ESurfEarth Surface DynamicsESurfEarth Surf. Dynam.2196-632XCopernicus PublicationsGöttingen, Germany10.5194/esurf-6-1059-2018Towards a standard typology of endogenous landslide seismic sourcesTypology of landslide seismic sourcesProvostFlorianef.provost@unistra.frMaletJean-Philippehttps://orcid.org/0000-0003-0426-4911HibertClémenthttps://orcid.org/0000-0003-3457-6617HelmstetterAgnèsRadiguetMathildehttps://orcid.org/0000-0002-3877-9393AmitranoDavidLangetNadègeLaroseErichttps://orcid.org/0000-0001-8353-5470AbancóClàudiaHürlimannMarcelhttps://orcid.org/0000-0003-0119-1438LebourgThomasLevyClarahttps://orcid.org/0000-0003-3922-0396Le RoyGaëlleUlrichPatriceVidalMaurinVialBenjaminInstitut de Physique du Globe de Strasbourg, CNRS UMR 7516,
EOST/Université de Strasbourg, 5 rue Descartes, 67084
Strasbourg CEDEX, FranceUniv. Grenoble Alpes, Univ. Savoie Mont
Blanc, CNRS, IRD, IFSTTAR, ISTerre, 38000 Grenoble, FranceNorsar,
Gunnar Randers Vei 15, 2007 Kjeller, NorwayGeological Hazards
Prevention Unit, Institut Cartografic i Geologic de Catalunya, Parc de
Montjuïc, 08038 Barcelona, SpainDepartament of
Civil and Environmental Engineering, UPC-BarcelonaTECH, C. Jordi Girona 1–3,
08034 Barcelona, SpainGéosciences Azur, CNRS UMR
7329, OCA/Université de Nice, 250 rue Albert Einstein,
06905 Sophia-Antipolis CEDEX, FranceBRGM, Avenue
C. Guillemin, 45100 Orléans, FranceGéolithe, Crolles,
FranceFloriane Provost (f.provost@unistra.fr)16November201864105910888March201821March201830September201812October2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://esurf.copernicus.org/articles/6/1059/2018/esurf-6-1059-2018.htmlThe full text article is available as a PDF file from https://esurf.copernicus.org/articles/6/1059/2018/esurf-6-1059-2018.pdf
The objective of this work is to propose a standard classification of seismic
signals generated by gravitational processes and detected at close distances
(<1 km). We review the studies where seismic instruments have been
installed on unstable slopes and discuss the choice of the seismic
instruments and the network geometries. Seismic observations acquired at 13
unstable slopes are analyzed in order to construct the proposed typology. The
selected slopes are affected by various landslide types (slide, fall, topple
and flow) triggered in various material (from unconsolidated soils to
consolidated rocks). We investigate high-frequency bands (>1 Hz) where
most of the seismic energy is recorded at the 1 km sensor to source
distances. Several signal properties (duration, spectral content and
spectrogram shape) are used to describe the sources. We observe that similar
gravitational processes generate similar signals at different slopes. Three
main classes can be differentiated mainly from the length of the signals, the
number of peaks and the duration of the autocorrelation. The classes are the
“slopequake” class, which corresponds to sources potentially occurring
within the landslide body; the “rockfall” class, which corresponds to
signals generated by rock block impacts; and the “granular flow” class,
which corresponds to signals generated by wet or dry debris/rock flows.
Subclasses are further proposed to differentiate specific signal properties
(frequency content, resonance, precursory signal). The signal properties of
each class and subclass are described and several signals of the same class
recorded at different slopes are presented. Their potential origins are
discussed. The typology aims to serve as a standard for further comparisons
of the endogenous microseismicity recorded on landslides.
Introduction
Seismology can be used to record (remotely and in a noninvasive way) ground
deformation processes and to measure stress–strain conditions through the
hydromechanical interactions occurring in the media. Seismology is widely
used to understand the physical processes taking place on tectonic faults or
volcanoes, to investigate fluid reservoir circulation, and more recently to
analyze the dynamics of Earth surface processes such as glaciers
, snow avalanches
and
landslides . In
this manuscript, the term landslide describes a wide variety of processes
resulting from the downslope movement of slope-forming materials by falling,
toppling, sliding or flowing mechanisms . Thus, landslides
cover a large range of deformation processes, that can be differentiated in
terms of sizes and volumes (smaller than 1 m3 up to more than
107 m3), in terms of displacement rates (mm yr-1 to
m s-1), and in terms of mobilized material (hard or soft
rocks, debris, poorly consolidated soils, and artificial fills).
With the increasing number of seismic sensors deployed worldwide and the
development of automatic seismological processing chains, the construction of
landslide catalogs using seismology is now possible, especially at the
regional scale (e.g., Switzerland; or
France; ). However, the forecast of a particular
landslide rupture or acceleration is still challenging at the slope scale,
which is the focus of this work. In the 1960s, observed an
increase in acoustic emissions (AEs) generated by slopes tilted towards
failure at both laboratory and field scales. AEs are high-frequency
(10–1000 kHz) body waves generated by the release of strain energy through
grain rearrangement . Further studies confirmed these
results for several slopes ,
where correlations between AE, surface displacement and heavy rainfall were
documented. AEs record deep deformation processes before signs of
displacement are identifiable at the surface. However, AEs are rapidly
attenuated with the distance to the sources. The location of the sensors and
the type of waveguide are also critical to capture the slope behavior. Recent
developments of fiber optic distributed acoustic systems (FO-DAS) offer the
opportunity to overcome attenuation limitations and deploy measures over long
distances . More recently, several studies focused on
the analysis of the microseismicity (MS) observed on unstable slopes. MS
studies analyze the seismic waves generated by the release of strain energy
in the ground at a larger scale than the grain-to-grain interactions in the
frequency range of 1 to 500 Hz. The method offers the opportunity to
remotely record the spatial distribution of the deformation through time
and is less sensitive to attenuation than AE
methods. installed seismometers on the Slumgullion
slow-moving landslide (Colorado, USA) in order to understand the mechanical
processes taking place during landslide deformation. Further studies used the
same method for several slope configurations (hard or soft rocks, soils, very
slow to rapid movements) but also investigated the possible links between the
displacement rate and the seismic energy release
.
correlated the seismic response of the
Séchilienne rockslide with the surface displacement rate and the rainfall
amount. The analysis of the seismic waves generated by landslides allows for
monitoring spatiotemporal changes in the stress–strain field in the material
from the scale of microscopic internal damage to the initiation (e.g., pre-failure) of large
ruptures . Both the
failure and surface processes (e.g., rockfall, debris flow) generate seismic
waves. Physical properties (mass, bulk momentum, velocity, trajectory) of the
landslide can be inferred from the analysis of the seismic signals
.
On clayey landslides, drops in shear-wave velocity have been observed before
acceleration episodes. This shear-wave variation through time has been
documented using noise correlation techniques for laboratory experiments
, and for a few cases in the field at Pont Bourquin
landslide (Switzerland; ), at Harmaliére landslide
(France; ) and at Just-Tegoborze landslide (Poland;
). Precursory seismic signals are also expected and
documented before large failures. Precursory increase in microseismic
activity (in terms of event rates and/or average amplitudes) has been
observed first before the fall of a coastal cliff (Mesnil-Val, France;
) and was interpreted as the propagation of a fracture.
More recently, repeating events have been detected before the Rausu landslide
(Japan; ) and the Nuugaatsiaq landslide (Greenland;
). These events are likely associated with the repeated
failure of asperities surrounded by aseismic slip, driven by the acceleration
of the slope displacement during the nucleation phase of the landslide
rupture. recorded harmonic tremors that started 30 min
before the failure of the Askja caldera landslide (Iceland) with temporal
fluctuations of resonance frequency around 2.5 Hz. This complex tremor
signal was interpreted as repeating stick-slip events with very short
recurrence times (less than 1 s) producing a continuous signal. However, the
characterization of the size of the asperity and the velocity of the ruptures
associated with these precursory signals are difficult to invert mostly
because of the lack of a dense seismic network at close proximity to the
slope instability . Therefore, the monitoring of endogenous
MS may represent a promising approach, especially with the advent of robust,
cheaper and portable seismic sensors and digitizers. It is now possible to
install dense sensor networks close to the unstable slopes and record low
amplitude signals in broad frequency bands. A wide variety of unstable slopes
are currently monitored (i.e., through permanent or campaign installations)
with seismic networks of different sizes and instruments (Table ).
Table of the instrumented sites. The bolded names
correspond to the sites investigated in the present paper to establish the
typology.
G: geophone (f= [0.1–10] kHz); SP:
short-Period (f= [0.1–100] Hz); BB: broadband
(f= [10-2–100] Hz); A: accelerometer; P: permanent monitoring;
RC: repetitive campaigns; SC: single campaign. OPVF/IPGP: volcanological
observatory of the Piton de la Fournaise/Institut de Physique du Globe de
Paris. USGS: United States Geological Survey.
Understanding the possible mechanisms generating these seismic signals needs
to be achieved. The discrimination of the endogenous landslide seismic
signals is difficult and needs to be established. The objective of this paper
is thus to propose a typology of the landslide microseismic signals recorded
in the field. The proposed typology is based on the analysis of observations
from 13 monitored sites. The typology includes all of the seismic sources
recorded at near distances (<1 km) and in the frequency range of MS
studies (1–500 Hz), and generated by landslides (1) developed in hard or
soft rocks and soils, and (2) characterized by fragile (i.e., rupture) and
ductile (i.e., viscous) deformation mechanisms.
In our work, we first discuss all the physical processes that occur on
landslides and may generate seismic signals. We further present the available
seismic sensors, the most commonly used network geometry and the instrumented
sites. Then we establish a classification scheme of the landslide seismic
signals from relevant signal features based on the analysis of the datasets
of 13 sites. We further discuss the perspectives and remaining challenges of
monitoring landslide deformation with MS approaches. The seismic signals
associated with very large rock/debris avalanches and slides observed at
regional distances are out of the scope of this work.
Description of landslide endogenous seismic sources
This section describes the possible hydromechanical processes observed on
landslides that are susceptible to generate seismic
sources. We present the conditions controlling their occurrences (type of
material, topography), their sizes and their mechanical properties.
Fracture-related sources
The term fracture denominates any discontinuous surface observed in
consolidated media and originating from the formation of the rocks
(i.e., joint) or the action of tectonic (i.e., schistosity), gravitational or
hydraulic loads. In the case of slow-moving landslides, the propagation of
the material also creates fractures on the edge and at the base of the moving
material. Fractures occur in all types of materials at different scales
ranging from grain rupture to metric faults. The term fissure is sometimes
used to describe fractures affecting the surface of the ground and for
fractures affecting poorly consolidated material. We here include all these
surface discontinuities under the general term “fracture”. Fractures are
generated in three basic modes (I: opening, II: sliding and III: tearing)
depending on the movement of the medium on the sides of the fracture plane.
They result from either brittle failure of the media or from desiccation
effects forming polygonal failures during soil drying. On landslides, most of
the fractures occur in a tensile mode because of the low tensile toughness of
the landslide material and the shallow depth . The
formation of fractures can also be generated in depth by progressive
degradation of the rock through ground shaking and/or through weathering and
long-term damage due to gravitational load. At the base and on the edges of
the landslide, the movement is assumed to develop fractures in shear mode,
creating sliding surfaces. Shearing on the fracture plane and tensile
fracture opening/closing generate seismic signals. Shearing takes place at
different scales ranging from earthquakes on tectonic plates to grain
friction and generates a variety of seismic signals . The
unstable regime leads to stick-slip behavior where the stress is regularly
and suddenly released generating impulsive seismic events. Tremor-like
signals or isolated impulsive or emergent events are also generated during
plate motions. A variety of signals are observed during glacier motion. Deep
icequakes are usually associated with basal motion
.
Tremor-like signals are also recorded during glacier motion
. They are characterized by long duration signals of low
amplitudes with no clear phase onsets. They are associated with repetitive
stick-slip events on the fracture plane. Tensile fracture opening/closing
generate similar signals on glaciers at the surface and at depth
. Knowledge of the focal
mechanism and location of the source allows differentiation between the
tensile and shear mechanism.
Topple and fall related sources
On vertical to subvertical slopes, mass movement occurs as the topple of rock
columns or as the free fall (and possibly bouncing and rolling) of rocky
blocks . In the case of toppling, the movement starts with a
slow rotation of the rock blocks under the effects of water infiltration or
ground shaking and ends with the free fall of larger blocks. Rockfalls,
during the propagation phase, impact the ground at some location along their
trajectory. These impacts generate seismic waves that can be recorded
remotely by seismometers. The range of rockfall volumes can be very large,
varying from less than one cubic meter to thousands of cubic meters.
Mass-flow-related sources
Mass flows gather different run-out processes of debris or of a mixture of
water and debris. They cover a large range of volumes from large rock
avalanches of several millions of cubic meters to small (hundreds of cubic
meters) debris falls and flows . They can occur in wet or
dry conditions. The contacts of the rock/debris fragments with the bedrock
and in the mass flow generate seismic radiations
.
