Typology of endogenous seismic sources generated by gravitational slopemovements 

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Topple and fall related sources

On vertical to sub-vertical slopes, mass movement occurs as the topple of rock columns or as the free-fall (and possibly bouncing and rolling) of rocky blocks (Hungr et al., 2014). 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 thousand cubic of meters.

Landslide seismic investigation

Sensors used in landslide monitoring

Body and surface mechanical waves may be generated by the sources described in Section 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 direc-tion. 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 conver-sion 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 (e.g., gun shots, explosions, storms…), 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 the seismic instru-ments 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) seismome-ters and geophones, Broad-Band (BB) seismometers, accelerometers, and AE sensors are commonly installed in the field.
• Broad-Band seismometers are force-balanced sensors with 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 high corner frequency (> 1Hz). 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 frequencies 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 landslide, they are usually used as inclinometers;
• AE sensors can record ground vibrations at very high frequencies (10 kHz-10 MHz) and low am-plitudes. 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 (Michlmayr et al., 2012). 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 landslide to monitor very low magnitude sources at the grain-to-grain interactions (Dixon et al., 2003, Michlmayr et al., 2012, Smith et al., 2017);
• in addition, microphones or infrasound sensors can be useful to detect, locate and classify land-slides seismic signals (Kogelnig et al., 2014, Schimmel and Hübl, 2016, Helmstetter and Janex, 2017). 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 acoustic emissions generated at very high frequen-cies (> 10kHz) and consequently are very sensitive to attenuation. Indeed, attenuation factor Q is estimated to range between 10−2 and 101 dB.cm−1 (Michlmayr et al., 2012). Even with a waveguide, they must be collocated with the cracks or the sliding surfaces observed on the slope (Dixon et al., 2015). BB, 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 sources 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 co-located 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 network and enable 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 is 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 (Spillmann et al., 2007, Helmstetter and Garambois, 2010, Walter et al., 2017b). During field campaigns, maintenance of sensors installed on the unstable slopes is possible and often realized (Gomberg et al., 2011, Walter et al., 2012a, Tonnellier et al., 2013). Therefore, the main challenges for seismic sensor installation at this scale is 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 in the magnitude of completeness (Mc ) of the seismic network. While the geometry of the network (i.e. inter-sensor distances, azimuthal gap) mostly control the accuracy of source locations.
Seismic sensors can be deployed in network of single sensors or network of sensor arrays. The difference between seismic network 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 Podolskiy and Walter (2016). 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 (SNR) 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 sen-sors located on or at close distance of the unstable slopes. The inter-sensors distance and the az-imuthal gap are often controlled by the location of easily accessible or stable portions of the slopes. However, specific geometry can be adopted such as (almost) linear geometry. This is particularly the case for the 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 landslide and it has currently only been realized on hard-rock slopes (e.g. Randa, Spillmann et al. (2007) or Séchilienne, RESIF/OMIV (2015)). This kind of installation are however very valuable to constrain the depth of the sources.

MS processing chains

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One of the current challenge 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 micro-seismicity 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 (SNR) 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 com-mon. 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 (Allen, 1982) applied on the summed energy of the spectrogram (Spillmann et al., 2007, Helmstetter and Garambois, 2010, Tonnellier et al., 2013).
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 ve-locity) 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. Dammeier et al. (2016) 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 classifica-tion with higher success rate and versatility (Provost et al., 2017, Hibert et al., 2017c). New signals are successfully classified in multiple pre-defined classes and changes in the source properties may be detected by change on the uncertainties (Hibert et al., 2017c). It must be noticed that this approach requires a training set with sufficient examples 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 (Allstadt and Malone, 2014, Yamada et al., 2016a, Poli, 2017, Helmstetter et al., 2015a,b, Bièvre et al., 2017, Helmstetter et al., 2017a) in order to detect and classify seismic signals. This method consist in 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; (Anthony et al., 2000, Lomax et al., 2009)) were used in combination to 3D-velocity models for locating impulsive micro-earthquakes occurring at the Randa rockslide (Spillmann et al., 2007). However, a certain number of recorded signals do not exhibit impulsive first arrivals and clear P- and S-waves onsets. For this kind of signal, location methods based on the inter-trace correlation of the surface waves (Lacroix and Helmstetter, 2011) or on the amplitude (Burtin et al., 2016, Walter et al., 2017b) are more suitable and easier to automatize. Other methods such as HypoLine (Joswig, 2008) aim at integrating different strategies (i.e. first arrival picking, inter-trace correlation and beam-forming) to locate accurately the epicenter under the control of an operator. (Provost et al., 2018) developed a method combining Amplitude Source Location (ASL) and inter-trace correlation of the first arrivals in an automatic scheme. This strategy showed accurate location of impulsive events while the error on the epicenter of emergent events is reduced by the use of ASL to constrain the location. Many studies approximate the media attenuation field and/or the ground velocity, or do not take into account the topography, leading to mis-location of the events that prevents for accurate interpretation of certain sources and leads to false alarms (Walter et al., 2017b).

