The deformation of slow‐moving landslides developed in clays induces endogenous seismicity of mostly low‐magnitude events (ML<1). Long seismic records and complete catalogs are needed to identify the type of seismic sources and understand their mechanisms. Manual classification of long records is time‐consuming and may be highly subjective. We propose an automatic classification method based on the computation of 71 seismic attributes and the use of a supervised classifier. No attribute was selected a priori in order to create a generic multi‐class classification method applicable to many landslide contexts. The method can be applied directly on the results of a simple detector. We developed the approach on the seismic network of eight sensors of the Super‐Sauze clay‐rich landslide (South French Alps) for the detection of four types of seismic sources. The automatic algorithm retrieves 93% of sensitivity in comparison to a manually interpreted catalog considered as reference.

Abstract. We conducted controlled releases of single blocks within a soft-rock (black marls) gully of the Rioux Bourdoux torrent (French Alps). 28 blocks, with masses ranging from 76 kg to 472 kg, were used for the experiment. An instrumentation combining video cameras and seismometers was deployed along the traveled path. The video cameras allow to reconstruct the trajectories of the blocks and to estimate their velocities at the time of the different impacts with the slope. These data are compared to the recorded seismic signals. As the distance between the falling block and the seismic sensors at the time of each impact is known, we were able to determine the associated seismic signal amplitude corrected from propagation and attenuation effects. We compared the velocity, the loss of potential energy, the kinetic energy and the momentum of the block at each impact to the true amplitude and the energy of the corresponding part of the seismic signal. Our results suggest that the amplitude of the seismic signal scales with the momentum of the block at the impact. We also found a scaling law between the potential energy lost, the kinetic energy and the energy of the seismic radiation generated by the impacts. By combining these scaling laws, we inferred the mass and the velocity before impact of each block directly from the seismic signal. Despite high uncertainties, the values found are close to the true values of the mass and the velocities of the blocks. These relationships also provide new insights to understand the source of high-frequency seismic signals generated by rockfalls.

Abstract. Seismic monitoring of mass movements can significantly help to mitigate the associated hazards; however, the link between event dynamics and the seismic signals generated is not completely understood. To better understand these relationships, we conducted controlled releases of single blocks within a soft-rock (black marls) gully of the Rioux-Bourdoux torrent (French Alps). A total of 28 blocks, with masses ranging from 76 to 472 kg, were used for the experiment. An instrumentation combining video cameras and seismometers was deployed along the travelled path. The video cameras allow reconstructing the trajectories of the blocks and estimating their velocities at the time of the different impacts with the slope. These data are compared to the recorded seismic signals. As the distance between the falling block and the seismic sensors at the time of each impact is known, we were able to determine the associated seismic signal amplitude corrected for propagation and attenuation effects. We compared the velocity, the potential energy lost, the kinetic energy and the momentum of the block at each impact to the true amplitude and the radiated seismic energy. Our results suggest that the amplitude of the seismic signal is correlated to the momentum of the block at the impact. We also found relationships between the potential energy lost, the kinetic energy and the seismic energy radiated by the impacts. Thanks to these relationships, we were able to retrieve the mass and the velocity before impact of each block directly from the seismic signal. Despite high uncertainties, the values found are close to the true values of the masses and the velocities of the blocks. These relationships allow for gaining a better understanding of the physical processes that control the source of high-frequency seismic signals generated by rockfalls.

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Abstract. 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.

SUMMARY Quantifying landslide activity in remote regions is difficult because of the numerous complications that prevent direct landslide observations. However, building exhaustive landslide catalogues is critical to document and assess the impacts of climate change on landslide activity such as increasing precipitation, glacial retreat and permafrost thawing, which are thought to be strong drivers of the destabilization of large parts of the high-latitude/altitude regions of the Earth. In this study, we take advantage of the capability offered by seismological observations to continuously and remotely record landslide occurrences at regional scales. We developed a new automated machine learning processing chain, based on the Random Forest classifier, able to automatically detect and identify landslide seismic signals in continuous seismic records. We processed two decades of continuous seismological observations acquired by the Alaskan seismic networks. This allowed detection of 5087 potential landslides over a period of 22 yr (1995–2017). We observe an increase in the number of landslides for the period and discuss the possible causes.

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Seismological observations offer valuable insights on the stress-strain states, the physical mechanisms and the possible precursory signs of activation of various Earth surface processes (i.e. volcanoes, glaciers and landslides). Comprehensive catalogues of the endogenous landslide seismicity, that is corresponding to seismic sources generated by the unstable slope from either mechanical or hydrological origins, should include the typology and an estimate of the source parameters (location, magnitude) of the event. These advanced catalogues constitute a strong basis to better describe the slope deformation and its time evolution and better understand the controlling factors. Because the number of seismic events in landslide catalogues is generally large, automatic approaches must be considered for defining both the typology and the location of the sources. We propose here a new location approach called Automatic Picking Optimization and Location method-APOLoc for locating landslide endogenous seismic sources from seismological arrays located at close distance. The approach is based on the automatic picking of the P waves arrivals by optimizing the intertrace correlations. The method is tested on calibration shots realized at the Super-Sauze landslide (Southeast French Alps) and compared to other location approaches. By using a realistic velocity model obtained from a seismic tomography campaign, APOLoc reduces the epicentre errors to 23 m (on average) compared to ca. 40 m for the other approaches. APOLoc is then applied for documenting the endogenous seismicity (i.e. slopequakes and rockfalls) at the landslide.