The seismic signal is hence a combination of grain contacts within the
granular flow and of grain-to-ground surface contacts and hence generate a
complex seismic signal.
Fluid-related sources
Hydrological forcing (e.g., precipitation, snowmelt) is one of the most
common landslide triggers. The presence of fracture networks, water pipes and
the heterogeneity of the rock/soil media result in the development of
preferential water flow paths . These
preferential flows induced local saturated area where the increase in pore
water pressure may destabilize shallow or deep shear surfaces. In soils, the
dissolution of material into finer granular debris creates weak zones prone
to collapse either by suffusion (i.e., non cohesive material wash out under
mechanical action) or by dispersion (i.e., chemical dissolution of fractured
clay soils; Richards, Jones, 1981). In rocks, pipes may develop by erosion.
In these saturated fracture networks, hydraulic fracturing can occur creating
earthquakes and harmonic tremors related to flow migration in the fractures
.
Landslide seismic investigationSensors used in landslide monitoring
Body and surface mechanical waves may be generated by the sources described
in Sect. 2. Body waves (primary – P, secondary – S) radiate inside the
media. P waves shake the ground in the same direction they propagate while
S waves shake the ground perpendicularly to their propagation direction.
Surface waves only travel along the surface of the ground and their velocity,
frequency content and intensity change with the depth of propagation.
Acoustic waves can be generated by the conversion of body waves at the
surface. These waves travel in the air at a velocity of about
340 m s-1, slightly varying with temperature and air pressure.
Acoustic waves are often generated by anthropogenic or atmospheric sources
(gun shots, explosions, storms, etc.), but can also be generated by
rockfalls, debris flows or shallow fracture events. All these mechanical
waves are subject to attenuation with the travel distance; the high-frequency
waves are attenuated faster than the low-frequency waves. The relatively low
energy released by the landslide-related sources makes the choice of seismic
instruments to deploy very important. Four types of instruments are used to
record ground motion for different frequency ranges and sensitivities. For
landslide monitoring, short-period (SP) seismometers and geophones, broadband
(BB) seismometers, accelerometers and AE sensors are commonly installed in
the field.
Broadband seismometers are force-balanced sensors with a very low corner
frequency (<0.01 Hz) that can record the ground motion with a flat response
in a large frequency range [0.01–25] Hz. They require a careful mass calibration
during their installation and are sensitive to temperature and pressure variations.
They are mostly used to record very weak ground motion and ambient noise;
SP seismometers are passive or force-balanced instruments with a high corner
frequency (>1 Hz). They measure the velocity of the ground with high
sensitivity and a flat response in the [1–100] Hz frequency band. They are
recommended for volcanic and glacier monitoring among other applications.
They are less sensitive to air temperature and pressure variations and do not
require mass calibration. They are hence particularly suitable for landslide
monitoring. Geophones are similar to SP seismometers but usually cover higher
frequencies [1–600] Hz with lower sensitivity. They are mainly used for
active seismic campaigns but may also be installed for the same purposes as
SP seismometers;
Accelerometers are strong motion sensors able to record high amplitudes and
high-frequency seismic waves. They can resolve accelerations in the frequency
bands from 0.1 to 10 kHz. The response of the sensor is proportional to
ground acceleration for all frequencies (there is no corner frequency). But
the noise level is important for low frequencies and the sensitivity is not
as good as for velocimeters. They are used to record strong ground motion in
particular when installed close to epicenters (<100 km) of large
earthquakes where seismometers usually saturate. For landslides, they are
usually used as inclinometers;
AE sensors can record ground vibrations at very high
frequencies (10–10 MHz) and low amplitudes. There are two types of AE
sensors: the first type is very sensitive to a narrow frequency band only,
while the second type is sensitive to a broader frequency band
. In the field, a waveguide is often installed together
with AE sensors in order to counteract the attenuation of the signal. They
are used in combination with accelerometers for structural monitoring and for
laboratory experiments (e.g., loading, shear, flume tests) and can be used on
landslides to monitor very low magnitude sources at the grain-to-grain
interactions ;
In addition, microphones or infrasound sensors can be useful to detect,
locate and classify landslides seismic signals .
The detection of acoustic waves and body waves at one point, because they propagate
at different velocities, can be used to estimate the distance from the source. The relative
amplitude of seismic and acoustic waves can also provide information on the depth of the
source, because shallow sources generate more acoustic waves than deeper ones.
It must be noted that AE sensors only record AEs generated at very high
frequencies (>10 kHz) and consequently are very sensitive to attenuation.
Indeed, attenuation factor Q is estimated to range between 10-2 and
101 dB cm-1. Even with a waveguide, they must
be collocated with the cracks or the sliding surfaces observed on the slope
. BB and SP seismometers and geophones record seismic
signals in the common band of 100–102 Hz and hence offer a solution
to monitor more distant sources. The detection of a seismic source by MS
sensors depends on the seismic energy released by the source, the sensor to
the source distance and the attenuation of the media. Installation of MS
sensors at the proximity of the geomorphological features of interest
(e.g., scarp, faults, sliding surfaces, superficial crack networks, etc.)
optimize the detection of the seismic signals generated by those processes
but distant sources (>1 m) can also be recorded by MS sensors. The latter
do not need to be colocated with the geomorphological features of interest.
After correcting the sensor response, the signals generated by these sensors
can be analyzed and compared in their common frequency range. Installation of
BB seismometers can complete SP networks and enable us to investigate the
low-frequency signals generated by the slope, while geophones are more
adapted to explore very-high-frequency content (>100 Hz). Dense networks of the latter instruments are recommended to
investigate the seismicity induced by landslide deformation while the
installation of one unique BB seismometer is enough to investigate the
low-frequency radiations of the landslide.
Network geometry
Several network configurations have been tested in different studies. It must
be noted that the network geometry in the case of landslides is constrained
by the site configuration. Indeed, the maintenance of seismic sensors may be
very challenging when installed on the moving parts of the landslide;
therefore, an installation on the most stable parts of the landslide or at
its vicinity is often preferred for permanent monitoring
. During field campaigns,
maintenance of sensors installed on the unstable slopes is possible and often
realized . Therefore, the main
challenges for seismic sensor installation at this scale are (1) to locate
the sensor at close distance to the sources, (2) to maximize the number of
stations and to locate the sensor close to each other to record the same
event at different seismic station, and (3) minimize the azimuthal gap
between the sensors. The number of deployed sensors plays an important role
in the magnitude of completeness (Mc) of the seismic network, while
the geometry of the network (i.e., inter-sensor distances, azimuthal gap)
mostly controls the accuracy of source locations.
Seismic sensors can be deployed in a network of single sensors or a network
of sensor arrays. The difference between seismic networks and seismic arrays
is related to the distance at which the signals recorded by two sensors can
be correlated. In the case of seismic arrays, the distance between the
sensors is reduced to maximize the correlation of the signals recorded by
each sensor. Otherwise the installation is called a seismic network
. Although the inter-sensor distance is often small
(<1 km) in the case of landslide monitoring, decorrelation of the signals
is often observed even at small distances due to the complexity of the
underground structure especially at high frequencies. The use of the seismic
array approach in landslide monitoring often refers to specific geometries of
collocated sensors (inter-sensors distances <50 m) organized with a
central sensor (often a three-component seismometer) and several satellite
sensors (often vertical sensors). This kind of installation presents many
advantages such as enhancing the signal-to-noise ratio and allowing
the computation of the back azimuth of the source with beam-forming methods.
For the majority of the instrumented landslides, seismic networks are
organized with single sensors located on or at close distance to the unstable
slopes. The inter-sensors distance and the azimuthal gap are often controlled
by the location of easily accessible or stable portions of the slopes.
However, a specific geometry can be adopted such as (almost) a linear
geometry. This is particularly the case for monitoring the propagation of
debris flows in stream channels. Dense networks (number of sensors >50) can
also be deployed. In this case, the sensors are installed using a grid
geometry with regular inter-sensor distances. This kind of installation is
probably the most optimal but is currently mostly realized during short
acquisition campaigns due to the difficulty to maintain a large number of
sensors over long periods (battery, data storage, possible movement of the
sensor), especially when installed directly on the unstable zones of
landslides. Finally, the installation of sensors at depth (>1 m) is
challenging for landslides and it has currently only been realized on
hard-rock slopes (e.g., Randa; , or Séchilienne;
). This kind of installation is, however, very valuable to
constrain the depth of the sources.
MS processing chains
One of the current challenges for landslide MS analysis is the development of
dedicated processing chains able to analyze the unconventional seismic
signals observed on landslides. The three steps of MS processing are,
successively, the detection, the classification and the location of the
endogenous seismic events. The development of robust and versatile processing
chains for analyzing landslide MS is challenging because of (1) the low
magnitude of the events and the attenuation of the media that results in
emergent and low signal-to-noise ratio records, (2) the seismic source
radiation patterns that may be single centroid source, double couple source
or volumetric source, and, (3) the heterogeneity and variation in time
(i.e., topography, water table levels, fissures) of the underground structure
preventing the construction of precise velocity models and hence, accurate
source locations.
First, for detecting automatically or manually the seismic events, the use of
spectrograms is common. Spectrograms represent the evolution of the frequency
content in time by computing the Fourier transform on small moving time
windows (e.g., <1 s). Automatic detection is usually carried out with the
STA/LTA (short-term average/long-term average) detector
applied on the summed energy of the spectrogram
.
Second, classifying the detected signals can be carried out automatically by
discarding exogenous events with simple criteria (i.e., threshold on the
signal duration, inter-trace correlation, apparent velocity) but the
determination of the threshold to differentiate the class of signals may be
difficult. Machine learning algorithms offer nowadays the possibility to
automatize and improve this step. developed a hidden
Markov model (HMM) that can detect automatically in the time series the
occurrence of one particular type of events. The success rate of HMM is
reasonable and this technique has the advantage of requiring only one single
example to scan the time series. The Random Forest algorithm has proven its
efficiency for volcanic and landslide signals classification with higher
success rate and versatility . New signals are
successfully classified into multiple pre-defined classes and changes in the
source properties may be detected by change on the uncertainties
. It must be noticed that this approach requires a
training set with sufficient elements to build the model. Good success rates
(i.e., >85 %) are rapidly reached with 100 elements or more per class.
Template-matching filters have also been used in many studies of landslide
collapse and glaciers in order to
detect and classify seismic signals. This method consists of scanning
continuous data to search for signals with waveforms similar to template
signals. It can detect seismic signals of very small amplitude, smaller than
the noise level. Seismic signals are grouped in clusters of similar
waveforms, implying similar source locations and focal mechanism.
Finally, the location of the sources is the most challenging step. Common
location methods (such as NonLinLoc; ) were used
in combination with 3-D velocity models for locating impulsive
microearthquakes occurring at the Randa rockslide .
However, a certain number of recorded signals do not exhibit impulsive first
arrivals and clear P- and S-wave onsets. For this kind of signal, location
methods based on the inter-trace correlation of the surface waveform
or on the amplitude are
more suitable and easier to automatize. Other methods such as HypoLine
aim at integrating different strategies (i.e., first
arrival picking, inter-trace correlation and beam-forming) to accurately
locate the epicenter under the control of an operator, while
developed a method combining amplitude source
location and inter-trace correlation of the first
arrivals in an automatic scheme. In most of the studies, the media
attenuation field and/or the ground velocity are approximated to a 1-D model,
and/or do not take into account the topography. Both the complexity of the
landslide underground structure and of the recorded seismic signals lead to
mislocation of the events that prevents the accurate interpretation of
certain sources and leads to false alarms .
Instrumented sites
In the last two decades, seismic networks have been installed on several
unstable slopes worldwide. Table summarizes the unstable slopes or
debris-flow-prone catchments instrumented with seismic sensors worldwide. The
sites are classified in terms of landslide types (i.e., slide, fall or flow)
according to the geomorphological typology of .
Studies on snow avalanches
are not
integrated. Most of the instrumented sites are located in the European Alps
(France, Italy and Switzerland). Short-period (SP) seismometers and Geophones
(G) are the most common type of instruments. Their installation and
maintenance is easy as they do not require mass calibration in comparison to
broadband (BB) or long-period (LP) seismometers.
Data
Seismic observations from 13 sites are used to propose the typology. The
sites are representative of various types of slope movements and lithology
(Table ) with four slides occurring in hard rocks, four slides
occurring in soft rocks, three rockfall-prone cliffs occurring in hard and
soft rocks, and one catchment prone to debris flows. The seismic instruments
installed on these sites are recording the seismicity generated by the slope
deformation and are installed either permanently or were acquired during
short campaigns (Table ). The Riou Bourdoux catchment is the only
site where the seismic signals were manually triggered as rock blocks were
thrown down the cliff and monitored with cameras, lidar and seismic sensors
().
Characteristic of the seismic network for the 13 sites analyzed in
the present parer. The landslide dimensions are given for the most active
area of the slope instabilities (as presented in the published studies). The
total number of sensors in the seismic network are given, as well as the
minimal and maximal inter-sensor distance and distance to the active zone. In
the cases where only a few of the sensors have been investigated in the
present study, we indicate the number of the sensors as well as the name of
the station in parenthesis.
