Eleonore Stutzmann

Primary and secondary microseisms are generated by ocean wave interactions, with the latter occasionally split into short‐ and long‐period bands in some regions. The short‐period secondary microseism (SPSM) sources have traditionally been associated with nearby coastal segments, suggesting a more uniform distribution across shorelines. Here, we challenge this assumption by combining polarization analysis, source mechanism modeling and attenuation assessment in the Mediterranean Sea. This region offers an optimal setting, as we reveal that the primary and long‐period secondary microseisms originate in the North Atlantic Ocean, whereas the SPSM arise within the Mediterranean Sea, allowing these sources to be clearly isolated. We find that SPSM sources are well‐constrained, spatially localized and highly dynamic. Our results reveal that bathymetry strongly controls the spatial distribution of these sources.

Volcanic activity encompasses a wide range of seismogenic phenomena occurring from the deep magmatic conduit to the surface of volcanic flanks. Volcanic tremor, long period (LP), and very long period (VLP) seismic signals are commonly associated with magma and fluid movement within the conduit, whereas sliding mass and density currents along the flanks typically produce minute-long, cigar-shaped seismic traces.Characterising these phenomena through seismic analysis requires high measurement accuracy over a broad frequency range, together with high spatial and temporal resolution. Meeting these requirements in complex volcanic environments can be particularly challenging because the deployment and the maintenance of dense seismic networks involves considerable logistical effort. Distributed Acoustic Sensing (DAS) offers the opportunity to bridge the gap between sparse seismic networks and denser arrays, enabling continuous strain measurements along fibre-optic cables at comparatively low operational costs.Here, we investigate several different volcanic processes at Stromboli volcano (Italy) through DAS observations acquired along a 6 km fibre-optic cable integrated within a permanent multiparameter monitoring network comprising broadband seismometers, thermal and visible cameras, and infrasonic pressure sensors. The fibre was deployed on the volcanic flanks between 2020 and 2023 and interrogated during several month-long campaigns using a Febus A1-R. The dataset includes signals generated by ordinary Strombolian explosions, major explosions, lava flows, partial crater collapses and pyroclastic density currents (PDCs), which were analysed using different analytical approaches.Array-processing techniques in the 1–5 Hz frequency range were used to track the source of volcanic tremor, explosions, and PDCs with DAS strain-rate signals. Tremor and explosion signals are consistently located near the crater area, whereas PDCs propagate along the volcanic flanks. Moreover, by combining visible imagery with seismic energy recorded by DAS and inertial seismometers, we estimate the flow velocities and volumes of the PDCs and derive empirical, volume-dependent friction angles that provide insight into flow dynamics.Additionally, we exploit the distributed nature of DAS measurements to reconstruct the axisymmetric principal strain axes of VLP strain signals (between 0.04–0.2 Hz) associated with each explosion. The VLP strain signals recorded along the fibre nicely fit a deformation point-source (Mogi) located beneath the active craters, with an estimated volumetric change of ~30 m³.Our results demonstrate the capability of DAS measurements to characterise the dynamics of volcanic processes and to resolve the VLP strain distribution with enhanced spatial resolution. Overall, these findings highlight the significant potential of DAS as an innovative tool for analysing and monitoring a wide range of volcanic phenomena across different spatial and temporal scales.

The modern French railway network is equipped with optical fibers dedicated to telecommunication purposes, among which some remain unused. These so-called dark fibers can be exploited using Distributed Acoustic Sensing technology (DAS) to provide an effective tool for rapid assessment and long-term monitoring of site conditions along railway tracks. We present a methodology applied to a 19 km long DAS array operating under normal railway traffic conditions, highlighting the capability to perform continuous spatial analysis at kilometer scale with measurements every 4.9 meters. Despite the limited coupling associated with the on-conduit installation, corresponding to the standard operational conditions without any modification to the existing infrastructure, time windows selected before and after train passages allow the extraction of the resonance frequencies at each DAS channel, overcoming the low signal-to-noise ratio of the installation setup. Variations of resonance frequencies along the railway reflect changes in near surface soil conditions, related either to shear wave velocity or to variation in impedance contrast depth, with rapid spatial variation observed in karstic areas over only a few tens of meters. The novelty of this work lies in the use of resonance frequencies as a stable and repeatable site parameter derived from DAS data on a large scale. While this information does not quantify site amplification, it provides direct information on the frequency ranges that may be preferentially amplified. This makes them well suited for long term monitoring and for tracking temporal or spatial changes in site conditions under linear infrastructures, and for supporting future strategies to manage infrastructure evolution and time dependent variability. Seasonal variations of these resonance frequencies have been observed, further supporting this method as an effective tool for continuous site monitoring.

