Slope instabilities in Dolomieu crater, Réunion Island: From seismic signals to rockfall characteristics

International audienceThe seismic signals of hundreds of rockfalls within Dolomieu crater, Piton de la Fournaise volcano, Réunion Island, have been analyzed to investigate a possible link between physical rockfall-generating processes and associated seismic signal features. Moreover, indirect observation of rockfalls via the seismic signals they generate can provide useful data for studying volcanoes and the temporal variations of their structure. An increase in the number of rockfall events and their volumes might be an indicator of structural weakness and deformation of the volcano associated with potential eruptive activity. The study focuses on a 10 month period following the 6 April 2007 crater floor collapse within Dolomieu crater, from May 2007 to February 2008. For granular flows a scaling law is revealed between seismic energy and signal duration. A semiempirical approach based on both analytical analysis and numerical simulation of these flows shows that a similar scaling law exists between the difference of potential energy computed for an event and its propagation times and also emphasizes the effect of local topography on this scaling law. Simulated and observed data were compared to evaluate the proportion of potential energy dissipated in the form of seismic waves and confirm a direct link between the seismic energy and potential energy of a given granular flow. The mean ratio of seismic to potential energy is of the order of 10−4, comparable to the range of values observed in previous studies. A simple method based on these ratios is proposed to estimate the volumes of rockfalls from their seismic signal. Observed seismic energy and the frequency of rockfalls decreased at the beginning of the studied period and reached a stable level in July, thus suggesting a postcollapse relaxation time of Dolomieu crater structure of about 2 months from seismic signal analysis, which is confirmed by deformation data. The total rockfall volume over the study period is estimated to be 1.85 Mm3

Event recognition in marine seismological data using Random Forest machine learning classifier

Automatic detection of seismic events in ocean bottom seismometer (OBS) data is difficult due to elevated levels of noise compared to the recordings from land. Popular deep-learning approaches that work well with earthquakes recorded on land perform poorly in a marine setting. Their adaptation to OBS data requires catalogues containing hundreds of thousands of labelled event examples that currently do not exist, especially for signals different than earthquakes. Therefore, the usual routine involves standard amplitude-based detection methods and manual processing to obtain events of interest. We present here the first attempt to utilize a Random Forest supervised machine learning classifier on marine seismological data to automate catalogue screening and event recognition among different signals [i.e. earthquakes, short duration events (SDE) and marine noise sources]. The detection approach uses the short-term average/long-term average method, enhanced by a kurtosis-based picker for a more precise recognition of the onset of events. The subsequent machine learning method uses a previously published set of signal features (waveform-, frequency- and spectrum-based), applied successfully in recognition of different classes of events in land seismological data. Our workflow uses a small subset of manually selected signals for the initial training procedure and we then iteratively evaluate and refine the model using subsequent OBS stations within one single deployment in the eastern Fram Strait, between Greenland and Svalbard. We find that the used set of features is well suited for the discrimination of different classes of events during the training step. During the manual verification of the automatic detection results, we find that the produced catalogue of earthquakes contains a large number of noise examples, but almost all events of interest are properly captured. By providing increasingly larger sets of noise examples we see an improvement in the quality of the obtained catalogues. Our final model reaches an average accuracy of 87 per cent in recognition between the classes, comparable to classification results for data from land. We find that, from the used set of features, the most important in separating the different classes of events are related to the kurtosis of the envelope of the signal in different frequencies, the frequency with the highest energy and overall signal duration. We illustrate the implementation of the approach by using the temporal and spatial distribution of SDEs as a case study. We used recordings from six OBSs deployed between 2019 and 2020 off the west-Svalbard coast to investigate the potential link of SDEs to fluid dynamics and discuss the robustness of the approach by analysing SDE intensity, periodicity and distance to seepage sites in relation to other published studies on SDEs

Slopes instability of the Dolomieu crater in La Reunion from seismological observations and numerical modeling.

International audienceThe intensity of volcanic activity and seasonal rains associated with the instability of the natural slopes has caused many rockfalls in the Dolomieu crater located on top of the volcano Piton de la Fournaise in La Reunion Island. These phenomena, that involve individual blocks up to larger volumes, are expected to be related to the volcanic activity. The unpredictable nature and destructive power of gravitational flows make in-situ measurements extremely difficult. The seismic signal generated by these slope instabilities provides thus a unique tool to trace back these events and retrieve their characteristics (volume, duration, localization, . . . ). The permanent seismic network set on Le Piton de la Fournaise volcano is particularly well suited to the study of seismic signals related to gravitational collapse and of their relation to volcanic activity. Using this network and the new seismic broadband stations recently installed, the seismic signals generated by slope instabilities have been acquired and analyzed. In a first step, signal processing techniques have been developed to distinguish the seismic signal generated by rockfalls from that generated by other seismological events that affect the Piton de la Fournaise Volcano. A localization method has been developed based on inversion of waves arrival time. We focus on the 2006-2007 period, during which the crater has undergone a major collapse. This event has considerably destabilized the Dolomieu crater edges, providing a good opportunity to study the evolution in time of the rockfall activity. Analysis of the seismic signal and simple scaling laws for granular flows made it possible to derive interesting relations between the energy of the seismic waves and the characteristics of rockfalls. The role of the local topography in these relations has been investigated using numerical modeling of dry granular flows and the Digital Elevation Model of the Dolomieu crater constructed by photogrammetric techniques. Good agreement is found between the scaling laws obtained theoretically and those derived from seismic observation providing insight into the effect of the source parameters on the generated seismic signal. The detection methods and the scaling laws developed here provide useful tools for monitoring of rockfall activity, in particular in relation with the volcanic activity. These works were conducted within UNDERVOLC project