SiteSensorNetworkNumber of sensors Inter-sensor distance Distance to the landslide Landslidetypegeom.in tot.analyzed min. max. min. max.dim.SéchlienneSPSA4111 (THE)25 m85 m<50 m<200 m600 m × 200 mLa ClapièreSPSN189 (CL4)30 m770 m 900 m × 700 mAaknesGSN8 <50 m250 m0 m 1 km × 1 kmAiguilles-Pas de l'OursBBSN4 205 m690 m0 m200 m500 m × 500 mSuper-SauzeSPSA8 30 m150 m0 m< 100 m800 m × 150 mPont BourquinSPSN2 30 m 0 m 240 m × 35 mPechgrabenSPSA + SS5 5 m40 m0 m 500 m × 100 mSlumgullionSPD-SN88 11 m450 m0 m 1 km × 500 mChamoussetSPSN7 15 m50 m0 m40 m60 m × 30 mSaint-EynardSPSN43∗500 m1.7 km0 m500 m7 km × 300 mRiou BourdouxSP, BBSA + SS5 50 m200 m20 m30 mlength: 200 mPiton de la FournaiseBBSN101 (BOR)––< 50 m 1 km × 300 mReibaxaderGSN9 <20 m200 m0 m 700 m × 50 m
The dimensions of the unstable slopes range from 60 m × 30 m for
the Chamousset cliff to 7 m × 300 m for the Saint-Eynard cliff
(Table ). The seismic networks are deployed with various geometry
depending on the configuration of the slope, its activity and the duration of
the installation. For most of the sites, at least one seismic sensor is
deployed on the active zone or very close to it (Table ). The
maximal distance to the slope instabilities is 500 m for the Saint-Eynard
cliff being the largest investigated site of our study.
The seismic network geometry of the majority of sites is a distributed
seismic network where sensor locations are regularly installed over the
active zone or in its vicinity. In the case of the Rebaixader catchment, the
seismic network is installed at the border of the stream channel almost
linearly. At the Slumgullion landslide, a dense network has been installed
with regular spacing of the seismic sensors. Seismic arrays are installed at
the other sites. The geometry of the seismic arrays are triangular in shape
with the exception of the Séchilienne landslide where an hexagonal shape is
used.
The instruments are mostly SP seismometers with natural frequencies of 1 to
5 Hz. Fewer geophones and BB seismometers are installed at the sites. The
instrument response is corrected for all of the dataset. To be consistent
with the sensitivity of all the sensors, we do not investigate the data below
1 Hz for BB seismometers or above 100 Hz for SP seismometers and geophones.
The dataset being analyzed is composed of either published seismic events or
published catalogs. The comparison of these events and catalogs enable us to
compare the signals and to compose the classes of the typology. In the case
that no published events or catalogs are available, we manually analyzed the
dataset to complete the number of examples for each proposed class (see
Sect. 5 for detailed information).
Methodology
The seismic signals recorded at different sites are compared in order to
identify common features. Seismic signals result from the convolution of both
the wave propagation and of the seismic source mechanism. Consequently, the
observation of common signal features in signals recorded at different sites
can only be explained by similar source mechanisms. The proposed typology is
hence based on the analysis of these common features. We then selected nine
signal features in order to quantify the differences and similarities between
the different classes. The nine parameters are chosen because they correspond
to the criteria used by experts to analyze and classify a seismic signal and
also because they can be used in automatic classification algorithms
.
They can be computed for any signal type and present a robust framework for
future comparison. The selected signal features are the
following.
The duration of the signal T (expressed in seconds) is computed on the stacked
spectrogram of the traces .
The dissymmetry coefficient of the signal (expressed in percent) is computed as
s=tm-t1t2-t1×100,
where t1, t2 and tm are the time of the signal onset, ending and
maximum, respectively.
The number of peaks of the signal envelop Npeaks, computed as the number of
local maximum above 50 % of maximal value of the signal envelop. The
envelop of the signal is computed as the absolute value of the Hilbert
transform of the signal. The envelop is smoothed by computing the average on
a moving window of length δt=100fsT.
The duration of the signal autocorrelation is defined as
Tcorr=tcT,
with
tc=maxt(C(t)<0.2×max(C)),
where C is equal to the signal autocorrelation. Tcorr is expressed
in percent (%) and represents the duration of the signal correlating with
itself. As an example, a signal with a rapid and abrupt change in frequency
content will rapidly be uncorrelated (low Tcorr), while a signal
with a constant frequency content will have a long autocorrelation (high
Tcorr).
The mean frequency (expressed in hertz) is computed as
Fmean=∑i=1NPSD(fi)fi∑i=1NPSD(fi)
with the power spectral density (PSD) defined as
PSD(f)=2|FFT(y)|2Nfs,
where fs and N are the sampling frequency of the signal and the number
of samples, respectively. The mean frequency is chosen here as it is more
representative of the signal spectrum energy and less sensitive to noise than
the frequency of maximum energy. .
The frequency corresponding to the maximal energy of the spectrum is denoted Fmax
(expressed in hertz).
The frequency bandwidth Fw is defined as
Fw=2∑i=1NPSD(fi)fi2∑i=1NPSD(fi)-Fmean2.
The minimal frequency of the signal spectrum is computed as
fmin=minf(PSD(f)<0.2×max(PSD)).
The maximal frequency of the signal spectrum is computed as
fmax=maxf(PSD(f)<0.2×max(PSD)),
which is the maximal frequency of the signal spectrum fmax (not to
be confused with parameter Fmax defined above).
The signal features are always computed on the trace with the maximal
amplitude passed in the band [fc–50] Hz (fc: natural frequency). This
enables is to limit the influence of the wave propagation and to compare
signals with different sampling frequencies (i.e., 120 to 1000 Hz).
Based on already published events and further interpretations, we propose a
standard classification of landslide endogenous seismic sources. The
unpublished datasets are used to investigate the presence of these signals at
other sites and to increase the number of examples for different contexts.
Numerous signals were analyzed to draw the proposed classification and
selected examples are further presented to describe the different classes.
Seismic description of the signals – typology
The typology of the signals is based on the duration and the frequency
content of the seismic signals. The signals are classified into three main
classes: “slopequake” (SQ), “rockfall” (RF) and “granular flow” (GF).
For slopequake, subclasses are proposed and discussed based on the frequency
content of the signals. Several examples of signals recorded at different
sites are presented and the sources are discussed in the corresponding
section.
Conceptual scheme of the landslide endogenous seismic sources with
(a) wet granular flow, (b) dry granular flow,
(c) rockfall, (d) tensile fracture opening,
(e) tensile cracks opening, (f) shearing and
(g) fluid migration in fracture.
Example of one controlled rockfall (mass = 430 kg) at the Riou
Bourdoux catchment recorded by a SP seismometer located
at 50 m from the rock departure (a) and recorded by a BB
seismometer near the rock arrival (b). The waveforms of the vertical
traces are plotted in the upper part of the figure. The amplitude are
normalized on the trace with the maximal amplitude (black), the signal
recorded by the other sensors (when available) are represented in color
below. The maximal amplitudes (Amax) of all the traces are plotted on
the subplot in nm s-1. The spectrogram is plotted on the middle part of
the figure and normalized to the maximal energy. The lower part of the figure
represents the PSD of the most energetic trace and the frequency
corresponding to the maximum and the mean of the PSD are plotted in red and
gray, respectively.
Rockfall (RF)
Figure displays the seismic waves recorded for a single block fall
at the Riou Bourdoux catchment (French Alps). The block was manually launched
in the catchment and recorded with seismic sensors and cameras
. The signal is characterized by successive impacts
visible both on the waveform and on the spectrograms and lasts around 20 s.
The spectral content contains mostly frequencies above 10 Hz but energy
below 10 Hz is present for certain impacts (Fig. a). At closer
distance, very high frequencies can be recorded up to 100 Hz
(Fig. a). The autocorrelation remains large over time due to the
similitude of the individual impact signals (Tcorr>10 %). P and
S waves are hardly distinguishable on the record and the signals recorded at
the seismic sensors are dominated by surface waves
.
Rockfall events recorded at (a) and
(d) Super-Sauze (France; ), (b) at the
Séchilienne (France; ),
(c) Chamousset , (e) Aaknes and
(f) Mount Saint-Eynard slopes . See Fig.
for a description of the figure.
Seismic signals of natural masses detaching from cliffs are presented in
Fig. . They present similar characteristics to the artificially
triggered rockfall. Depending on the height of the cliff, the signal ranges
from 5 seconds up to tens of seconds. The symmetry of the signal ranges from
0 to 80 % depending on the cliff configuration. In general, the most
energetic impacts are recorded at the middle or after the middle of the
signal (dissymmetry coefficient >50 %). The highest measurable frequency
depends on the source-to-sensor distance and can be very high (>100 Hz).
The spectral energy is concentrated in frequencies above 5 Hz, with the
largest PSD values (Fmax) ranging from 20 to 40 Hz. Generally, the
PSD energy is low below 10–15 Hz with the exception of one case (Fig. 5c)
where spectral energy can be observed. The initial falling masses can
themselves break into smaller units during propagation. In this case, the
signal does not return to the noise level between the impacts due to
developing granular flow (Fig. b, e, f) leading to a decrease in
the duration of the autocorrelation of the signal. When several blocks are
falling at the same time, impacts may overlap, and so do the peaks of the
signals. In certain cases, the first rock free fall is preceded by a signal
that can be associated with the rock detachment. An example of this
precursory signal can be observed in Fig. a, f and in the data
reported by and . The seismic signals of
rockfalls contain information on the physics of the process. The seismic
energy of rockfall signals is proportional to the volume
. Scaling laws have also been established
between seismic energy, momentum, block mass and velocity before impacts
. The frequency content is mainly controlled by the block
mass. The frequency of the spectral maximum energy decreases when the block
mass increases . If the rockfalls are
well isolated, each impact generates impulsive waves. In the case of multiple
rockfalls or short distances between the seismic sources and the sensors, the
first arrivals may be emergent due to simultaneous arrivals of waves
generated by impactors of different sizes impacting the ground at closely
spaced time intervals .
Dry granular flow events recorded at (a) Séchilienne
and (b) the Piton de la Fournaise caldera. See Fig. for a
description of the figure.
Granular flow (GF)
Granular flows are characterized by cigar-shape signals lasting between tens
to thousands of seconds. They are subdivided in two classes.
Dry granular flow (Fig. ): These signals are characterized by
cigar-shape waveforms of long duration (<500 s). Due to the absence of
water, the source generally propagates over small distances. The duration of
autocorrelation is very weak (Tcorr≈0 %) and no seismic
phase can be distinguished. No distinguishable impacts can be observed in the
waveform nor in the spectrogram unlike rockfall signals. The signal
onset is emergent, P and S waves are hardly distinguishable and the signal is
dominated by surface waves
. The
dissymmetry coefficient of the signal varies between 30 % and 75 % and
depends on the acceleration and the volume of mass involved in the flow
through time
. The
frequency ranges from 1 to 35 Hz. The maximal frequency of the PSD varies
between 5 and 10 Hz and can be larger (up to 20 Hz) when the seismic
sensors are located close to the propagation path. The PSD values are
significantly low below 3 Hz and increase rapidly between 3 and 20 Hz.
Wet granular flow (Fig. ): These signals range from several thousands
of seconds to several hours and correspond to debris flows. They occur during
rainfall episodes when fine material and boulders propagate downstream over
long distances (>500 m). Like dry granular flow, the duration
autocorrelation is very weak (Tcorr=0 %) and no seismic phase
can be distinguished. The seismic sensors are often installed at very close
distance to the flow path so high frequencies up to 100 Hz may be recorded
. Little energy is present in the
low frequencies (<10 Hz) depending on the amount of water and the size of
the rocky blocks integrated in the flow . The signal is
emergent and the amplitude variation depends on the mass involved in the flow
passing in the vicinity of the sensor. Debris flows are very often divided
into a front with the largest boulders and the highest velocity followed by a
body and a tail where the sediment concentration and the velocity decrease
. The seismic signal amplitude hence increases
progressively as the front is passing in the vicinity of the sensor
, and decreases
progressively as the front is moving away from the sensor (dissymmetry
coefficient >50 %). Large spikes and low frequencies may be observed in
the seismic signal corresponding to the front of the debris flow generated by
large boulder impacts. The frequency content also changes and, progressively,
energy in the lower frequencies decreases (Fig. a).
Wet granular flow events recorded at Rebaixader torrent
. See Fig. for a
description of the figure.
Slopequake (SQ)
The “slopequake” class gathers all the seismic signals generated by sources
located within the slope at the subsurface or at depth such as
fracture-related sources or fluid migration (cf. Sect. 2). Different names
have already been proposed for this kind of signals: “slidequakes”
, “micro-earthquake”
, “quakes”
or “landslide micro-quake (LMQ)”
. We here proposed the term “slopequake” as a general
name for these events. They are characterized by short duration (<10 s)
and are subdivided into two classes “simple” and “complex”.
Low-frequency slopequakes recorded at the
(a) Slumgullion , (b) Pont Bourquin,
(c) La Clapière and (d) Aiguilles-Pas de l'Ours slopes.
