Orals: Wed, 30 Apr | Room G1
Ambient seismic noise is a highly useful signal to monitor various Earth structures and processes over time. Through its excitation at the Earth’s surface by the oceans, wind and other sources, it also provides an observational basis to study the interaction between the solid Earth and its oceans and atmosphere.
While ambient noise has been used extensively for monitoring crust and soil with coda wave passive image interferometry, it remains challenging to localize and quantitatively model the observed changes. Ballistic waves retrieved by ambient noise cross-correlation, which would provide a more straightforward means to interpret observed changes, are only considered an acceptable observable for monitoring under specific circumstances due to the high spatio-temporal variability of ambient noise sources which may bias the measurements.
With the motivation to understand such biases better, we investigate the time-dependent behaviour of attenuation and phase velocity on a regional-scale, 20-year cross-correlation dataset from Switzerland, including stations in the Jura, the Molasse basin and the Alps. Seasonal variations in the composition of the ambient seismic noise field due to the generation of microseismic noise by the ocean have been previously observed. Here, we observe seasonal phase velocity and surface wave attenuation changes, which we further compare to conventional dv/v measurements and time-dependent ambient noise coda-Q measurements. To investigate these changes more quantitatively, we model ambient noise correlations numerically using pre-computed Green’s function libraries for a 3-D Earth model from SPECFEM3D_globe and oceanographically constrained secondary microseism source proxy maps. With these models we aim to determine whether the observed seasonal variations can be explained by ocean microseism source effects.
With this work, we intend to contribute to the quantitative understanding and usage of ambient noise correlations, in particular for the secondary microseism, and ultimately to detailed and interpretable time-dependent monitoring of the crust.
How to cite: Ermert, L., Boschi, L., and Obermann, A.: Zooming out: Seasonal changes shown by the background seismic wavefield in the Swiss Alps and Molasse basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6046, https://doi.org/10.5194/egusphere-egu25-6046, 2025.
Slope instabilities represent a significant hazard to communities and infrastructure across various regions worldwide. Climate change and resultant increasing severe precipitation events potentially raise the risk of failing mass movements. Therefore, a fundamental understanding of slope failure processes is vital for reducing risks. Established remote-sensing and synthetic aperture radar technologies provide valuable data on the surface movement of landslides, but only provide limited information on the instability’s internal state. In contrast, seismic imaging and monitoring techniques can provide critical complementary information on the subsurface structure, physical properties, and time-dependent processes linked to the slope instability dynamics.
The ‘Cuolm da Vi’ slope instability near Sedrun (central Switzerland) represents one of the Alps’ largest active landslides, with an estimated volume of around 150 million m3 and maximum displacement rates of up to 20 cm per year. While the instability currently does not pose an imminent danger, the slope's surface displacement is under constant observation. However, little is known about the Cuolm da Vi internal structure and dynamics at depth. The primary objective of our project is to advance our understanding of the subsurface structures and processes over time, with potential implications for deepening our fundamental knowledge of toppling instabilities in general.
In the summer of 2022, we established an extensive seismic observation network at Cuolm da Vi. This seismic sensor setup included over 1’000 autonomous seismic nodes and a 6-kilometer-long trenched fibre-optic cable. The fibre-optic sensing system was designed for long-term Distributed Acoustic Sensing (DAS) and Distributed Strain Sensing (DSS) observations. This multi-sensor geophysical network provides a unique spatial and temporal resolution for studying the Cuolm da Vi instability, allowing us to observe time-dependent changes across a wide range of spatial and temporal scales. Between summer 2022 and 2024, we gathered a comprehensive data set, including long-term continuous recordings from the nodal, DAS, and DSS systems.
Using a DAS dataset continuously collected from February to July 2023, we developed a wavefield coherence-based workflow to detect and cluster over 7’000 events recorded along the fibre-optic cable. These event clusters of highly similar seismic signals were manually classified into categories such as regional earthquakes, anthropogenic noise, rockfalls, and local seismic events, based on their time- and frequency domain characteristics. The spatial and temporal distribution of several local seismic event clusters exhibits distinct patterns that correlate closely, for example, with the surface displacement measurements. We are currently analysing these clusters of local events and investigating whether spatial links to known tectonic structures can be established, and whether the observed seismic signals allow refining the hazard scenarios and associated early warning strategies.
