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Understanding the dynamics of slope instabilities is critical to mitigate the associated hazards, but their direct observation is often difficult due to their remote locations and their spontaneous nature. Seismology allows us to get unique information on these events, including on their dynamics. However, the link between the properties of these events (mass and kinematics) and the seismic signals generated is still poorly understood. We conducted a controlled rockfall experiment in the Riou Bourdoux torrent (southern French Alps) to try to better decipher those links. We deployed a dense seismic network and inferred the dynamics of the block from the reconstruction of the 3D trajectory from terrestrial and airborne high-resolution stereophotogrammetry. We propose a new approach based on machine learning to predict the mass and the velocity of each block. Our results show that we can predict those quantities with average errors of approximately 10 % for the velocity and 25 % for the mass. These accuracies are as good as or better than those obtained by other approaches, but our approach has the advantage in that it does not require the source to be localised, nor does it require a high-resolution velocity model or a strong assumption on the seismic wave attenuation model. Finally, the machine learning approach allows us to explore more widely the correlations between the features of the seismic signal generated by the rockfalls and their physical properties, and it might eventually lead to better constraints on the physical models in the future. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of slope instabilities is critical to mitigate the associated hazards but their direct observation is often difficult due to their remote locations and their spontaneous nature. Seismology allows to get unique information on these events, including on their dynamics. However, the link between the properties of these events (mass and kinematics) and the seismic signals generated are still poorly understood. we conducted a controlled rockfall experiment in the Riou-Bourdoux torrent (South French Alps) to try to better decipher those links. We deployed a dense seismic network and inferred the dynamics of the block from the reconstruction of the 3D trajectory from terrestrial and airborne high-resolution stereo-photogrammetry. We propose a new approach based on machine learning to predict the mass and the velocity of each block. Our results show that we can predict those quantities with average errors of approximately 10 % for the velocity and 25 % for the mass. These accuracies are as good as or better than those obtained by other approaches, but our approach has the advantage of not requiring to localize the source and an a priori knowledge of the environment, nor of making a strong assumption on the seismic wave attenuation model. Finally, the machine learning approach allows us to explore more widely the correlations between the features of the seismic signal generated by the rockfalls and their physical properties, and might eventually lead to better constrain the physical models in the future. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of two large rock toppling/sliding events which occurred in January 2014 and February 2019 at the Cliets unstable slope (Savoie, French Alps). To understand the mechanism involved and its control by external forcings, a multi-technique analysis approach is used combining geological observations, meteorological data analysis, topographic measurements and simple physical modeling. The pre-failure stage of the events is more particularly analyzed. No direct relationships are found between triggering factors and surface motion though a kinematics analysis highlights the transition toppling-sliding. It showed that, at first order, this transition occurred 4 years before the first failure of 2014, while it happened 2 months before the second failure of 2019. From this date, the environment is considered like a block sliding on an inclined plane. By applying a frictional model (Helmstetter et al. in Journal of Geophysical Research: Solid Earth 109(B2), 2004), we illustrated that the two events belong to an unstable velocity-weakening sliding regime. The time to failure (Voight in Science 243(4888):200–203, 1989) is forecasted with the model, and the results are consistent with the observations. They confirm that the gravitational factor is predominant over the triggering factors for the two events. [ABSTRACT FROM AUTHOR]

Fluid substitution processes have been investigated in the laboratory on 14 carbonate and siliciclastic reservoir rock analogues through spontaneous imbibition experiments on vertical cylindrical specimens with simultaneous ultrasonic monitoring and imaging. The motivation of our study was to identify the seismic attributes of fluid substitution in reservoir rocks and to link them to physical processes. It is shown that (i) the P wave velocity either decreases or increases when the capillary front reaches the Fresnel clearance zone, (ii) the P wave amplitude is systematically impacted earlier than the velocity is, (iii) this precursory amplitude decrease occurs when the imbibition front is located outside of the Fresnel zone, and (iv) the relative variation of the P wave amplitude is always much larger than that of the P wave velocity. These results suggest that moisture diffuses into the pore space ahead of the water front. This postulate is further supported by a quantitative analysis of the time evolution of the observed P wave amplitudes. In a sense, P wave amplitude acts as a precursor of the arrival of the capillary front. This phenomenon is used to estimate the effective diffusivity of moisture in the tested rocks. The effective moisture diffusivity estimated from the ultrasonic data is strongly correlated with permeability: a power law with exponent 0.96 predicts permeability from ultrasonic monitoring within a factor 3 without noticeable bias. When the effective diffusivity is high, moisture diffusion affects ultrasonic P wave attributes even before the imbibition starts and impacts the P wave reflectivity as evidenced by the variations recorded in the waveform coda. [ABSTRACT FROM AUTHOR]