Rockfall trajectory reconstruction: a flexible method utilizing video footage and high-resolution terrain models

Many examples of rockfall simulation software provide great flexibility to the user at the expense of a hardly achievable parameter unification. With sensitive site-dependent parameters that are hardly generalizable from the literature and case studies, the user must properly calibrate simulations for the desired site by performing back-calculation analyses. Thus, rockfall trajectory reconstruction methods are needed. For that purpose, a computer-assisted videogrammetric 3D trajectory reconstruction method (CAVR) built on earlier approaches is proposed. Rockfall impacts are visually identified and timed from video footage and are manually transposed on detailed high-resolution 3D terrain models that act as the spatial reference. This shift in reference removes the dependency on steady and precisely positioned cameras, ensuring that the CAVR method can be used for reconstructing trajectories from witnessed previous records with nonoptimal video footage. For validation, the method is applied to reconstruct some trajectories from a rockfall experiment performed by the WSL Institute for Snow and Avalanche Research SLF. The results are compared to previous ones from the SLF and share many similarities. Indeed, the translational energies, bounce heights, rotational energies, and impact positions against a flexible barrier compare well with those from the SLF. The comparison shows that the presented cost-effective and flexible CAVR method can reproduce proper 3D rockfall trajectories from experiments or real rockfall events.</p

Towards a standard typology of endogenous landslide seismic sources

The objective of this work is to propose a standard classification of seismic signals generated by gravitational processes and detected at close distances (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.Peer ReviewedPostprint (published version

Rock fall photogrammetric monitoring in the active crater of Piton de la Fournaise volcano, la Reunion Island

International audienceThe collapse of the active crater at Piton de la Fournaise volcano, La Reunion Island, 5th April 2007, offers a rare opportunity to observe frequent rock fall and granular landslides, and test new monitoring techniques. Events concern volumes ranging from single blocks to more massive cliff collapse. The purpose of the presentation is two fold: first, we present a comparison between a Digital Terrain Model (DTM) obtained prior to crater collapse and a DTM extracted from aerial photographs shot in October 2010 (before the eruptive crisis of November 2009 and January 2010). This provides an assessment of morphological changes at the scale of the crater. The second purpose is to describe slope instabilities on the south-western flank of the crater observed since October 2009. These ground-based observations were obtained from a pair of photogrammetric stations deployed along the northern and eastern edges of the crater. These works were conducted within UNDERVOLC project. With this monitoring system we mapped zones affected by rockfalls (departure and accumulation areas) and propose a first estimate of volumes of lava produced by the eruption affecting the inside of the crater since January 2

A rockslide-generated tsunami in a Greenland fjord rang Earth for 9 days

Climate change is increasingly predisposing polar regions to large landslides. Tsunamigenic landslides have occurred recently in Greenland (Kalaallit Nunaat), but none have been reported from the eastern fjords. In September 2023, we detected the start of a 9-day-long, global 10.88-millihertz (92-second) monochromatic very-long-period (VLP) seismic signal, originating from East Greenland. In this study, we demonstrate how this event started with a glacial thinning–induced rock-ice avalanche of 25 × 106 cubic meters plunging into Dickson Fjord, triggering a 200-meter-high tsunami. Simulations show that the tsunami stabilized into a 7-meter-high long-duration seiche with a frequency (11.45 millihertz) and slow amplitude decay that were nearly identical to the seismic signal. An oscillating, fjord-transverse single force with a maximum amplitude of 5 × 1011 newtons reproduced the seismic amplitudes and their radiation pattern relative to the fjord, demonstrating how a seiche directly caused the 9-day-long seismic signal. Our findings highlight how climate change is causing cascading, hazardous feedbacks between the cryosphere, hydrosphere, and lithosphere.acceptedVersio

Enhanced glacial earthquake catalogues with supervised machine learning for more comprehensive analysis

International audiencePolar regions and Greenland in particular are highly sensitive to global warming. Impacts on Greenland's glaciers may be observed through the increasing number of calving events. However, a direct assessment of the calving activity is limited due to the remoteness of polar regions and the cloudy weather which makes impossible a recurrent observation through satellite imagery. To tackle this issue, we exploit the seismological network deployed in Greenland which actively records seismic signals associated with calving events, hereinafter referred to as glacial earthquakes. These seismic signals present a broad frequency range and a wide diversity of waveform which make them difficult to discriminate from tectonic events as well as anthropogenic and natural noises. In this study, we start from two catalogues of known events, one for glacial earthquake events which occurred between 1993 and 2013 and one for earthquakes which occurred in the same time period, and we implement a detection algorithm based on the STA/LTA method to extract signals' events from continuous data. Then, we train and test a machine learning processing chain based on the Random Forest algorithm which allows us to automatically associate the events respectively with calving and tectonic activity, with a certain probability. Finally, we investigate 844 selected days spanning time of continuous data from the Greenland regional seismic network which results in a new, more exhaustive, catalogue of glacial earthquakes expanded of 1633 newly detected glacial events. Moreover, we extensively discuss the choice of the features used to describe glacial earthquakes, in particular the 39 new features created in this study which have drastically improved our results with 7 of the 10 best features being in the added set. The perspective of further expansion of the glacial earthquake catalogue applying the processing chain discussed in this paper on different time spans highlights how combining seismology and machine learning can increase our understanding of the spatio-temporal evolution of calving activity in remote regions