How to cite: Kiers, T., Grimm, J., Schmelzbach, C., Amann, F., Maurer, H., Edme, P., Poli, P., and Robertsson, J.: Fibre-Optic Monitoring of Seismic Events from an Alpine Slope Instability: Insights into Spatial and Temporal Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13382, https://doi.org/10.5194/egusphere-egu25-13382, 2025.
Enhancing real-time detection of mass movement events is critical for improving early warning systems and reducing risks to individuals and communities. Seismic monitoring offers an effective tool for hazard detection and timely alerts. However, a significant challenge remains in successfully isolating seismic signals associated with mass movements from continuous recordings, often obscured by persistent background noise. Therefore, it is essential to develop robust and reliable algorithms for automatic detection. This study proposes utilizing fractal geometry to quantify signal patterns across various scales, distinguishing seismic signals from background noise based on fractal dimension (FD). The study analyzed seismic data from various mass movement events, including debris flows and rockfalls in the Illgraben catchment of Switzerland and a landslide event from the Askja caldera in Iceland. Two methods were employed to estimate the FD: (i) the variogram estimator and (ii) detrended fluctuation analysis. The results show that noise typically exhibits a higher FD than the seismic signals produced by mass movements. Additionally, this study established distinct FD ranges for each type of mass movement, facilitating their classification. The outcomes also show that landslide seismic landslide signals exhibit high variability, particularly with low (signal-to-noise ratio) SNR and increased distance from the source. The findings highlight the potential for this method to improve seismic event detection in real-time monitoring systems.
How to cite: Jaffar, Q., Zhou, Q., and Tang, H.: How fractal dimension changes during mass movement events in seismic signals?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11390, https://doi.org/10.5194/egusphere-egu25-11390, 2025.
Following an initial landslide in Taiwan, frequent post-failure events, primarily rockfalls with occasional debris flows, pose risks to the safety of road users on a road section next to the bare land slope. To address this issue, a comprehensive warning system has been developed. This system utilizes two seismometers strategically positioned at the crown and toe of the landslide. This configuration effectively captures the physical processes of rockfalls, with the elevation difference between the stations correlating to the time difference in their peak ground velocities. Eleven seismic parameters are employed for initial rockfall detection. Subsequently, a machine learning model, trained on over 100,000 spectrograms, is implemented as a secondary filter to minimize false alarms. Additionally, the system assesses rockfall risk levels by calculating nighttime rockfall activity (from 6 PM to 6 AM) to determine a daily risk level communicated through a traffic light concept. Furthermore, the system integrates local acceleration and rainfall data to address potential coseismic rockfalls and debris flows. This data is transmitted to local electronic boards on both sides of the landslide, displaying the corresponding rockfall/debris flow risk levels with red, yellow, and green lights. Overall, this multi-tiered approach facilitates immediate hazard alerts and proactive risk management. The system provides a robust and adaptable solution for real-time warnings and risk assessments related to rockfalls and debris flows, ultimately enhancing road safety and management efficiency in hazard-prone slopes.
How to cite: Chang, J.-M. and Chao, W.-A.: Development and Implementation of a Real-Time Rockfall Warning System Using Seismic signal and machine learning analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10336, https://doi.org/10.5194/egusphere-egu25-10336, 2025.
Rockfalls pose significant risks, with the potential to cause severe infrastructural damage and fatalities. Among the primary weathering agents - freezing, rainfall, and thermal variations - rainfall's impact on rock weathering remains poorly understood. The mechanical properties - damage and rigidity - are crucial determinants of long-term rock stability (2). This study investigates the effects of rainfall on the sonic velocities and apparent rigidity of a natural rock column.
Ultrasonic testing, a widely used method in structural health monitoring, was employed in situ on a 50-meter-high south-facing limestone cliff overlying the Chauvet Cave in the Ardèche Plateau, SE France. This cliff experiences a range of climatic solicitations, including solar illumination, temperature fluctuations, and rainfall events. Sonic velocity changes, obtained during repeated ultrasonic testing, are indicative of internal stress variations within the rock, driven by environmental factors (thermal-acousto-elasticity, (1)).