Capillary rise experiments (spontaneous imbibition tests) were conducted in the laboratory with ultrasonic and X-ray monitoring on the Sherwood sandstone and the Majella grainstone. The aim was to provide a direct comparison between the variation in seismic attributes (amplitude, velocity, spectral content, and energy) and the actual fluid distribution in the rock. Two pairs of ultrasonic P wave sensors located at different heights on a cylindrical rock specimen recorded every 5 s the waveforms when capillary forces make water rise up into the rock from the bottom in contact with a water tank. Simultaneously, computerized tomography scan images of a vertical cross section were also recorded. Two important results were found. (i) The amplitude of the first P wave arrival is impacted by the upward moving fluid front before the P wave velocity is, while the fluid front has not yet reached the sensors level. In contrast, the P wave velocity decreases when the fluid front reaches the Fresnel clearance zone. The spectral analysis of the waveforms shows that the peak frequency amplitude is continuously decreasing without noticeable frequency shift. (ii) A methodology based on the calculation of the analytical signal and instantaneous phase was designed to decompose each waveform into discrete wavelets associated with direct or reflected waves. The energy carried by the wavelets is very sensitive to the fluid substitution process: the coda wavelets related to reflections on the bottom end face of the specimen are impacted as soon as imbibition starts and can be used as a precursor for the arriving fluid. [ABSTRACT FROM AUTHOR]

Passive sensors such as multi-spectral (e.g., Single Lens Reflex, SLR) cameras are increasingly being used for geohazards monitoring (landslides, cliffs affected by rock falls, ice glaciers, and volcano flanks) because of their low cost compared to expensive terrestrial laser scanner (TLS) or radar imaging (GB-InSAR) systems. Indeed, due to the large consumer market, sensor resolution and quality (e.g., gain, dynamic range, and geometry) are increasing rapidly. For gravitational processes, such as landslides, recent research has focused on the development and implementation of image correlation techniques to estimate the spatial shift between at least a pair of images by maximizing a cross-correlation function. A generic and fully automated pipeline is proposed for the processing of long image time series acquired for several site configurations. The system associates modules for (1) the selection of the image sequences, (2) the registration of the image stacks and the correction of the camera movements, and (3) the calculation of the terrain motion using change detection approaches. The system is based on the open-source photogrammetric library MicMac and tailored for the processing of monoscopic images. A sensitivity analysis is conducted to design and test the image processing for two use cases respectively the Chambon landslide (Isère, France) characterized by slow motion (< 10 cm·day−1), and the Pas de l'Ours landslide (Hautes-Alpes, France) characterized by moderate motion (> 50 cm·day−1). Four categories of parameters are tested: the image modality, the image matching parameters, the size of the stable area used in the co-registration stage, and the strategy used to combine the images in the time series. The application of the pipeline on the two use cases provides information about the kinematics and the spatial behavior of the landslides. [ABSTRACT FROM AUTHOR]

Recent studies using satellite data have shown a growing interest in detecting and anticipating landslide failures. However, their value for an actual landslide prediction has shown variable results. Therefore, the use of satellite images for that purpose still requires additional attention. Here, we study the landslide of the Tunnel du Chambon in the French Alps that ruptured in July 2015, generating major impacts on economic activity and infrastructures. To evaluate the contribution of very high-resolution optical satellite images to characterize and potentially anticipate the landslide failure, we conduct here a retro analysis of its evolution. Two time periods are analyzed: September 2012 to September 2014, and May to July 2015. We combine Pléiades optical images analysis and geodetic measurements from in situ topographic monitoring. Satellite images were correlated to detect pre-failure motions, showing 1.4-m of displacement between September 2012 and September 2014. In situ geodetic measures were used to analyze motions during the main activity of the landslide in June and July 2015. Topographic measurements highlight different areas of deformations and two periods of strong activity, related to the last stage of the tertiary creep and to anthropic massive purges of unstable masses. The law of acceleration toward the rupture observed in June and July 2015 over the topographic targets also fits well the satellite observation between 2012 and 2014, showing that the landslide probably already entered into tertiary creep 2.5 years before its failure. [ABSTRACT FROM AUTHOR]