See Fig. for a description of the figure. Note that for the
Slumgullion signals (a), the amplitudes are expressed in
counts.
Simple slopequake
“Simple slopequake” signals are of short (<2 s) to very short duration
(<1 s). Their main feature is the triangular-shape of the spectrogram with
largest amplitudes being recorded in the first part of the signal
(dissymmetry coefficient <50 %). The first arrivals contain the highest
frequencies of the signal and are followed by a decrease in the frequencies.
Depending on the frequency content, these signals can be subdivided into
three classes.
Low-frequency slopequake (LF-SQ) (Fig. ): The signal lasts
between 1 and 5 s. The maximal amplitude of the signal waveform occurs at
the beginning or at the center of the signal (15 % < dissymmetry
coefficient <50 %). The waveform presents only one peak and most of the
first arrivals are emergent. Phase onsets are difficult to identify. The
signals are mostly dominated by surface waves. Consequently, the duration
autocorrelation of the signals is large (>10 %). The largest PSD values
are observed between 5 and 25 Hz with a mean frequency ranging between 10
and 15 Hz.
High-frequency slopequake (HF-SQ) (Fig. ): The signal lasts
between 1 and 5 s. The maximal amplitude of the signal waveform occurs close
to the beginning of the signal (dissymmetry coefficient <30 %). The
waveform presents only one peak and the first arrivals are mainly impulsive.
Different phases may be observed : P arrivals
are detected at the beginning of the signal and correspond to the
high-frequency waves, surface waves are then observed at the time the
frequency decreases. However, in general the short sensor-to-source distance
makes the differentiation between the different seismic phases difficult. The
autocorrelation of these signals is hence lower than for LF-SQ (<10 %).
In most of the cases, the picking of the different wave onsets is made
difficult because of the sensor-to-source distances and the low-frequency
sampling. The largest PSD values are observed between 3 and 45 Hz with a
mean frequency ranging between 20 and 30 Hz.
Hybrid slopequake (Hybrid-SQ) (Fig. ): The signal lasts between
1 and 2 s. It presents the characteristics of the two precedent signals. The
brief first arrivals are very impulsive and last less than 1 second. They are
followed by a low-frequency coda similar to the LF-SQ. The maximal amplitude
of the signal waveform occurs close to the beginning of the signal
(dissymmetry coefficient <40 %). The waveform presents only one peak and
the first arrivals are impulsive.
High-frequency slopequakes recorded at the (a) Super-Sauze
, (b) Séchilienne
, (c) Pont Bourquin,
(d) La Clapière, (e) Aaknes and
(f) Slumgullion slopes. See Fig. for
a description of the figure. Note that for the Slumgullion signals
(f), the amplitudes are expressed in counts.
These signals are suspected to be associated with boundary or basal sliding
or fracturing
of the slope . Currently, only few
studies have proposed inversion of the source tensor . To the
best of our knowledge, for soft-rock landslides, no source mechanism was
modeled. Therefore, it remains difficult to see if the observation of LF- and
HF-SQs is due to attenuation of the high frequencies with the distance or to
the source mechanism. Indeed, the rupture velocity may explain the difference
in frequency content and low-frequency earthquakes are observed on tectonic
faults . They are characterized by low
magnitude (Mw<2) and short duration (<1 s) and constitute at least
part of the seismic tremor signal. Therefore, the main assumption for the
source of these events is slow rupture . Another
interpretation for the low-frequency quakes dominated by surface waves is
crevasse opening (at the surface) as observed in glaciers
. analyzed AE at
laboratory scales generated during thermal fracturing. During this
experiment, high-frequency AEs are recorded during the heating stage up to
the failure of the rock sample and are interpreted as thermal cracking events
. Low-frequency AEs are recorded during the cooling
stage (after failure) and are associated with stick-slip events
.
Hybrid slopequake recorded at the (a) Pechgraben and
(b) Super-Sauze landslide. See Fig. for a description of
the figure.
Hybrid slopequakes are very similar to the events recorded on volcanoes and
glaciers with the presence of fluids in conduits or crevasses
. The sources of these events are assumed
to be related to hydro-fracturing. The first high-frequency events
corresponding to a brittle failure is followed by water flow into the newly
opened cracks .
The frequency content depends on the sensor-to-source distance and on the
source mechanism. Observation of LF- and HF-SQ may be the signature of
ongoing processes taking place within the slope instabilities justifying the
three proposed classes for simple slopequakes.
Examples of slopequakes with precursory events recorded at the
(a) Super-Sauze, (b) Séchilienne and
(c) Chamousset slopes. See Fig. for a description of the
figure.
Complex slopequake
The second class of short-duration signals has the same general properties as
the simple slopequakes but exhibits particular frequency content or
precursory events. These additional characteristics change the possible
interpretation of the sources. Consequently, these signals are gathered in
the class “complex slopequake”. Three different subclasses are proposed:
Examples of tremor-like slopequakes recorded at the (a, c) Super-Sauze, (b) La
Clapière and (d) Aiguilles-Pas de l'Ours slopes. See
Fig. for a description of the figure.
Slopequake with precursors (Fig. ): The third class of short
duration signals are similar to the slopequake signals but are preceded by a
precursory signal of smaller amplitude (Fig. ). The content of the
precursory signal ranges from 5 to 100 Hz depending on the site and is
slightly lower than the highest frequency generated by slopequake-like event.
The precursory arrival lasts up to 1.2 s in the presented examples and no
clear phases are detected. The frequency content ranges from 5 to 100 Hz but
varies significantly at each site. At all sites, the amplitude of the signal
is significantly higher for one of the sensor (3 to 50 times higher) when
considering vertical traces. The precursory signal is buried in the noise at
the sensors with lowest amplitudes and the signal is similar to a LF-SQ. Such
events have never been documented to our knowledge. They are likely to be
generated by a strong and local source located in the very close vicinity of
one of the sensors (<10 m) due to the maximal amplitude
(>105 nm s-1) and the rapid decrease in the amplitude recorded by
the other sensors. Although the signal is similar to certain earthquakes (the
precursory signals interpreted as P-wave arrivals and the strong arrivals as
surface waves), no earthquake location can explain the signal recorded at the
time these events are recorded. Their occurrence in the nighttime makes a
human activity unlikely to be the source. The most probable source would then
be the detachment of a single block and its fall in the vicinity to one of
the sensors. This kind of precursory signal is observed for some rockfalls
(Fig. a) and at a the Saint-Martin-le-Vinoux quarry (France;
). At the Saint-Martin-le-Vinoux underground quarry,
the duration between the detachment and the signal impact is well correlated
to the room height. This interpretation is coherent with the drop of
amplitude before the more energetic event at the Chamousset rock column
(Fig. c), where a progressive decrease in the precursory signal is
observed. However, at the other sites (Fig. a, b) such a decrease
is not present. The 1-second-long precursory signal has a constant amplitude
and frequency content. Another interpretation could be that these precursory
signals are a succession of overlapping slip or fracture events. The
interpretation of these signals cannot be established with certainty and
further analysis (i.e., location, time of occurrence) and other examples are
needed to discriminate the mechanism at work.
Tremor-like slopequake (Fig. ): The last class of short-duration signals often last between 1 and 5 s (Fig. ). They
present a symmetrical waveform (S=50 %) with emergent arrivals and a slow
decrease in the amplitude to the noise level. The frequency ranges from 5 to
25 Hz. High frequencies may be briefly recorded in certain events
(Fig. c). The maximal energy of the PSD corresponds to a frequency
of 8 to 13 Hz, while the mean energy corresponds to a frequency of 13 to
17 Hz. No seismic phases are identified. The signal is not recorded by all
the sensors even when the sensors are organized in small arrays with short
inter-sensor distances (<50 m). Their waveforms and frequency content are
similar to the one of the granular flows (Fig. ). Small debris
flows have been observed at La Clapière and Super-Sauze landslides and
are likely to generate seismic waves; however, small debris flows are not
observed at the Pas de l'Ours landslide when these kinds of seismic signals
are recorded. Another possible source mechanism for such events may also be a
very rapid succession (<1 s) of shear events along the basal or the side
bounding strike-slip faults . Further investigations are
needed to analyze their occurrences over time and their location to confirm
one or the other assumption.
(a) Summary of the proposed classification with plots of
the attributes for the examples presented in the precedent figures and an
example waveform for each class. The convention for the attribute plot is
presented in (b); n being the number of seismic signals examples
used to plot the feature diagram.
Discussion
The proposed typology is summarized in Fig. . The approach
consisted of comparing the datasets of different sites in order to identify
the common features of the recorded seismic signals. Three main classes can
be differentiated mainly from the length of the signals, the number of peaks
and the duration of the autocorrelation. Figure shows more
examples of the signal variability for the sites where long seismic catalogs
have been recorded (e.g., Aaknes, Chamousset, Séchilienne, Super-Sauze and
La Clapière). Only the signals classified as rockfall, LF-SQ and HF-SQ are
presented because fewer events of the other classes are present in the
investigated datasets. The signal features are in good agreement with the
defined classes proposed in the present classification (Fig. ). In
general, narrow variability is observed on the feature values among the
different sites and consequently, the observed features are likely associated
with the source mechanism.
Variability in the signal features of classes “rockfall”,
“HF-SQ” and “LF-SQ” for five different sites: Aaknes, Chamousset,
Séchilienne, Super-Sauze and La Clapière. The axes of the star diagrams
are the same as in Fig. .
However, some variability exists for rockfall events. Indeed, the volume of
the blocks and possible breaks control the frequency content and the
autocorrelation duration while the height of the scarp will play a
significant role in the duration of the event. Depending on the site,
rockfall signals can have similar features to each other
(e.g., Séchilienne, Fig. ) suggesting a constant source
mechanism or very different features suggesting the presence of different
rockfall mechanisms and/or trajectories (e.g., Super-Sauze,
Fig. ). In the case of the Super-Sauze datasets, rockfall are
characterized by a lack of energy in high frequencies due, in this case, to
the distance between the seismic network and the scarp. Installation of
additional sensors could be the easiest way to get rid of this variability.
It must also be noted, that differentiating flow and fall signals may be
challenging. Indeed, some of the events are very likely a mix of these two
sources. Rockfalls of various blocks may generate granular flows with metric
block impacts, both overlapping in the recorded seismic signals. Presence of
metric rocks is also observed in debris-flow-prone torrents; for this type of
event, the block impacts within the mass flows are recorded in the seismic
signals .
Examples of pure harmonic signals recorded at the
(a) Pechgraben, (b) La Clapière,
(c) Aiguilles-Pas de l'Ours, (d) Séchilienne,
(e) Slumgullion and (f) Super-Sauze
slopes. See Fig. for a description of the figure. Note that for
the Slumgullion signals (e), the amplitudes are
expressed in counts.
Our analysis does not allow us, at this stage, to conclude whether the
frequency content of the simple slopequakes is associated with the
source mechanism because complete catalogs differentiating between HF-SQ and LF-SQ are not yet available.
suggested that HF-SQs are the dominant class of
slopequake at the Madonna del Sasso cliff (hard rock) and were generated by
thermal cracking, while LF-SQs associated with frictional sliding are less
frequent. Although we did not investigate the whole dataset, no LF-SQs were
provided at the Aaknes or Chamousset hard-rock cliffs (Fig. ),
while LF-SQs are recorded at the La Clapière and Séchilienne hard-rock
slides: (Fig. ). This observation seems to confirm the results of
. However, further comparison of the occurrence of the
different slopequakes at specific sites in space and time must be done to
improve the comprehension of these sources and confirm this statement.
Harmonic signals have also been documented at the Pechgraben and Super-Sauze
landslides . These signals last from 1 to 5 s and may
repeat during minute-long sequences. The proposed interpretation includes
hydro-fracturing or repetitive swarms of microearthquakes
, while hypothesized that these
kinds of signals were caused by trapped waves along the side-bounding
strike-slip fault generated by shear events. In the investigated datasets,
similar signals are recorded at the La Clapière and the Aiguilles
landslides with a fundamental frequency of 8±1 Hz
(Fig. b, c). At the Séchilienne landslide, harmonic signals
are also detected (Fig. d), mostly during the day, with different
resonant frequencies between 2 and 12 Hz. Similar signals are observed at
the Slumgullion and Super-Sauze sites but without clear harmonics in the PSD
(Fig. e, f). The presence of pipes and drains on or in the
vicinity of these sites could also explain the origin of these signals
justifying that these signals are not included in the slopequake class as
they may not be generated by a slope deformation process. The location of the
source, the distribution of the amplitude, the stability of the fundamental
frequency and the daily temporal occurrence of the source supports this
assumption. Systematic location of these events is needed to determine if
they must be integrated or not in the general typology in the case that they
are generated by fluid resonance in fractures.
For certain signals, the coda is dominated by resonance frequencies
(Figs. d, c) at high frequencies (i.e., 20 and 43 Hz),
and well observed in the spectrogram of the signal. The resonance is not
present before the beginning of the signal and hence can not be due to
anthropogenic noise (i.e., motors). In the case of Chamousset cliff,
explained the presence of this monochromatic coda by the
resonance of the rock column after the occurrence of the rock bridge
breakage. At Super-Sauze, a similar resonant coda is observed at the end of
certain rockfalls (Fig. 4d). Considering the distance between the main scarp
and the seismic arrays (>300 m), and the absence of a large fracture on
the scarp, the occurrence of this kind of resonance is very surprising in
this case. This signal feature could also result from the wave propagation
(i.e., trapped waves).