We combined ultrasonic testing with resonance frequency measurements to evaluate stress changes at both centimeter and decameter scales of a limestone cliff. While sonic velocities provide insights into local rigidity, resonance frequency measurements reflect changes in the apparent rigidity and fracture dynamics of the rock mass as a whole. Summer rain events caused a drop in resonance frequency, likely due to rock mass contraction and fracture adjustments, while sonic velocity responses varied depending on rainfall intensity. These results suggest an interplay between rainfall and rock properties, potentially involving pore space filling and increased local rigidity from micro-crack closure. This study underscores the value of sonic velocity measurements as a proxy for assessing rock damage and rigidity, emphasizing the need for further quantification to better understand damage evolution and rock stability.
1 ) Guillemot, A., Baillet, L., Larose, E., & Bottelin, P. (2022). Changes in resonance frequency of rock columns due to thermoelastic effects on a daily scale: observations, modelling and insights to improve monitoring systems. Geophysical Journal International, 231(2), 894-906.
2 ) Guillemot, A., Audin, L., Larose, É., Baillet, L., Guéguen, P., Jaillet, S., & Delannoy, J. J. (2024). A comprehensive seismic monitoring of the pillar threatening the world cultural heritage site Chauvet-Pont d'Arc cave, toward rock damage assessment. Earth and Space Science, 11(4), e2023EA003329.
How to cite: Starke, J., Rousseau, R., Baillet, L., Guillemot, A., and Larose, E.: Monitoring Rain-Induced Stress Changes in a Limestone Cliff Using Ultrasonic Testing and Resonance Frequency, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5793, https://doi.org/10.5194/egusphere-egu25-5793, 2025.
The microtremor measurements data have been carried out in 72 locations in and around Varanasi city (Uttar Pradesh), India, to understand the local site conditions and preliminary site effect of the Quaternary sediments of Varanasi in the Indo-Gangetic plain. Estimated outcomes from the horizontal to vertical spectral ratio show the predominant frequency varies from 0.34 Hz to 0.94 Hz, site amplification varies from 1.96 to 3.88, and the vulnerability index (Kg) varies from 4.82 to 39.61, and the low shear wave velocity (approximate ~ 300 m/s) down to the depth of 30 m is evident from the synthesis of the 1-D velocity model for the city, which are classified as class D soil type (NEHRP classification). The primary goal of the current study is to determine the dynamic properties of soil response during a potential earthquake in Varanasi city The obtained results will support the seismic microzonation study by identifying areas prone to liquefaction and aiding in mitigating the risks associated with near-surface site failures during seismic activity in and around Varanasi city.
How to cite: Yadav, A. K. and Sengupta, P.: Microtremor measurements and analysis for local geology condition of Varanasi city based on seismic vulnerability index (kg), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16364, https://doi.org/10.5194/egusphere-egu25-16364, 2025.
The Wind Science and Engineering Test Site in Complex Terrain (WINSENT) in SW Germany is a research facility to study wind energy harvesting in mountainous regions. WINSENT consists of two 0.75 MW wind turbines (WTs) along with a massive instrumentation for scientific measurements, including four 100 m high masts with numerous meteorological sensors at different heights. In addition, there are further open-field measurement systems such as remote sensing devices and a huge amount of instrumentation for nature conservation research, e.g. a bird radar and high-speed cameras for bird monitoring. For studying the soil-structure interaction, each WT foundation has six manholes for geotechnical and geophysical instrumentation such as pressure, displacement and seismic sensors inside the foundations. In addition, there are three shallow boreholes with broadband seismic sensors at 6 m depth and temporary seismic experiments are conducted to measure the propagation properties of seismic waves. These measurements are important for the safe and economic building of WTs and the understanding of the ground motion emissions from wind turbines. The results can later be used to design countermeasures at the source side and refine the determination of protection zones for seismic monitoring stations which can be disturbed from these emissions.
We present the design of the geoscientific research at WINSENT and the first results from seismic refraction measurements for local compressional and shear wave velocity models. The 3-D motion of the WT foundation was recovered: it is composed of a major tilt motion of a few micrometers and a minor wobble-type contribution. We acknowledge financial support by the German Federal Ministry for Economic Affairs and Climate Action, project WINSENTvalid, no. 03EE2028B.
How to cite: Ritter, J., Fesseler, P., Hirsch, J., Pena Pinto, C., Gehring, S., Stutz, H., Rettenmeier, A., and Wigger, M.: Seismological and geotechnical studies at the wind energy test site WINSENT, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12608, https://doi.org/10.5194/egusphere-egu25-12608, 2025.
