The Seismic Signature of Debris Flows: Flow Mechanics and Early Warning at Montecito, California

Lai, Voon Hui; Tsai, Victor C.; Lamb, Michael P.; Ulizio, T. P.; Beer, Alexander R.

doi:10.1029/2018gl077683

Cited by 85 publications

(201 citation statements)

References 37 publications

(56 reference statements)

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“…The maximum value of kinetic energy per unit area (58.4 kJ/m 2 ) is correctly associated with the main front of the 2013 debris flow (Figure b), which is a bouldery front characterized by the highest value of flow velocity observed at Gadria so far (Table ). This is consistent with the model proposed by Lai et al (), where the power spectral density of the signal linearly scales with the third power of particle size and flow velocity. 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).…”

Section: Resultssupporting

confidence: 92%

“…Length and mass distribution within a debris flow wave do not change over time scales; consequently, different amplitudes are due to either different front‐receiver distances or different kinetic energy values. The dominance of collisional forces in high‐velocity bouldery fronts can explain this effect, also observed at Montecito, where Lai et al () documented that the power spectral density of the signal scales with the third power of particle size and flow velocity. Focusing on the effect of source‐receiver distance on the signal amplitude, if we consider a single debris flow front with constant emission of seismic energy E s , we can solve equation for u ( t ) as it moves toward a sensor assuming a small time span over which the integrand is constant.…”

Section: Discussionmentioning

confidence: 72%

“…The source‐receiver distance also has a strong control on the spectral characteristics of the seismic signals. The spectral information of the signal produced by a moving source such as a debris flow could thus be used to characterize the main source characteristics (Lai et al, ). At Gadria we do not have the raw seismic signal because the recording unit was designed to continuously store only a few parameters (amplitude of the envelope, max and min amplitude, main frequency, and band width).…”

Section: Discussionmentioning

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: Resultssupporting

confidence: 92%

Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

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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“…; Lai et al. ). Both models modify a previous model for the high‐frequency seismic signals caused by bedload transport (Tsai et al.…”

Section: Introductionmentioning

confidence: 99%

A physical model of the high‐frequency seismic signal generated by debris flows

Farin

Tsai

Lamb

et al. 2019

Earth Surf Processes Landf

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193

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We propose a physical model for the high‐frequency (>1 Hz) spectral distribution of seismic power generated by debris flows. The modeled debris flow is assumed to have four regions where the impact rate and impulses are controlled by different mechanisms: the flow body, a coarser‐grained snout, a snout lip where particles fall from the snout on the bed, and a dilute front composed of saltating particles. We calculate the seismic power produced by this impact model in two end‐member scenarios, a thin‐flow and thick‐flow limit, which assume that the ratio of grain sizes to flow thicknesses are either near unity or much less than unity. The thin‐flow limit is more appropriate for boulder‐rich flows that are most likely to generate large seismic signals. As a flow passes a seismic station, the rise phase of the seismic amplitude is generated primarily by the snout while the decay phase is generated first by the snout and then the main flow body. The lip and saltating front generate a negligible seismic signal. When ground properties are known, seismic power depends most strongly on both particle diameter and average flow speed cubed, and also depends on length and width of the flow. The effective particle diameter for producing seismic power is substantially higher than the median grain size and close to the 73rd percentile for a realistic grain size distribution. We discuss how the model can be used to estimate effective particle diameter and average flow speed from an integrated measure of seismic power. © 2019 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd. © 2019 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.

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“…Sites 1, 2, 4, 6, and 7 were burned at high severity (539 < dNBR < 897), and Sites 3, 5, and 8 were classified as unburned (46 < dNBR < 86). The goal was to collect all samples before any substantial rainfall; however, they were all collected after a historical rainstorm (20 mm in 10 min, Lai, Tsai, Lamb, Ulizio, & Beer, ; 14 mm in 5 min, National Weather Service, ) that caused major debris flow damage in Montecito, California, on January 9, 2018. The storm, itself, had a high degree of spatial variability.…”

Section: Resultsmentioning

confidence: 99%

Sources of inherent infiltration variability in postwildfire soils

Moody

Martin²,

Ebel

2019

Hydrological Processes

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An automated disc infiltrometer was developed to improve the measurements of soil hydraulic properties (saturated hydraulic conductivity and sorptivity) of soils affected by wildfire. Guidelines are given for interpreting curves showing cumulative infiltration as a function of time measured by the autodisc. The autodisc was used to measure the variability of these soil hydraulic properties in three different sample sets: (a) a reference soil consisting of a nonrepellent, uniform, fine sand; (b) soils with the same soil textural classification derived from the same bedrock geology but having different initial burn severities; and (c) soils from different bedrock geology but having the same burn severity. The autodisc infiltrometer had greater sampling rates and volume resolution when compared with the visual minidisc infiltrometer from previous studies. There was no statistical difference in the mean values measured using the autodisc and visual minidisc, but the variability of the autodisc measurements was significantly less than the visual minidisc for a given set of samples. The greatest variability of soil hydraulic properties in reference samples with uniform particle size was attributed to different pore geometries (coefficient of variation [COV] = 0.28–0.34). Unburned field samples (same soil type) with heterogeneous particle sizes had greater variability (COV = 0.57–0.78) than the reference samples. However, this basic variability decreased or remained constant in these field samples as burn severity increased. Additional sources of variability (COV = 0.53–1.99) were attributed to multiple layers resulting from ash or sediment deposition. Results indicate that resolving differences in soil hydraulic properties from different sites requires more than the common 10 random samples because of the multiple sources of variability.

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The Seismic Signature of Debris Flows: Flow Mechanics and Early Warning at Montecito, California

Cited by 85 publications

References 37 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

A physical model of the high‐frequency seismic signal generated by debris flows

Sources of inherent infiltration variability in postwildfire soils

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