The deployment of dense seismic arrays on volcanoes has increased significantly over the past decades, enabling more precise monitoring of volcanic activity. While short-period sensors are commonly used, Distributed Acoustic Sensing (DAS) represents a promising complementary technology, providing high spatial resolution and remote location of the interrogator. Accurate monitoring requires a robust understanding of seismic wave propagation, particularly within the shallow subsurface beneath the sensors. On volcanic edifices, the distribution of eruptive deposits along the flanks can be highly heterogeneous, leading to strong lateral variations in physical properties that can significantly affect seismic records.We use ambient noise cross-correlation to investigate the subsurface velocity structure beneath a 4 km-long DAS cable installed on Stromboli volcano, Italy. The analysis relies on two months of continuous strain-rate data recorded on this persistently active volcano, enabling a passive approach. Empirical Green’s Functions (EGFs) are retrieved using Phase Cross-Correlation and time-scale Phase-Weighted Stacking methods, and are validated through comparison with EGFs derived from collocated short-period seismic sensors. Local phase velocities are then estimated along the fiber and inverted to obtain a 2D S-wave velocity model. The results reveal two distinct regions along the profile, which correlate with variations in topography and volcanic deposits.

The deployment of telecommunication fiber-optic cables along railway tracks facilitates the recording of strain-rate wavefields for subsurface characterization. However, the broadband seismic noise generated by the train itself often masks site-specific signals, complicating the extraction of local geological characteristics. To address this, recent research has utilized the ambient wavefield—recorded both immediately before (prelude) and after (coda) train passage—to compute cross-correlograms and derive phase velocity dispersion curves along the fiber.In this study, we conduct a systematic comparative analysis of train-induced prelude and coda wavefields to assess the consistency of synthesized dispersion curves. The dataset consists of 24 h of DAS recordings acquired over a 1,250 m fiber, with a gauge length of 20 m, channel spacing of 5 m, and a sampling rate of 500 Hz. Homogeneous segments along the fiber are first identified by examining resonant site frequencies in the 1–20 Hz range using autocorrelation functions. Cross-correlograms are subsequently computed between a selected virtual source and neighboring channels within these homogeneous sections to reconstruct phase velocity dispersion curves.Preliminary results indicate that comparing resonance frequency maps for the prelude and coda of the same train reveals channel-specific discrepancies of 2–3 Hz. Furthermore, the lateral stability of these resonance frequencies fluctuates depending on the wavefield phase analyzed. Even in segments identified as laterally homogeneous, the resulting correlograms vary significantly; wave propagation is generally clearer in the prelude, whereas the coda more frequently excites higher modes. Interestingly, the fundamental modes on the dispersion curves remain consistent across both phases, even when the underlying correlograms differ substantially. These variations are highly dependent on the train type. For instance, trains with higher RMS amplitude values produce more laterally stable resonance maps—particularly within the coda—though a discrepancy in absolute values between the prelude and coda persists. These trains also yield more consistent correlograms and dispersion curves along the fiber. These findings highlight the sensitivity of the results to the specific source signal and suggest that combining the prelude and coda data from multiple trains could significantly improve the stability and reliability of the seismic imaging of the sites along the fiber.

There is a growing interest in applying ambient noise processing techniques to fiber-optic arrays, which are now a mainstream tool in seismology. Traditionally applied to geophone arrays, noise-based interferometry methods have been widely used for subsurface imaging and monitoring over the last two decades. In this study, we retrieve surface wave phase velocities by cross-correlation of DAS-recorded ambient noise data to monitor the subsurface of a quiet brackish marsh site of the Loire estuary. Seasonal recordings were obtained from a DAS array consisting of multiple linear and a spiral cable layout, capturing the ambient seismic wavefield mainly influenced by natural forcing, for example, tidal activity in the river and streams (below 15 Hz), but also by anthropogenic sources. We use time-frequency weighted Phase Cross-Correlation (PCC) technique, which in addition to being efficient, reduces sensitivity to amplitude variations and emphasizes phase coherence. We observe diurnal and seasonal variation in ground water level and in noise characteristics, like amplitude and directionality. Such temporal variations provide an opportunity to monitor both (a) the changes in the subsurface medium itself, and (b) the impact of noise characteristics on surface wave retrieval. We also observe a pronounced lateral contrast of surface wave phase velocity across the marsh site, highlighting the extent of spatial variability of the subsurface in complex natural environments.