No long-lasting tremors are presented in this study.
recorded a tremor with gliding before the occurrence of the Askja caldera
landslide. Similar tremors have been found on the Whillans ice stream in
Antarctica during slow slip events , which
repeat twice a day with a slip of about 10 cm lasting for about 20 min.
Therefore, such signals may also occur during the nucleation phase of
landslide failure. The question remains if they are not observed because
landslide acceleration is aseismic due to high pore fluid pressure
or low normal stress at the subsurface of the slope.
Difficulties still arise in providing an exhaustive description and
interpretation of all the sources from the simple analysis of the proposed
signal features, particularly those generating short-duration signals, in
particular for the classes discriminated by the frequency content such as
LF-SQ and HF-SQ. The ambiguity between propagation effect and source
mechanisms prevents further interpretation due to several limitations.
Firstly, the location of the sources remain difficult to establish due to the
complexity of some of the signals
, the size of the
instrumented sites and the complexity of the underground structure that
influences the polarization of the waves and the sensors
(ie. number, location and type: 1C/3C sensor) installed close to the unstable
slopes . The location of the epicenter of most of the
events seems coherent with the instability deformation field at the surface
, although resolving
dispersion and 3-D heterogeneities of the velocity fields currently prevents
us from inferring the depth of the events and their focal mechanisms.
Secondly, a complementary approach to explain the origin of the sources is
the analysis of their occurrence with respect to surface or basal
displacement and monitoring of the water content and pore fluid pressures. It
requires both exhaustive catalogs of landslide seismicity over long time
periods and continuous and distributed datasets of displacements and pore
fluid pressures, which remain challenging to acquire. Finally, in addition to
the characteristics of seismic signals, further information on the sources
processes can be obtained from the distribution of the events in time, space
and size. Events that occur regularly in time with similar amplitudes are
likely associated with the repeated failure of an asperity surrounded by
aseismic slip, for instance, at the base of a glacier
or of a landslide .
Signal amplitudes and recurrence times often display progressive variations
in time. In contrast, events that are clustered in time and space, with a
broad distribution of energies, are more likely associated with the
propagation of a fracture . The daily distribution of
an events time can also be helpful to identify anthropogenic sources, which
occur mostly during the day. In contrast, natural events are more frequently
detected at night, when the noise level is lower.
Simulations and models are also required to explain the current observations.
Indeed, experimental results suggest an increase in AEs correlated with an
increase in the slope velocity or an increase in AE due to
the creation of the rupture area . Acceleration of
pre-existing rupture surface(s) seems to be the mechanism responsible for the
seismicity recorded before large rockslide collapse. and
argued that the high correlation between the repetitive
events could only be explained by stick-slip movement of the locked
section(s), while a cracking process would imply a migration of the location
of the events and a change in the events waveforms. argued
that the presence of gliding frequencies could only be produced by similar
sources and hence close location. On the contrary, in the case of the
Mesnil-Val column, interpreted the evolution from
high-frequency to low-frequency events as the progressive formation of the
rupture surface followed by the final rupture process immediately before the
column collapse, where both tensile cracks and shearing motion on the created
rupture are generated.
Conclusions
Over the last few decades, numerous studies have recorded seismic signals
generated by various types of landslides (i.e., slide, topple, fall and
flow), for different kinematic regimes and rock/soil media. These studies
demonstrated the added value of analyzing landslide-induced MS to improve our
understanding of the mechanisms and to progress in the forecast of landslide
evolution.
In this work we propose a review of the endogenous seismic sources generated
by the deformation of unstable slopes. A dataset of 14 slopes is gathered and
analyzed. Each of the sources are described by nine quantitative features of
the recorded seismic signals. Those features provide distinct characteristics
for each type of source. A library of relevant signals recorded at relevant
sites is shared as supplementary material. We propose three main classes
“slopequake”, “rockfall” and “granular flow” to describe the main type
of deformation observed on the slopes. Slopequakes are related to shearing or
fracturing processes. This family exhibits the most variability due to the
complexity of the sources. These variations are likely to be generated by
different source mechanisms. “Rockfall” and “granular flow” classes are
associated with mass propagation on the slope surface. They are
distinguishable by the number of peaks clearly identified in the seismic
signals.
Presently, several descriptions of the seismic sources are proposed for each
study case. We believe that a standard typology will allow researchers to
discuss and compare seismic signals recorded at many unstable slopes. We
encourage future studies to use and possibly enrich the proposed typology.
This also requires publication of the datasets and/or catalogs to progress
towards a common interpretation. Recently, organizations such as the United
States Geological Survey (USGS) or the French Landslide Observatory (OMIV)
have started this work .
Recent arrivals on the market of relatively cheap and autonomous seismometers
(e.g., ZLand® node systems, Raspberry Shake
systems) will allow the deployment of denser seismic networks of 3C sensors.
The latter will certainly improve the location accuracy and enable inversion
of the focal mechanism of the sources. Moreover, the recent operational
applications of ground-based SAR (synthetic aperture radar) and terrestrial
lidar technologies for monitoring purposes shows their relevance to monitor
distributed surface displacements. Ongoing monitoring on several landslides
combining those innovative approaches will certainly help to associate
slopequake events to deformation processes
.
The proposed typology will help to constrain the design of new models to
confirm the assumptions on the nature and the properties of the seismic
sources. This will be particularly important for (1) explaining the
variability of the slopequake sources observed at the sites, (2) progressing
in the physical understanding of the slopequake sources, and (3) ascertaining
the spatiotemporal variations in the seismic activity observed at some
unstable slopes in relation with their deformation, as well as with external
forcings such as intense rainfalls and earthquakes.
The library of the endogenous seismic signals recorded at
the sites and described in the paper is shared as supplementary material. The
seismic data are shared in the OMIV website:
http://www.ano-omiv.cnrs.fr/ressources/library.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “From process to
signal – advancing environmental seismology”. It is a result of the EGU
Galileo conference, Ohlstadt, Germany, 6–9 June 2017.
Acknowledgements
This work was carried with the support of the French National Research Agency
(ANR) through the projects HYDROSLIDE “Hydrogeophysical Monitoring of Clayey
Landslides”, SAMCO “Society Adaptation to Mountain Gravitational Hazards in
a Global change Context” and TIMES “High-performance Processing Techniques
for Mapping and Monitoring Environmental Changes from Massive, Heterogeneous
and High Frequency Data Times Series”. Additional support by the Open
Partial Agreement “Major Hazards” of the Council of Europe through the
project “Development of Cost-effective Ground-based and Remote Monitoring
Systems for Detecting Landslide Initiation” was available. The continuous
seismic data were provided by the Observatoire Multi-disciplinaire des
Instabilités de Versant (OMIV) . Some seismic signals
analyzed were acquired with seismometers belonging to the French national
pool of portable seismic instruments SISMOB-RESIF. The authors thank
J. Gomberg for access to the data of the Slumgullion slope, constructive
discussions and review of the early version of this paper as well as Nick
Rosser and Emma Vann Jones for access to the data of the North Yorkshire
cliff. The authors also thank Naomi Vouillamoz (University of Stuttgart) and
Pascal Diot (ONF-RTM) for helping with the data acquisition at, respectively,
the Pechgraben site and the Aiguilles-Pas de l'Ours site. The authors
gratefully acknowledge Velio Coviello and Wei-An Chao for their careful
reviews and thorough comments which helped improving the
manuscript. Edited by: Jens
Turowski Reviewed by: Velio Coviello, Wei-An Chao, and one
anonymous referee
References
Abancó, C., Hürlimann, M., Fritschi, B., Graf, C., and Moya, J.:
Transformation of ground vibration signal for debris-flow monitoring and
detection in alarm systems, Sensors, 12, 4870–4891, 2012.Abancó, C., Hürlimann, M., and Moya, J.: Analysis of the ground vibration generated
by debris flows and other torrential processes at the Rebaixader monitoring site
(Central Pyrenees, Spain), Nat. Hazards Earth Syst. Sci., 14, 929–943,
10.5194/nhess-14-929-2014, 2014.
Allen, R.: Automatic phase pickers: Their present use and future prospects,
Bull. Seis. Soc. Am., 72, S225–S242, 1982.Allstadt, K. and Malone, S. D.: Swarms of repeating stick-slip icequakes
triggered by snow loading at Mount Rainier volcano, J. Geophys. Res., 119, 1180–1203, 10.1002/2014JF003086, 2014.Amitrano, D., Grasso, J. R., and Senfaute, G.: Seismic precursory patterns
before a cliff collapse and critical point phenomena, Geophys. Res.
Lett., 32, l08314, 10.1029/2004GL022270, 2005.Amitrano, D., Arattano, M., Chiarle, M., Mortara, G., Occhiena, C., Pirulli, M.,
and Scavia, C.: Microseismic activity analysis for the study of the rupture
mechanisms in unstable rock masses, Nat. Hazards Earth Syst. Sci., 10, 831–841,
10.5194/nhess-10-831-2010, 2010.
Arattano, M. and Moia, F.: Monitoring the propagation of a debris flow along a
torrent, Hydrol. Sci. J., 44, 811–823, 1999.Arattano, M., Abancó, C., Coviello, V., and Hürlimann, M.: Processing
the ground vibration signal produced by debris flows: the methods of
amplitude and impulses compared, Comput. Geosci., 73, 17–27,
10.1016/j.cageo.2014.08.005, 2014.
Arattano, M., Covellio, V., Abancó, C., Hürlimann, M., and McArdell,
B. W.: Methods of Data Processing for Debris Flow Seismic Warning,
Int. J. Ero. Cont. Eng., 9, 114–121, 2016.Benson, P. M., Vinciguerra, S., Meredith, P. G., and Young, R. P.: Laboratory
Simulation of Volcano Seismicity, Science, 322, 249–252,
10.1126/science.1161927, 2008.
Berti, M., Genevois, R., LaHusen, R., Simoni, A., and Tecca, P.: Debris flow
monitoring in the Acquabona watershed on the Dolomites (Italian Alps),
Phys. Chem. Earth Pt. B, 25, 707–715, 2000.Bessason, B., Eirísson, G., Thorarinsson, O., Thórarinsson, A., and
Einarsson, S.: Automatic detection of avalanches and debris flows by seismic
methods, J. Glaciol., 53, 461–472,
10.3189/002214307783258468, 2007.Biescas, B., Dufour, F., Furdada, G., Khazaradze, G., and Suriñach, E.:
Frequency Content Evolution of Snow Avalanche Seismic Signals, Surv. Geophys., 24, 447–464, 10.1023/B:GEOP.0000006076.38174.31, 2003.
Bièvre, G., Helmstetter, A., Lacroix, P., Baillet, L., Langlais, M., Vial,
B., Voisin, C., Larose, E., and Jongmans, D.: Réactivation du
glissement-coulé d'Harmalière (Isère, France), Journées
Aléas Gravitaires, Besançon, France, 2017.Bottelin, P., Levy, C., Baillet, L., Jongmans, D., and Gueguen, P.: Modal and
thermal analysis of Les Arches unstable rock column (Vercors massif, French
Alps), Geophys. J. Int., 194, 849–858,
10.1093/gji/ggt046, 2013a.Bottelin, P., Jongmans, D., Baillet, L., Lebourg, T., Hantz, D., Levy, C.,
Roux, O. L., Cadet, H., Lorier, L., Rouiller, J.-D., Turpin, J., and Darras,
L.: Spectral Analysis of Prone-to-fall Rock Compartments using Ambient
Vibrations, J. Environ. Eng. Geophys., 18,
205–217, 10.2113/JEEG18.4.205, 2013b.Bottelin, P., Jongmans, D., Daudon, D., Mathy, A., Helmstetter, A., Bonilla-Sierra, V.,
Cadet, H., Amitrano, D., Richefeu, V., Lorier, L., Baillet, L., Villard, P., and Donzé,
F.: Seismic and mechanical studies of the artificially triggered rockfall at Mount Néron
(French Alps, December 2011), Nat. Hazards Earth Syst. Sci., 14, 3175–3193, 10.5194/nhess-14-3175-2014, 2014.Brückl, E., Brunner, F. K., Lang, E., Mertl, S., Müller, M., and Stary,
U.: The Gradenbach Observatory – monitoring deep-seated gravitational slope
deformation by geodetic, hydrological, and seismological methods, Landslides,
10, 815–829, 10.1007/s10346-013-0417-1, 2013.
BRGM: Ecoute acoustique et microsismicité appliquée aux mouvements de
terrain. Etat de l'art., Rapport BRGM R38659, BRGM, document annexe à la
PG11., 1995.Brodsky, E. E., Gordeev, E., and Kanamori, H.: Landslide basal friction as
measured by seismic waves, Geophys. Res. Lett., 30, 2236,
10.1029/2003GL018485, 2003.Brown, J. R., Beroza, G. C., Ide, S., Ohta, K., Shelly, D. R., Schwartz, S. Y.,
Rabbel, W., Thorwart, M., and Kao, H.: Deep low-frequency earthquakes in
tremor localize to the plate interface in multiple subduction zones,
Geophys. Res. Lett., 36, l19306, 10.1029/2009GL040027, 2009.
