E3S Web of Conferences 415, 03014 (2023)
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https://doi.org/10.1051/e3sconf/202341503014

Study of debris-flow initiation through the analysis of seismic
signals
Elena Ioriatti1*, Velio Coviello2, Matteo Berti1, and Francesco Comiti3
1 Department of Biological, Geological, and Environmental Sciences, Università di Bologna, Bologna, Italy
2 Research Institute for Geo-Hydrological Protection (IRPI), National Research Council (CNR), Padova, Italy
3 Faculty of Science and Technology, Free University of Bozen-Bolzano, Bolzano, Italy

Abstract. Monitoring data gathered in the headwaters of the Gadria catchment, eastern Italian Alps, havebeen analysed to study debris-flow initiation. The active channel, located at 2200 m a.s.l., was instrumented
with a geophone, a time-lapse video camera and a rain gauge. The peak amplitude and duration of the seismic
signals and their frequency content were analysed and compared with video images. Results showed thatdifferent seismic sources produced signals with different characteristics and that it is possible to discriminate
the most intense runoff by analysing the combination of peak amplitude and duration of the seismic signal.
The further development of this research would be to create an algorithm able to automatically classify theseismic sources and identify intense channel processes that can generate debris flows. In perspective, thecombination of seismic detection in the initiation area with monitoring just above the infrastructures at risk
could represent an effective solution to expand the lead time of an early warning system.

1 Introduction

2 Study area

In mountain regions, debris flows are responsible formajor damage to infrastructure and casualties [1, 2]. The
population exposed to this hazard has increased with theconstruction of settlements and transport infrastructures
on debris-flow fans [3]. Warning systems based on
sensor networks installed along the debris-flow channel
have been implemented in some catchments around theworld [4]. The weakness of these systems is that theygive the alarm only shortly before the debris flow
reaches the infrastructures at risk. Regional warning
systems based on the exceedance of critical rainfall
thresholds, generally defined by Intensity-Duration (ID) curves, provide a longer lead-time [5, 6]. However,these systems are affected by large spatial uncertainties
because it is often not possible to catch rainfall
variability in the source areas with an adequate numberand distribution of rain gauges [7, 8]. The mechanisms
causing the triggering of debris flows are still poorly
understood due to the complexity that characterizes theinitiation zones. In particular, antecedent moisture
conditions, sediment availability, and the evolution invegetation and soil cover may change the rainfall
thresholds previously defined [4, 5, 7]. This study
explores whether geophones, i.e. low-cost seismicsensors, could be used in future for detecting initiation
processes and developing enhanced debris-flow
warning systems.

The Gadria catchment is located in the Vinschgau –Venosta Valley, in the north-eastern Italian Alps and ispart of the Etsch-Adige River basin. The elevation of thecatchment ranges from 1394 to 2945 m a.s.l. and its
drainage area is 6.3 km2 [9]. It is bordered on the Westby the Strimm catchment, which enters the Gadria
stream just before a retention check dam that was built
to protect the downstream settlements from debris flows(Figure 1a). The lithology of the basin shows thepresence of paragneiss and orthogneiss formed bymetamorphism during the Permian and Cretaceousperiods. These metamorphic rocks are highly fractured
and particularly susceptible to the weather agents. The
upper and intermediate sections of the catchments arecharacterized by colluvial processes, which fill thechannels with sediments through rockfalls, debris slides
and dry raveling. The basin is prone to a chronic debrisflow activity with one to two debris flows and several
small-magnitude floods per year [9, 10].

3 Material and methods
An active channel in the headwaters of the Gadria
catchment has been selected for monitoring channel
processes related to debris-flow initiation with a raingauge, a time-lapse video camera and a geophone. The
specific goals of the monitoring are the following:- Investigate whether it is possible to classify different
seismic sources (channel processes, earthquakes, wind,* Corresponding author: elena.ioriatti2@unibo.it© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

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rainfall, the passage of animals) based on thecharacteristics of the seismic waves they produce;- Discriminate between seismic signals produced byrunoff, debris flows, or other processes (earthquakes,wind gusts, rainfall or animals) by means of simple
signal metrics (i.e., signal duration, peak amplitude andmain frequency).

