Implications for Warning

Deciphering controls for debris‐flow erosion derived from a LiDAR‐recorded extreme event and a calibrated numerical model (Roßbichelbach, Germany)

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

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