Brückl, E.: Large landslides with seismicity, Rock Mechanics and
Engineering Volume 4: Excavation, Support and Monitoring, p. 365, 2017.Burtin, A., Bollinger, L., Cattin, R., Vergne, J., and Nábĕlek, J. L.:
Spatiotemporal sequence of Himalayan debris flow from analysis of
high-frequency seismic noise, J. Geophys. Res., 114,
F04009, 10.1029/2008JF001198, 2009.
Burtin, A., Hovius, N., Milodowski, D. T., Chen, Y.-G., Wu, Y.-M., Lin, C.-W.,
Chen, H., Emberson, R., and Leu, P.-L.: Continuous catchment-scale monitoring
of geomorphic processes with a 2-D seismological array, J. Geophys. Res., 118, 1956–1974, 2013.Burtin, A., Hovius, N., McArdell, B. W., Turowski, J. M., and Vergne, J.: Seismic constraints
on dynamic links between geomorphic processes and routing of sediment in a steep
mountain catchment, Earth Surf. Dynam., 2, 21–33, 10.5194/esurf-2-21-2014, 2014.Burtin, A., Hovius, N., and Turowski, J. M.: Seismic monitoring of torrential and
fluvial processes, Earth Surf. Dynam., 4, 285–307, 10.5194/esurf-4-285-2016, 2016.Cadman, J. D. and Goodman, R. E.: Landslide Noise, Science, 158, 1182–1184,
10.1126/science.158.3805.1182, 1967.
Chen, Z., Stewart, R., Bland, H., and Thurston, J.: Microseismic activity and
location at Turtle Mountain, Alberta, 16, 18, Consortium for
Research in Elastic Wave Exploration Seismology, CREWES, University of
Calgary, Canada, 2005.Chouet, B.: Resonance of a fluid-driven crack: Radiation properties and
implications for the source of long-period events and harmonic tremor,
J. Geophys. Res., 93, 4375–4400,
10.1029/JB093iB05p04375, 1988.
Colombero, C., Comina, C., Vinciguerra, S., and Benson, P. M.:
Microseismicity of an unstable rock mass: From field monitoring to laboratory
testing, J. Geophys. Res.-Solid Earth, 123, 1673–1693,
https://doi.org/10.1002/2017JB014612, 2018.
Coviello, V., Arattano, M., and Turconi, L.: Detecting torrential processes
from a distance with a seismic monitoring network, Nat. Hazards, 78,
2055–2080, 2015.
Cruden, D. M. and Varnes, D. J.: Landslide types and processes, in: Landslide
investigation and mitigation, edited by: Turner, A. K. and Schuster, R. L.,
Transportation Research Board Special Report, 36–71, National Academy of
Sciences, Washington DC, 1996.
Curilem, G., Vergara, J., Fuentealba, G., Acuña, G., and Chacón, M.:
Classification of seismic signals at Villarrica volcano (Chile) using neural
networks and genetic algorithms, J. Volc. Geotherm. Res., 180, 1–8, 2009.Dammeier, F., Moore, J. R., Haslinger, F., and Loew, S.: Characterization of
alpine rockslides using statistical analysis of seismic signals, J. Geophys. Res., 116, F04024, 10.1029/2011JF002037, 2011.
Dammeier, F., Moore, J. R., Hammer, C., Haslinger, F., and Loew, S.: Automatic
detection of alpine rockslides in continuous seismic data using Hidden Markov
Models, J. Geophys. Res., 121, 351–371, 2016.Deichmann, N., Ansorge, J., Scherbaum, F., Aschwanden, A., Bernard, F., and
Gudmundsson, G. H.: Evidence for deep icequakes in an Alpine glacier, Ann. Glaciol., 31, 85–90, 10.3189/172756400781820462, 2000.Deparis, J., Jongmans, D., Cotton, F., Baillet, L., Thouvenot, F., and Hantz,
D.: Analysis of Rock-Fall and Rock-Fall Avalanche Seismograms in the French
Alps, Bull. Seis. Soc. Am., 98, 1781–1796,
10.1785/0120070082, 2008.Derode, B., Guglielmi, Y., De Barros, L., and Cappa, F.: Seismic responses to
fluid pressure perturbations in a slipping fault, Geophys. Res.
Lett., 42, 3197–3203, 10.1002/2015GL063671, 2015.Dietze, M., Mohadjer, S., Turowski, J. M., Ehlers, T. A., and Hovius, N.:
Seismic monitoring of small alpine rockfalls – validity, precision and limitations,
Earth Surf. Dynam., 5, 653–668, 10.5194/esurf-5-653-2017, 2017a.Dietze, M., Turowski, J. M., Cook, K. L., and Hovius, N.: Spatiotemporal patterns, triggers
and anatomies of seismically detected rockfalls, Earth Surf. Dynam., 5, 757–779,
10.5194/esurf-5-757-2017, 2017b.Dixon, N., Hill, R., and Kavanagh, J.: Acoustic emission monitoring of slope
instability: development of an active waveguide system, Proceedings of the
Institution of Civil Engineers – Geotech. Eng., 156, 83–95,
10.1680/geng.2003.156.2.83, 2003.Dixon, N., Spriggs, M. P., Smith, A., Meldrum, P., and Haslam, E.:
Quantification of reactivated landslide behaviour using acoustic emission
monitoring, Landslides, 12, 549–560, 10.1007/s10346-014-0491-z, 2015.Dixon, N., Smith, A., Flint, J. A., Khanna, R., Clark, B., and Andjelkovic, M.:
An acoustic emission landslide early warning system for communities in
low-income and middle-income countries, Landslides,
10.1007/s10346-018-0977-1, 2018.
Doi, I., Matsuura, S., Shibasaki, T., and Osawa, H.: in: Seismic measurements
in a mudstone landslide area, 10th Asian Regional Conference of IAEG, 2015.Ekström, G. and Stark, C. P.: Simple Scaling of Catastrophic Landslide
Dynamics, Science, 339, 1416–1419, 10.1126/science.1232887, 2013.Ekström, G. and Stark, C. P.: Simple scaling of catastrophic landslide
dynamics, Science, 339, 1416–1419, 10.1126/science.1232887, 2013.Fäh, D. and Koch, K.: Discrimination between Earthquakes and Chemical
Explosions by Multivariate Statistical Analysis: A Case Study for
Switzerland, Bull. Seis. Soc. Am., 92,
1795–1805, 10.1785/0120010166, 2002.Farin, M., Mangeney, A., and Roche, O.: Fundamental changes of granular flow
dynamics, deposition and erosion processes at high slope angles: insights
from laboratory experiments, J. Geophys. Res., 119,
10.1002/2013JF002750, 2014.Galgaro, A., Tecca, P. R., Genevois, R., and Deganutti, A. M.: Acoustic module of the Acquabona
(Italy) debris flow monitoring system, Nat. Hazards Earth Syst. Sci., 5, 211–215,
10.5194/nhess-5-211-2005, 2005.
Godano, M., Regnier, M., Deschamps, A., Bardainne, T., and Gaucher, E.: Focal
mechanisms from sparse observations by nonlinear inversion of amplitudes:
method and tests on synthetic and real data, Bull. Seis. Soc. Am., 99, 2243–2264, 2009.Gomberg, J., Bodin, P., Savage, W., and Jackson, M. E.: Landslide faults and
tectonic faults, analogs?: The Slumgullion earthflow, Colorado, Geology,
23, 41–44, 10.1130/0091-7613(1995)023<0041:LFATFA>2.3.CO;2, 1995.Gomberg, J., Schulz, W., Bodin, P., and Kean, J.: Seismic and geodetic
signatures of fault slip at the Slumgullion Landslide Natural Laboratory,
J. Geophys. Res., 116,
10.1029/2011JB008304, 2011.
Hammer, C., Beyreuther, M., and Ohrnberger, M.: A Seismic-Event Spotting
System for Volcano Fast-Response Systems, Bull. Seis. Soc. Am., 102, 948–960, 2012.Hammer, C., Ohrnberger, M., and Fäh, D.: Classifying seismic waveforms from
scratch: a case study in the alpine environment, Geophys. J. Int., 192, 425–439, 10.1093/gji/ggs036, 2013.Harba, P. and Pilecki, Z.: Assessment of time–spatial changes of shear wave
velocities of flysch formation prone to mass movements by seismic
interferometry with the use of ambient noise, Landslides, 14, 1225–1233,
10.1007/s10346-016-0779-2, 2017.Harp, E. L., Reid, M. E., Godt, J. W., DeGraff, J. V., and Gallegos, A. J.:
Ferguson rock slide buries California State Highway near Yosemite National
Park, Landslides, 5, 331–337, 10.1007/s10346-008-0120-9, 2008.Hartzell, S., Leeds, A. L., and Jibson, R. W.: Seismic Response of Soft
Deposits due to Landslide: The Mission Peak, California, LandslideSeismic
Response of Soft Deposits due to Landslide: The Mission Peak, California,
Landslide, B. Seismo. Soc. Am., 107, 2008–2020, 10.1785/0120170033, 2017.Hawthorne, J. and Ampuero, J.-P.: A phase coherence approach to identifying
co-located earthquakes and tremor, Geophys. J. Int., 209,
623–642, 10.1093/gji/ggx012, 2017.Helmstetter, A. and Garambois, S.: Seismic monitoring of Séchilienne
rockslide (French Alps): Analysis of seismic signals and their correlation
with rainfalls, J. Geophys. Res., 115,
f03016, 10.1029/2009JF001532, 2010.
Helmstetter, A. and Janex, G.: Ecoute sismique et acoustique du mouvement de
terrain de Séchilienne (Massif de Belledonne), Métrologie en
Milieu Extrême, Collection EDYTEM, 2017.
Helmstetter, A., Ménard, G., Hantz, D., Lacroix, P., Thouvenot, F., and
Grasso, J.-R.: Etude multidisciplinaire d'un effondrement dans la
carrière de ciment de Saint-Martin-le-Vinoux, Journées Aléas
Gravitaires, Strasbourg, France, 2011.Helmstetter, A., Moreau, L., Nicolas, B., Comon, P., and Gay, M.:
Intermediate-depth icequakes and harmonic tremor in an Alpine glacier
(Glacier d'Argentière, France): Evidence for hydraulic
fracturing?, J. Geophys. Res., 120, 402–416,
10.1002/2014JF003289, 2015a.Helmstetter, A., Nicolas, B., Comon, P., and Gay, M.: Basal icequakes recorded
beneath an Alpine glacier (Glacier d'Argentière, Mont Blanc, France):
Evidence for stick-slip motion?, J. Geophys. Res., 120, 379–401, 10.1002/2014JF003288, 2015b.
Helmstetter, A., Larose, E., Baillet, L., and Mayoraz, R.: Repeating quakes
detected at Gugla rock-glacier and Alestch rockslide (Valais), 2017a.
Helmstetter, A., Larose, E., Baillet, L., and Mayoraz, R.: Repeating quakes
detected at Gugla rock-glacier and Alestch rockslide (Valais),
Enviroseis, From process to signal – advancing environmental seismology,
Ohlstadt, Germany, 2017b.Hencher, S. R.: Preferential flow paths through soil and rock and their
association with landslides, Hydrol. Proc., 24, 1610–1630,
10.1002/hyp.7721, 2010.Hibert, C., Mangeney, A., Grandjean, G., and Shapiro, N. M.: Slope
instabilities in Dolomieu crater, Réunion Island: From seismic signals to
rockfall characteristics, J. Geophys. Res., 116, F04032,
10.1029/2011JF002038, 2011.Hibert, C., Mangeney, A., Grandjean, G., Baillard, C., Rivet, D., Shapiro,
N. M., Satriano, C., Maggi, A., Boissier, P., Ferrazzini, V., and Crawford,
W.: Automated identification, location, and volume estimation of rockfalls
at Piton de la Fournaise volcano, J. Geophys. Res., 119, 1082–1105, 10.1002/2013JF002970, 2014a.Hibert, C., Malet, J.-P., Bourrier, F., Provost, F., Berger, F., Bornemann, P., Tardif, P.,
and Mermin, E.: Single-block rockfall dynamics inferred from seismic signal analysis, Earth
Surf. Dynam., 5, 283–292, 10.5194/esurf-5-283-2017, 2017a.Hibert, C., Mangeney, A., Grandjean, G., Peltier, A., DiMuro, A., Shapiro,
N. M., Ferrazzini, V., Boissier, P., Durand, V., and Kowalski, P.:
Spatio-temporal evolution of rockfall activity from 2007 to 2011 at the Piton
de la Fournaise volcano inferred from seismic data, J. Vol. Geotherm. Res., 333–334, 36–52,
10.1016/j.jvolgeores.2017.01.007, 2017b.Hibert, C., Provost, F., Malet, J.-P., Maggi, A., Stumpf, A., and Ferrazzini,
V.: Automatic identification of rockfalls and volcano-tectonic earthquakes at
the Piton de la Fournaise volcano using a Random Forest algorithm,
J. Vol. Geotherm. Res., 340, 130–142,
10.1016/j.jvolgeores.2017.04.015, 2017c.Huang, C.-J., Yin, H.-Y., Chen, C.-Y., Yeh, C.-H., and Wang, C.-L.: Ground
vibrations produced by rock motions and debris flows, J. Geophys. Res., 112, f02014, 10.1029/2005JF000437, 2007.Hungr, O., Evans, S. G., Bovis, M. J., and Hutchinson, J. N.: A review of the
classification of landslides of the flow type, Environ. Eng. Geosci., 7, 221, 10.2113/gseegeosci.7.3.221, 2001.Hungr, O., Leroueil, S., and Picarelli, L.: The Varnes classification of
landslide types, an update, Landslides, 11, 167–194,
10.1007/s10346-013-0436-y, 2014.Hürlimann, M., Abancó, C., Moya, J., and Vilajosana, I.: Results and
experiences gathered at the Rebaixader debris-flow monitoring site, Central
Pyrenees, Spain, Landslides, 11, 939–953, 10.1007/s10346-013-0452-y,
2014.