Instrumented sites

In the last two decades, seismic networks have been installed on several unstable slopes worldwide. Table 1.1 synthesizes 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 and flow) accord-ing to the geomorphological typology of Cruden (1996) (Cruden and Varnes, 1996). Studies on snow avalanches (Lawrence and Williams, 1976, Kishimura and Izumi, 1997, Sabot et al., 1998, Suriñach et al., 2001, Biescas et al., 2003) 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 Broad-band (BB) or long-period (LP) seismometers.

Data

Seismic observations from 14 sites are used to propose the typology. The sites are representative of various types of slope movements and lithology (Table 1.1) 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 1.1). The Riou-Bourdoux catchment is the only site where the seis-mic signals were manually triggered as rock blocks were thrown down the cliff and monitored with cameras, LiDAR and seismic sensors (Hibert et al. (2017a)).
The dimension of the unstable slopes range from 60 m × 30 m for the Chamousset cliff to 7 km × 300 m for the St.-Eynard cliff (Table 1.2). The seismic networks are deployed with various geome-try 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 (Table 1.2). The maximal distance to the slope instabilities is 500 m for the St.-Eynard cliff being the largest investigated site of our study.
The seismic network geometry of the majority of sites are distributed seismic network where sen-sors location are regularly installed over the active zone or at 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 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 Hz to 5 Hz and 50 to 100 Hz. Fewer geophones and BB seismometers are installed at the sites. The instrument response is cor-rected for all the dataset. To be consistent with the sensitivity of all the sensors, we do not investigate the data below 1 Hz for BB seismometers and above 100 Hz for SP seismometers and geophones.
The dataset being analyzed is composed of either published seismic events or published cata-logs. The comparison of these events and catalogs enable to compare the signals and to compose the classes of the typology. In the case that no published events or catalogs are available, we analyzed manually the dataset to complete the number of examples for each proposed class (see Section 5 for detailed information).

Table of contents :

Introduction 
Research context: micro-seismicity of Earth surface processes
Research objectives
Research questions
Study sites
Outline of the thesis
Framework
1 Typology of endogenous seismic sources generated by gravitational slopemovements 
1.1 Introduction
1.2 Description of landslide endogenous seismic sources
1.2.1 Fracture related sources
1.2.2 Topple and fall related sources
1.2.3 Mass flow related sources
1.2.4 Fluid related sources
1.3 Landslide seismic investigation
1.3.1 Sensors used in landslide monitoring
1.3.2 Network geometry
1.3.3 MS processing chains
1.3.4 Instrumented sites
1.4 Data
1.5 Methodology
1.6 Seismic description of the signals – typology
1.6.1 Rockfall (RF)
1.6.2 Granular Flow (GF)
1.6.3 Slopequake (SQ)
1.7 Discussion
1.8 Conclusions
2 Classification of seismic signals for the automated creation of seismicity catalogs 
2.1 Introduction
2.2 Data
2.3 Methods
2.4 Results
2.5 Discussion and conclusion
2.6 Appendix: Analysis of the classification uncertainty
3 Location of seismic signals for understanding the landslide deformation pattern 
3.1 Introduction
3.2 Study site: the Super-Sauze landslide
3.3 Data: geophysical structure of the landslide and seismological observations
3.3.1 Geophysical structure of the landslide: P-wave seismicmodel
3.3.2 Micro-seismic monitoring and seismic signal description
3.4 Method: APOLoc, Automatic Picking Optimization and Location
3.4.1 Initial location with signal amplitude analysis
3.4.2 Initial picking of the signal onset
3.4.3 Location procedure
3.4.4 Iterative improvement of the picking time
3.4.5 Error estimation
3.5 Results
3.5.1 Validation of APOLoc
3.5.2 Influence of the velocity model
3.5.3 Comparison to other location approaches
3.6 Discussion
3.6.1 Relevance of APOLoc for landslide endogenous sources
3.6.2 Estimation of the depth of the sources
3.6.3 Estimation of the attenuation coefficient
3.7 Conclusion
3.8 Appendix A
3.8.1 Sensitivity analysis: location accuracy vs. seismic velocity models and picking errors
4 Application: Landslide deformation pattern of clayey landslides 
4.1 Introduction
4.2 Study site
4.3 Observation data
4.3.1 Seismological observations
4.3.2 Surface displacement monitoring
4.3.3 Hydro-meteorological monitoring
4.4 Methodology
4.4.1 Seismic processing
4.5 Results
4.5.1 Quality of the seismicity catalog
4.5.2 Analysis of the temporal pattern of seismic sources, hydro-meteorological forcings and motion
4.5.3 Analysis of the spatio-temporal distribution of seismic sources
4.6 Discussion
4.7 Conclusion
5 Conclusion and perspectives 
5.1 General conclusion
5.2 Perspectives
Bibliography

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