of 0.6 mm/min and then lasts for 2 hours. Groundvibrations are recorded by a “Raspberry Shake RS4D”,version V5. The advantages of this instrument are that it
is low-cost and integrates a 4.5-Hz geophone, a 24-bit
digitizer, and a computer into a single box [11].
3.2 Analyses of the seismic signals
The seismic signal sampled at 100 sps was filtered
below 45 Hz according to the Nyquist–Shannon
sampling theorem and above 5 Hz (Figure 2a). Then, theenvelope of the signal Ae was calculated as the averageof the absolute value of the raw signal over a specifictime window (Figure 2b) using the formula [12]:(1)∑𝑓𝑠 |𝑣𝑖 |𝐴𝑒 = 𝑖=1
𝑓𝑠
where vi is the ground vibration velocity and fs is thesampling frequency. Amplitude and duration of theseismic signals, and the spectral characteristics ofchannel processes were analysed to characterize thedifferent processes observed in the area.

Fig. 2. (a) 5-45 Hz filtered signal; (b) 1-min envelope.

3.3 Classification of seismic sources
Video images were used to identify and classify thesources of the seismic waves recorded by the geophone.
The duration and the maximum amplitude of the 5-45
Hz filtered signal related to the sources were extracted.
Seismic sources were classified into the three followingclasses of channel processes:- Large runoff/ debris flow: strong channel runoff withsediment transport, in some cases associated to surges
typical of debris flow.
- Small runoff: channel runoff with limited or absent
sediment transport.
- Bank collapse and raveling: mobilization of coarse
debris from the channel banks caused by rainfall andraveling of loose sediments down the slope.
Other five sources of seismic noise were also identified:- Animals: animals captured by the camera whilecrossing the channel, grazing or jumping.
- Earthquakes: seismic events identified in the national
catalog of INGV.
- Wind: wind was identified through the movement oftree canopies.
- Rainfall: precipitation recorded by the rain gauge thatexceeds 0.2 mm/5 min.

Fig. 1. (a) Map showing the Gadria and Strimm catchments
and their river network, the alluvial fan, the Etsch-Adige Riverand the settlements of Laas, Allitz and Kortsch. (b)Instrumented channel in the upper monitoring station.

3.1 Monitoring of source areas
One of the most active steep channels located in thesource area of the Gadria catchment is monitored since
2018. The “upper” monitoring station, located at about
2200m a.s.l., was installed to study the triggering
processes and initiation conditions for debris flows [7].
The data examined in the present study have beenacquired in July and August 2019 with three instruments
installed close to the channel: a time-lapse video camera
and a tipping-bucket rain gauge installed on the leftbank, and a geophone installed on the right bank (Figure1b). The video camera points upstream and it is used torecord videos suitable to recognize the different flow
processes. The videos start when the precipitation
recorded by the rain gauge exceeds a very-low threshold2

E3S Web of Conferences 415, 03014 (2023)
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https://doi.org/10.1051/e3sconf/202341503014- Unknown source: signals with low amplitude and long
duration whose possible source was not visible in thevideos and not related to rainfall.

Apparently, it was not possible to distinguish the “largerunoff/ debris flow” events from the other channel
processes with a simplified frequency analysis, bylooking at the main frequency only. However, thespectral analysis confirms that all signals produced by“large runoff/ debris flow” events are characterized by ahigher signal power compared to the other seismic
sources.
The use of the video camera for the identification of theseismic sources has limitations due to the resolution of
the images and the limited framing area. Sometimes, thesignal variability suggested that something washappening, but it was not possible to identify the seismic
source from the videos. An attempt was made to identify
and classify seismic sources only based on the peak
amplitude and signal duration. These sources are called“supposed” in Figure 3. The result represents a firstattempt of classification only based on the seismic
information but independent observations are needed tovalidate this analysis. For what concerns the only
supposed debris-flow signal (Figure 3), fieldobservations confirmed that a debris-flow surgeinterested the neighbourhood channel. The distributionof the recorded seismic events is characterized by alower limit represented by:

4 Results
Peak amplitude and duration of the signals produced by
the different seismic sources are shown in Figure 3. As
can be seen, “large runoff/ debris flow” events show
values clearly different from the others in terms ofamplitude of the signal. In fact, these signals recorded
the highest peak amplitude with values from 0.175 to 1.7mm/s, while their durations (from about 5 to 11 min) aresimilar to those of other seismic sources. The mostintense event, which occurred on 26th July 2019,
produced significant morphological changes in thechannel with an estimated erosion depth of 1 m [7]. The
two “large runoff/ debris flow” events that affected theentire channel section show a difference of an order ofmagnitude in the peak amplitude. As shown by the videoimages, the reason probably lies in the different amountof mobilized material (Figure 4). These results areconsistent with the research performed in the lower partof the Gadria catchment by [3] which showed that theamplitude of the seismic signal is positively correlated
to the kinetic energy of debris flow, i.e. the flowing mass
and its squared velocity. Signal amplitudes of “smallrunoff” do not differ from “bank collapse and raveling”
but the durations are significantly longer allowing toeasily distinguish them (Figure 3).

(2)

𝐴 = 0.0008 ∗ (𝑡)−0.192, where A is the signal amplitude and t the signal
duration. 98% of the points are located above equation
2. Adopting an approach similar to rainfall thresholds,
the class “large runoff/ debris flow” can be bounded bymeans of the following empirical equation:(3)

𝐴 = 0.26 ∗ (𝑡)−0.192, where A is the signal amplitude and t the signal
duration (Figure 3). The calculated thresholds highlightthe channel response in the upper Gadria. However,signals produced by the 26th-July-2019 event thatreached the lower monitoring station (indicated with ablack arrow in Figure 3) show a higher intensity
compared to all the other observed signals. Therefore, it
is expected that the analyses of a longer seismic dataset
will allow to define also a threshold that identify the“large runoff/ debris flow” events with high probabilityof reaching the outlet of the catchment. In perspective,
this approach could be adopted to automatically classify
the seismic sources recorded in the upper Gadria andidentify the most intense channel processes (i.e., large
runoff/ debris flows) that can propagate downstream.

5 Conclusions
This study shows how it is possible to recognize
different phenomena occurring in the headwaters of adebris-flow basin by looking at the seismic signals they
produce. In particular, the combined analysis of peak
amplitude and signal duration allowed to discriminatebetween signals produced by “large runoff/ debris flow”
and by other seismic sources. The spectral analysis
confirms what was observed in the time-domain: allsignals caused by “large runoff/ debris flow” have thehighest power content.

Fig. 3. Scatterplot Peak amplitude Vs Duration of all observed
and supposed seismic signals. The black arrow indicates the“large runoff/ debris flow” event that interested the entire
channel section on 26th July 2019.

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E3S Web of Conferences 415, 03014 (2023)
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https://doi.org/10.1051/e3sconf/202341503014

Further research is needed to expand the dataset andverify whether these observations are also valid for
longer periods and in other sites. A more detailed
frequency analysis and the installation of additional
monitoring instruments in the adjacent channels mayhelp in the interpretation of seismic signals produced byunknown sources. The same approach should be appliedin other catchments to determine the site-specificity ofthe discriminant. The possible application of thisresearch would be to design an algorithm for theautomatic classification of the seismic sources and forthe identification of channel processes that can generatedebris flows. The identification of intense runoff in theheadwaters of the catchment could provide a first alert
to local authorities, while a downstream station canconfirm the arrival of the debris flow and allow them toclose, for instance, transport routes located on debrisflow fans.

4.

M. Hürlimann , V. Coviello , C. Bel , X. Guo , M.
Berti , C. Graf , J. Hübl , S. Miyata , J. B. Smith ,H-Y Yin, Earth-Sci. Rev. 199, 102981 (2019)
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7. V. Coviello, M. Berti, L. Marchi, F. Comiti, G.
Marchetti, R. Carrillo, S. Miyata, P. Macconi,
Multi-parametric observations of debris-flow
initiation at the headwaters of the Gadria
catchment (eastern Italian Alps), in EGU General
Assembly 2020, 4–8 May 2020, online (2020)
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Gregoretti, A. Simoni, T.L. Mote, A.M. Saylor,Nat. Hazards Earth Syst. Sci. 16, 509 (2016)
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Fig. 4. Time-lapses of debris flows that affected the entire
channel section on (a) 26th July 2019 and (b) 6th August 2019.
We thank Pierpaolo Macconi (Civil Protection Agency,
Autonomous Province of Bozen/Bolzano), Ricardo Carrillo,Giulia Marchetti, Lorenzo Marchi, Shusuke Miyata, andAndreas Schimmel for their support in the research activities
in the upper Gadria.

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