Itakura, Y., Fujii, N., and Sawada, T.: Basic characteristics of ground
vibration sensors for the detection of debris flow, Physics and Chemistry of
the Earth, Part B: Hydrology, Oceans Atmos., 25, 717–720, 2000.
Joswig, M.: Nanoseismic monitoring fills the gap between microseismic network
and passive seismic, First Break, 26, 117–124, 2008.
Kanamori, H., Given, J. W., and Lay, T.: Analysis of seismic body waves
excited by the Mount St. Helens eruption of May 18, 1980, J. Geophys. Res., 89, 1856–1866, 1984.Kean, J. W., Coe, J. A., Coviello, V., Smith, J. B., McCoy, S. W., and
Arattano, M.: Estimating rates of debris flow entrainment from ground
vibrations, Geophys. Res. Lett., 42, 6365–6372,
10.1002/2015GL064811.Kishimura, K. and Izumi, K.: Seismic Signals Induced by Snow Avalanche Flow,
Nat. Hazards, 15, 89–100, 10.1023/A:1007934815584, 1997.
Kogelnig, A., Hübl, J., Suriñach, E., Vilajosana, I., and McArdell,
B. W.: Infrasound produced by debris flow: propagation and frequency content
evolution, Nat. Hazards, 70, 1713–1733, 2014.Kumagai, H., Palacios, P., Maeda, T., Castillo, D. B., and Nakano, M.: Seismic
tracking of lahars using tremor signals, J. Volcanol. Geoth. Res., 183, 112–121,
10.1016/j.jvolgeores.2009.03.010, 2009.
Lacroix, P. and Helmstetter, A.: Location of seismic signals associated with
microearthquakes and rockfalls on the Séchilienne landslide, French
Alps, Bull. Seis. Soc. Am., 101, 341–353, 2011.Lacroix, P., Grasso, J.-R., Roulle, J., Giraud, G., Goetz, D., Morin, S., and
Helmstetter, A.: Monitoring of snow avalanches using a seismic array:
Location, speed estimation, and relationships to meteorological variables, J.
Geophys. Res., 117, F01034, 10.1029/2011JF002106, 2012.Langer, H., Falsaperla, S., Powell, T., and Thompson, G.: Automatic
classification and a-posteriori analysis of seismic event identification at
Soufrière Hills volcano, Montserrat, J. Vol. Geotherm. Res., 153, 1–10,
10.1016/j.jvolgeores.2005.08.012, 2006.Larose, E., Carrière, S., Voisin, C., Bottelin, P., Baillet, L.,
Guéguen, P., Walter, F., Jongmans, D., Guillier, B., Garambois, S.,
Gimbert, F., and Massey, C.: Environmental seismology: What can we learn on
earth surface processes with ambient noise?, J. Appl. Geophys., 116, 62–74, 10.1016/j.jappgeo.2015.02.001, 2015.
Larose, E., Bontemps, N., Lacroix, P., and Maquerhua, E. T.: Landslide
monitoring in southern Peru: SEG Geoscientists Without
Borders® project, in: 2017 SEG International Exposition and
Annual Meeting, Soc. Expl. Geophys., 2017.Lawrence, W. S. and Williams, T. R.: Seismic Signals Associated with
Avalanches, J. Glaciol., 17, 521–526,
10.3189/S0022143000013782, 1976.
Lavigne, F., Thouret, J.-C., Voight, B., Young, K., LaHusen, R., Marso, J.,
Suwa, H., Sumaryono, A., Sayudi, D., and Dejean, M.: Instrumental lahar
monitoring at Merapi Volcano, Central Java, Indonesia, J. Volcanol. Geoth. Res., 100, 457–478, 2000.Lenti, L., Martino, S., Paciello, A., Prestininzi, A., and Rivellino, S.:
Seismometric Monitoring of Hypogeous Failures Due to Slope Deformations, 309–315, Springer Berlin Heidelberg, Berlin, Heidelberg,
10.1007/978-3-642-31445-2_40, 2013.
Le Roy, G., Amitrano, D., and Helmstetter, A.: Multidisciplinary study of
rockfalls in Chartreuse massif, in: Enviroseis, From process to signal – advancing environmental
seismology, 6–9 June 2017, Ohlstadt, Germany, 2017.
Le Roy, G., Helmstetter, A., Amitrano, D., Guyoton, F., and Roux-Mallouf,
R. L.: Seismic characterization of rock falls from detachment to
propagation, in: EGU General Assembly, Vienna, Austria, 2018.Leprettre, B. J. P., Navarre, J.-P., and Taillefer, A.: First results from a
pre-operational system for automatic detection and recognition of seismic
signals associated with avalanches, J. Glaciol., 42, 352–363,
10.3189/S0022143000004202, 1996.Lévy, C., Baillet, L., Jongmans, D., Mourot, P., and Hantz, D.: Dynamic
response of the Chamousset rock column (Western Alps, France), J. Geophys. Res., 115, F04043, 10.1029/2009JF001606, 2010.Levy, C., Jongmans, D., and Baillet, L.: Analysis of seismic signals recorded
on a prone-to-fall rock column (Vercors massif, French Alps), Geophys. J. Int., 186, 296–310, 10.1111/j.1365-246X.2011.05046.x,
2011.Levy, C., Mangeney, A., Bonilla, F., Hibert, C., Calder, E. S., and Smith,
P. J.: Friction weakening in granular flows deduced from seismic records at
the Soufrière Hills Volcano, Montserrat, J. Geophys. Res., 120, 7536–7557, 10.1002/2015JB012151, 2015.Lipovsky, B. P. and Dunham, E. M.: Tremor during ice-stream stick slip,
The Cryosphere, 10, 385–399, 10.5194/tc-10-385-2016, 2016.
Lockner, D., Byerlee, J., Kuksenko, V., Ponomarev, A., and Sidorin, A.:
Quasi-static fault growth and shear fracture energy in granite, Nature, 350,
39–42, https://doi.org/10.1038/350039a0, 1991.Lomax, A., Virieux, J., Volant, P., and Berge-Thierry, C.: Probabilistic
Earthquake Location in 3D and Layered Models, 101–134, Springer
Netherlands, 10.1007/978-94-015-9536-0_5, 2000.Lomax, A., Michelini, A., and Curtis, A.: Earthquake Location, Direct,
Global-Search Methods, 1–33, Springer New York,
10.1007/978-3-642-27737-5_150-2, 2009.
Lotti, A., Saccorotti, G., Fiaschi, A., Matassoni, L., Gigli, G., Pazzi, V.,
and Casagli, N.: Seismic Monitoring of a Rockslide: The Torgiovannetto
Quarry (Central Apennines, Italy), in: Engineering Geology for Society and
Territory – Volume 2, edited by: Lollino, G., Giordan, D., Crosta, G. B.,
Corominas, J., Azzam, R., Wasowski, J., and Sciarra, N., 1537–1540,
Springer International Publishing, Cham, 2015.
Lube, G., Cronin, S. J., Manville, V., Procter, J. N., Cole, S. E., and
Freundt, A.: Energy growth in laharic mass flows, Geology, 40, 475–478, https://doi.org/10.1130/G32818.1, 2012.Maggi, A., Ferrazzini, V., Hibert, C., Beauducel, F., Boissier, P., and
Amemoutou, A.: Implementation of a multistation approach for automated event
classification at Piton de la Fournaise volcano, Seismol. Res. Lett., 88, 878–891, 10.1785/0220160189,
2017.Mainsant, G., Larose, E., Brönnimann, C., Jongmans, D., Michoud, C., and
Jaboyedoff, M.: Ambient seismic noise monitoring of a clay landslide: Toward
failure prediction, J. Geophys. Res., 117,
f01030, 10.1029/2011JF002159, 2012a.Mainsant, G., Jongmans, D., Chambon, G., Larose, E., and Baillet, L.:
Shear-wave velocity as an indicator for rheological changes in clay
materials: Lessons from laboratory experiments, Geophys. Res. Lett., 39, l19301, 10.1029/2012GL053159, 2012b.
Manconi, A. and Coviello, V.: Evaluation of the Raspberry Shakes seismometers
to monitor rock fall activity in alpine environments, in: EGU General
Assembly, Vienna, Austria, 2018.
Marcial, S., Melosantos, A. A., Hadley, K. C., LaHusen, R. G., and Marso,
J. N.: Instrumental lahar monitoring at Mount Pinatubo, Fire and mud:
eruptions and lahars of Mount Pinatubo, Philippines, edited by: Newhall, C.
G. and Punongbayan, R. S., Washington Press, Seattle, 1015–1022, 1996.McCann, D. and Forster, A.: Reconnaissance geophysical methods in landslide
investigations, Eng. Geol., 29, 59–78,
10.1016/0013-7952(90)90082-C, 1990.Michlmayr, G., Cohen, D., and Or, D.: Sources and characteristics of acoustic
emissions from mechanically stressed geologic granular media – A review,
Earth-Sci. Rev., 112, 97–114, 10.1016/j.earscirev.2012.02.009, 2012.Michlmayr, G., Chalari, A., Clarke, A., and Or, D.: Fiber-optic high-resolution
acoustic emission (AE) monitoring of slope failure, Landslides, 14,
1139–1146, 10.1007/s10346-016-0776-5, 2017.Mikesell, T. D., van Wijk, K., Haney, M. M., Bradford, J. H., Marshall, H. P.,
and Harper, J. T.: Monitoring glacier surface seismicity in time and space
using Rayleigh waves, J. Geophys. Res., 117, f02020,
10.1029/2011JF002259, 2012.
Navratil, O., Liébault, F., Bellot, H., Theule, J., Travaglini, E.,
Ravanat, X., Ousset, F., Laigle, D., Segel, V., and Fiquet, M.:
High-frequency monitoring of debris flows in the French Alps, in: Proceedings
of 12th interpraevent congress, Grenoble, 281–291, 2012.Neuberg, J., Luckett, R., Baptie, B., and Olsen, K.: Models of tremor and
low-frequency earthquake swarms on Montserrat, J. Vol. Geotherm. Res., 101, 83–104,
10.1016/S0377-0273(00)00169-4, 2000.Norman, E. C., Rosser, N. J., Brain, M. J., Petley, D. N., and Lim, M.: Coastal
cliff-top ground motions as proxies for environmental processes, J. Geophys. Res.-Ocean, 118, 6807–6823, 10.1002/2013JC008963,
2013.Occhiena, C., Coviello, V., Arattano, M., Chiarle, M., Morra di Cella, U., Pirulli, M.,
Pogliotti, P., and Scavia, C.: Analysis of microseismic signals and temperature recordings
for rock slope stability investigations in high mountain areas, Nat. Hazards Earth Syst. Sci.,
12, 2283–2298, 10.5194/nhess-12-2283-2012, 2012.Palis, E., Lebourg, T., Tric, E., Malet, J.-P., and Vidal, M.: Long-term
monitoring of a large deep-seated landslide (La Clapiere, South-East French
Alps): initial study, Landslides, 14, 155–170,
10.1007/s10346-016-0705-7, 2017.Paul Winberry, J., Anandakrishnan, S., Wiens, D. A., and Alley, R. B.:
Nucleation and seismic tremor associated with the glacial earthquakes of
Whillans Ice Stream, Antarctica, Geophys. Res. Lett., 40, 312–315,
10.1002/grl.50130, 2013.Pierson, T. C.: Flow characteristics of large eruption-triggered debris flows
at snow-clad volcanoes: constraints for debris-flow models, J. Vol. Geotherm. Res., 66, 283–294,
10.1016/0377-0273(94)00070-W, 1995.Podolskiy, E. A. and Walter, F.: Cryoseismology, Rev. Geophys., 54,
708–758, 10.1002/2016RG000526, 2016RG000526, 2016.Poli, P.: Creep and slip: Seismic precursors to the Nuugaatsiaq landslide
(Greenland), Geophys. Res. Lett., 44, 8832–8836,
10.1002/2017GL075039, 2017.Pratt, M. J., Winberry, J. P., Wiens, D. A., Anandakrishnan, S., and Alley,
R. B.: Seismic and geodetic evidence for grounding-line control of Whillans
Ice Stream stick-slip events, J. Geophys. Res., 119, 333–348, 10.1002/2013JF002842, 2014.Provost, F., Hibert, C., and Malet, J.-P.: Automatic classification of
endogenous landslide seismicity using the Random Forest supervised
classifier, Geophys. Res. Lett., 44, 113–120,
10.1002/2016GL070709, 2017a.