Distributed Acoustic Sensing (DAS) is an emerging technology that transforms fiber-optic cables into dense arrays of vibration sensors, offering significant potential for subsurface exploration in urban environments. DAS enables the recording of broadband ground vibrations generated by human activity, including both high-frequency seismic wavefields (>1 Hz) and low-frequency quasi-static deformations (

Quantifying iceberg calving is important for understanding ice mass loss of the Greenland Ice Sheet and its subsequent impact on sea level rise, and for refining calving laws that currently represent a major source of uncertainty in global climate models. These calving events, particularly those involving capsizing icebergs, exert time-varying forces on the edge of marine-terminating glaciers that produce distinct seismic signals known as glacial earthquakes.By processing twelve years of continuous seismic data and employing a Random Forest classifier to distinguish these glacial earthquakes from tectonic events, we generated a comprehensive catalogue of 6263 previously undocumented glacial earthquakes occurring between 2013 and 2024. The detected events are located along the Greenland coast with surface wave magnitudes ranging from MSW 4.1 to 5.4. They cluster at nine major calving glaciers, though the vast majority of activity is concentrated at Sermeq Kujalleq (Jakobshavn Isbræ) and Helheim Gletsjer.To investigate the driving mechanisms behind these events, we analysed the correlation between calving activity and various environmental variables, including glacier velocity, air temperature, sea ice fraction, sea surface temperature, and wind speed. We train a second Random Forest model to predict monthly calving events and evaluate the relative importance of these environmental features, while applying statistical analyses to investigate correlations on a yearly basis where data points are limited. Our results indicate that the relationship between calving and the environment is highly complex and site-specific, as no single variable serves as a universal driver for all glaciers.This complexity is further highlighted by scale-dependent correlations between calving events and environmental variables. For instance, while the glacier velocity shows a strong correlation with cumulative yearly calving at Sermeq Kujalleq, its importance diminishes on a monthly scale. Conversely, Helheim Gletsjer exhibits no clear yearly correlation with the glacier velocity, highlighting the site-specific nature of calving dynamics. We will present the spatio-temporal evolution of the detected events and discuss how these diverse environmental correlations quantify the varying sensitivity of individual glaciers to environmental forcing across different temporal scales.

Primary and secondary microseisms are both generated by ocean wave interactions. It has been observed that, in some regions, the secondary microseism splits into long- and short-period bands. Short-period secondary microseism (SPSM) sources have traditionally been associated with nearby coastal segments, local storms or wave heights, and relatively constant offshore distances. However, the parameters controlling this split, as well as the precise spatial distribution of SPSM sources, remain poorly understood.Motivated by the observation of a frequency bimodality in seismic noise cross-correlations in NE Iberia, we find evidence that the secondary microseism split occurs more broadly across the Mediterranean region. Using a combination of complementary and independent approaches, such as polarization analysis, source mechanism modeling, and attenuation assessment, we investigate possible variations in the location of source regions within the microseismic band. As a result, distinct source regions are consistently identified across all the methodologies employed, providing insight into the microseismic sources.We further demonstrate that SPSM sources are well constrained, spatially localized, and highly dynamic. In this study, we reveal key variables directly related to the generation of SPSM sources, enhancing our understanding of the parameters controlling their spatial distribution. These findings are likely applicable to other regions with similar conditions.

The manner by which glaciers evacuate the products of erosion is poorly known, especially for the coarser-sized sediments produced by plucking and quarrying, that is the bedload fraction. It has traditionally been assumed that bedload export from Alpine glaciers is relatively efficient. However, continuous, seismic measurements of bedload transport at glacier portals question this assumption as do the results of numerical modelling experiments. There are a number of possible hypotheses to explain such inefficiency, including the structure and geometry of subglacial channels, periodic variation in discharge due to diurnal melt cycles and near-ice margin effects. The problem remains, however, that there are no published datasets on subglacial bedload transport to investigate the effects, not surprising because of the challenges associated with measuring it. The aim of this research is to harness environmental seismology to quantify subglacial bedload transport rates for the first time using on-ice measurements.In the first part of the project, we traced the location and development of two subglacial channels of Glacier d’Otemma, Valais, Switzerland. In this study, we investigate the discharge and sediment traveling in our identified channels. With the help of thirty seismic sensors on the glacier surface and at the glacial terminus, we record the seismic activity of Glacier d’Otemma during its melt season in 2024. We trace the path the sediment takes underneath the glacier with seismic beamforming techniques and relate it to the previously identified shape of the subglacial drainage system. A line of seismic sensors along the glacier was able to record waves of bedload as they travel underneath the ice. With the help of physical models, the spectral information of the ground motion data is translated into sediment transport and discharge quantities. The project contributes to reveal the interaction of sediment and the characteristics of subglacial drainage system in alpine glaciers, giving further insights into erosion mechanisms at play.