Provost, F., Malet, J.-P., Hibert, C., and Vergne, J.: Significance and
interest of dense seismic arrays for understanding the mechanics of clayey
landslides: a test case of 150 nodes at Super-Sauze landslide, in: EGU
General Assembly Conference Abstracts, 19, 14097, 2017b.
Provost, F., Malet, J.-P., Gance, J., Helmstetter, A., and Doubre, C.:
Automatic approach for increasing the location accuracy of slow-moving
landslide endogenous seismicity: the APOLoc method, Geophys. J. Int., 215, 1455–1473, https://doi.org/10.1093/gji/ggy330, 2018.Pérez-Guillén, C., Sovilla, B., Suriñach, E., Tapia, M., and Köhler, A.:
Deducing avalanche size and flow regimes from seismic measurements, Cold
Reg. Sci. Techn., 121, 25–41, 10.1016/j.coldregions.2015.10.004, 2016.RESIF/OMIV: RESIF – Réseau Sismologique et géodésique Français /
OMIV- French Multidisciplinary Observatory of Versant Instabilities,
10.15778/RESIF.MT, 2015.Richards, K. S. and Reddy, K. R.: Critical appraisal of piping phenomena in
earth dams, Bull. Eng. Geol. Environ., 66,
381–402, 10.1007/s10064-007-0095-0, 2007.Roeoesli, C., Helmstetter, A., Walter, F., and Kissling, E.: Meltwater
influences on deep stick-slip icequakes near the base of the Greenland Ice
Sheet, J. Geophys. Res., 121, 223–240,
10.1002/2015JF003601, 2016a.Roth, M., Dietrich, M., Blikra, L. H., and Lecomte, I.: Seismic Monitoring of
the Unstable Rock Slope Site at Ȧaknes, Norway, 184–192,
10.4133/1.2923645, 2008.Rouse, C., Styles, P., and Wilson, S.: Microseismic emissions from
flowslide-type movements in South Wales, Eng. Geol., 31, 91–110,
10.1016/0013-7952(91)90059-T, 1991.Ruano, A., Madureira, G., Barros, O., Khosravani, H., Ruano, M., and Ferreira,
P.: Seismic detection using support vector machines, Neurocomputing, 135,
273–283, 10.1016/j.neucom.2013.12.020, 2014.Sabot, F., Naaim, M., Granada, F., Suriñach, E., Planet, P., and Furdada,
G.: Study of avalanche dynamics by seismic methods, image-processing
techniques and numerical models, Ann. Glaciol., 26, 319–323,
10.3189/1998AoG26-1-319-323, 1998.Schimmel, A. and Hübl, J.: Automatic detection of debris flows and debris
floods based on a combination of infrasound and seismic signals, Landslides,
13, 1181–1196, 10.1007/s10346-015-0640-z, 2016.Schneider, D., Bartelt, P., Caplan-Auerbach, J., Christen, M., Huggel, C., and
McArdell, B. W.: Insights into rock-ice avalanche dynamics by combined
analysis of seismic recordings and a numerical avalanche model, J. Geophys. Res., 115, F04026, 10.1029/2010JF001734, 2010.
Scholz, C. H.: Earthquakes and friction laws, Nature, 391, 37–42,
https://doi.org/10.1038/34097, 1998.Schöpa, A., Chao, W.-A., Lipovsky, B. P., Hovius, N., White, R. S., Green, R. G.,
and Turowski, J. M.: Dynamics of the Askja caldera July 2014 landslide, Iceland,
from seismic signal analysis: precursor, motion and aftermath, Earth Surf.
Dynam., 6, 467–485, 10.5194/esurf-6-467-2018, 2018.Senfaute, G., Duperret, A., and Lawrence, J. A.: Micro-seismic precursory cracks
prior to rock-fall on coastal chalk cliffs: a case study at Mesnil-Val, Normandie,
NW France, Nat. Hazards Earth Syst. Sci., 9, 1625–1641, 10.5194/nhess-9-1625-2009, 2009.
Shelly, D. R., Beroza, G. C., Ide, S., and Nakamula, S.: Low-frequency
earthquakes in Shikoku, Japan, and their relationship to episodic tremor and
slip, Nature, 442, 7099, 188, 2006.Smith, A., Dixon, N., Meldrum, P., Haslam, E., and Chambers, J.: Acoustic
emission monitoring of a soil slope: Comparisons with continuous deformation
measurements, Gétech. Lett., 4, 255–261,
10.1680/geolett.14.00053, 2014.Smith, A., Dixon, N., and Fowmes, G. J.: Early detection of first-time slope
failures using acoustic emission measurements: large-scale physical
modelling, Géotechnique, 67, 138–152, 10.1680/jgeot.15.P.200,
2017.Spillmann, T., Maurer, H., Green, A. G., Heincke, B., Willenberg, H., and
Husen, S.: Microseismic investigation of an unstable mountain slope in the
Swiss Alps, J. Geophys. Res., 112, b07301,
10.1029/2006JB004723, 2007.Stumpf, A., Malet, J.-P., Kerle, N., Niethammer, U., and Rothmund, S.:
Image-based mapping of surface fissures for the investigation of landslide
dynamics, Geomorphology, 186, 12–27,
10.1016/j.geomorph.2012.12.010, 2013.Suriñach, E., Furdada, G., Sabot, F., Biesca, B., and Vilaplana, J. M.: On
the characterization of seismic signals generated by snow avalanches for
monitoring purposes, Ann. Glaciol., 32, 268–274,
10.3189/172756401781819634, 2001.Suriñach, E., Vilajosana, I., Khazaradze, G., Biescas, B., Furdada, G.,
and Vilaplana, J. M.: Seismic detection and characterization of landslides and
other mass movements, Nat. Hazards Earth Syst. Sci., 5, 791–798,
10.5194/nhess-5-791-2005, 2005.
Surin, E., Sabot, F., Furdada, G., Vilaplana, J., et al.: Study of seismic
signals of artificially released snow avalanches for monitoring purposes,
Physics and Chemistry of the Earth, Part B: Hydrology, Ocean Atmos., 25, 721–727, 2000.
Suwa, H., Okano, K., and Kanno, T.: Behavior of debris flows monitored on test
slopes of Kamikamihorizawa Creek, Mount Yakedake, Japan, Int. J. Ero. Contr. Eng., 2, 33–45, 2009.Tang, C., Li, L., Xu, N., and Ma, K.: Microseismic monitoring and numerical
simulation on the stability of high-steep rock slopes in hydropower
engineering, J. Rock Mech. Geotech. Eng., 7, 493–508, 10.1016/j.jrmge.2015.06.010, 2015.Tary, J.-B., Van der Baan, M., and Eaton, D. W.: Interpretation of resonance
frequencies recorded during hydraulic fracturing treatments, J. Geophys. Res., 119, 1295–1315,
10.1002/2013JB010904, 2014a.Tary, J.-B., Van der Baan, M., Sutherland, B., and Eaton, D. W.:
Characteristics of fluid-induced resonances observed during microseismic
monitoring, J. Geophys. Res., 119, 8207–8222,
10.1002/2014JB011263, 2014b.Thomas, A. M., Beroza, G. C., and Shelly, D. R.: Constraints on the source
parameters of low-frequency earthquakes on the San Andreas Fault, Geophys. Res. Lett., 43, 1464–1471, 10.1002/2015GL067173, 2016.
Tonnellier, A., Helmstetter, A., Malet, J.-P., Schmittbuhl, J., Corsini, A.,
and Joswig, M.: Seismic monitoring of soft-rock landslides: the Super-Sauze
and Valoria case studies, Geophys. J. Int., 193,
1515–1536, 2013.Vázquez, R., Suriñach, E., Capra, L., Arámbula-Mendoza, R., and
Reyes-Dávila, G.: Seismic characterisation of lahars at Volcán de
Colima, Mexico, Bull. Vol., 78, 10.1007/s00445-016-1004-9, 2016.Vilajosana, I., Suriñach, E., Abellán, A., Khazaradze, G., Garcia, D., and Llosa, J.:
Rockfall induced seismic signals: case study in Montserrat, Catalonia, Nat. Hazards Earth
Syst. Sci., 8, 805–812, 10.5194/nhess-8-805-2008, 2008.
Voisin, C., Garambois, S., Larose, E., and Massey, C.: Seismic noise
correlations and monitoring of the Utiku (New-Zealand) landslide, in: EGU
General Assembly Conference Abstracts, Vol. 15 of EGU General Assembly
Conference Abstracts, EGU2013-5406-1, 2013.Vouillamoz, N., Rothmund, S., and Joswig, M.: Characterizing the complexity of microseismic
signals at slow-moving clay-rich debris slides: the Super-Sauze (southeastern France)
and Pechgraben (Upper Austria) case studies, Earth Surf. Dynam., 6, 525–550,
10.5194/esurf-6-525-2018, 2018.
Walter, M., Walser, M., and Joswig, M.: Mapping Rainfall-Triggered Slidequakes
and Seismic Landslide-Volume Estimation at Heumoes SlopeAll rights reserved.
No part of this periodical may be reproduced or transmitted in any form or by
any means, electronic or mechanical, including photocopying, recording, or
any information storage and retrieval system, without permission in writing
from the publisher, Vadose Zone J., 10, 487–495, 2011.Walter, F., Dalban Canassy, P., Husen, S., and Clinton, J. F.: Deep icequakes:
What happens at the base of Alpine glaciers?, J. Geophys. Res., 118, 1720–1728, 10.1002/jgrf.20124,
2013a.Walter, F., Burtin, A., McArdell, B. W., Hovius, N., Weder, B., and Turowski, J. M.:
Testing seismic amplitude source location for fast debris-flow detection at Illgraben,
Switzerland, Nat. Hazards Earth Syst. Sci., 17, 939–955, 10.5194/nhess-17-939-2017, 2017.Walter, M., Arnhardt, C., and Joswig, M.: Seismic monitoring of rockfalls,
slide quakes, and fissure development at the Super-Sauze mudslide, French
Alps, Eng. Geol., 128, 12–22,
10.1016/j.enggeo.2011.11.002, 2012.Walter, M., Gomberg, J., Schulz, W., Bodin, P., and Joswig, M.: Slidequake
Generation versus Viscous Creep at Softrock-landslides: Synopsis of Three
Different Scenarios at Slumgullion Landslide, Heumoes Slope, and
Super-Sauze Mudslide, J. Environ. Eng. Geophys., 18, 269–280, 10.2113/JEEG18.4.269, 2013b.Winberry, J. P., Anandakrishnan, S., Wiens, D. A., Alley, R. B., and
Christianson, K.: Dynamics of stick-slip motion, Whillans Ice Stream,
Antarctica, Earth Planet. Sci. Lett., 305, 283–289,
10.1016/j.epsl.2011.02.052, 2011.Worni, R., Huggel, C., Stoffel, M., and Pulgarín, B.: Challenges of
modeling current very large lahars at Nevado del Huila Volcano, Colombia,
B. Volcanol., 74, 309–324, 10.1007/s00445-011-0522-8, 2012.Yamada, M., Mangeney, A., Matsushi, Y., and Moretti, L.: Estimation of dynamic
friction of the Akatani landslide from seismic waveform inversion and
numerical simulation, Geophys. J. Int., 206, 1479–1486,
10.1093/gji/ggw216, 2016a.
Yamada, M., Mori, J., and Matsushi, Y.: Possible stick-slip behavior before the
Rausu landslide inferred from repeating seismic events, Geophys. Res. Lett., 43, 9038–9044, 10.1002/2016GL069288, 2016b.
Yin, H., Huang, C., Chen, C., Fang, Y., Lee, B., and Chou, T.: The present
development of debris flow monitoring technology in Taiwan'a case study
presentation, in: 5th International Conference on Debris-Flow Hazards
Mitigation: Mechanics, Prediction and Assessment, edited by: Genevois, R.,
Hamilton, D. L., and Prestininzi, A., Casa Editrice Universita La Sapienza,
Roma, 623–631, 2011.Zigone, D., Voisin, C., Larose, E., Renard, F., and Campillo, M.: Slip
acceleration generates seismic tremor like signals in friction experiments,
Geophys. Res. Lett., 38, l01315, 10.1029/2010GL045603, 2011.
Zimmer, V. L. and Sitar, N.: Detection and location of rock falls using seismic
and infrasound sensors, Eng. Geol., 193, 49–60, 2015.
Zobin, V. M., Plascencia, I., Reyes, G., and Navarro, C.: The characteristics
of seismic signals produced by lahars and pyroclastic flows: Volcán de
Colima, México, J. Volcanol. Geoth. Res., 179,
157–167, 2009.