How do we pinpoint fleeting geomorphic surface events in the planet's remotest corners, where no witness observes and classical methods falter? Processes like landslides, debris flows, avalanches, and rockfalls not only sculpt the Earth's dynamic landscape but also pose significant hazards in remote and populated areas alike. As environmental changes intensify, closing the gap of elusive detection holds profound implications for disaster response, hazard prediction, and geomorphic theory advancement. The difficulty lies in the concealed, stochastic nature of these processes and the challenges of direct observation. Continuous high-resolution seismic sensing offers unique potential to detect and locate geomorphic sources that evade other tools. However, surface processes generate chaotic, site-specific waveforms with rapid, nonlinear energy release, often in noisy, inaccessible settings. Existing, rigid location techniques are ill-equipped for this challenge, failing to match known details of historic geomorphic sources. We introduce a hybrid, nature-inspired seismic event location approach that fuses physical and biological principles to overcome longstanding obstacles in monitoring geomorphic processes.
Our method synergizes deterministic and heuristic elements into a robust, self-adaptive framework. The source location is approximated first by a hybrid of grid search, modified gradient descent, and full waveform inversion. A bio-inspired procedure then iteratively refines this output to near-optimal solutions. Our method autonomously picks arrival times through a multi-layered structure, leveraging dynamic time warping, Bayesian inference, and SNR optimization. Composite misfit metrics from synthetic and observed waveforms guide location estimation in a dynamic solution landscape. This search space self-adjusts to instrument network layout and landscape complexity using Voronoi tessellation, convex hulls, and velocity-refined grids.
The cornerstone of our approach is a biomimicry component, inspired by the adaptive, collaborative behaviors of diverse animal species. We leverage over ten animal behaviors mathematically encoded as optimization agents. Each species epitomizes niche strategies based on their specific strengths. For instance, elephants’ memory and herding guide global searches, fireflies’ light-attraction principles refine locally, and whales’ spiral foraging navigates complex search spaces. Guided by evolutionary mechanisms, predator-prey dynamics, and interagent communication, collective intelligence and a recursive memory are built, and global exploration is seamlessly integrated with local information, balancing far-field searches with near-field precision.
As a benchmark we will use a seismic dataset of 290 geomorphic events, spanning diverse types, scales, and complexities, worldwide. Preliminary results show a 47–200% reduction in location misfit compared to brute-force methods, which mislocate events by 11–20 km. Biomimicry achieves relocation precision of 2.6 km, reducing misfits by up to five orders of magnitude. Improvements are achieved within 150 iterations across varying noise levels, with location standard deviations as low as 1–2 km. Additionally, the method isolates subsurface anomalies, estimates source depth, provides a pathway to track process propagation, and can eventually integrate into real-time early warning systems.
By bridging geomorphology, biology, and seismology, our work elevates the capacity to detect surface processes with accuracy, adaptability, and scalability. Intelligent, resilient, and inspired by nature itself, it lays a foundation for applications ranging from hazard monitoring to planetary exploration.
How to cite: Ursica, S. and Hovius, N.: Nature’s intelligence: Hybrid bio-inspired method yields more accurate seismic locations of geomorphic events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17862, https://doi.org/10.5194/egusphere-egu25-17862, 2025.
Seismic sensors, traditionally used in geophysical studies, are emerging as non-invasive tools for continuous wildlife monitoring by capturing seismic waves generated by animal locomotion. This novel approach opens new possibilities but also presents methodological challenges. In this study, we analyze seismic signals from African savanna species during locomotion and apply machine learning to classify species based on footfall signals. Utilizing the SeisSavanna dataset, which includes over 70,000 labeled seismograms paired with camera trap images, we identify distinct species-specific footfall patterns. Our analysis reveals that local site effects significantly influence signal frequency content. To address this, we trained machine learning models on data from multiple locations, achieving a balanced accuracy of 87% for elephants, giraffes, hyenas, and zebras at distances up to 50 meters, decreasing to 77% at 150 meters due to weaker signals and lower label quality. Importantly, the models generalize well to new stations if similar site conditions are represented in the training data. These findings highlight the potential of seismic monitoring to complement tools like camera traps and acoustic loggers, offering unique insights into wildlife behavior and expanding monitoring capabilities to silent species. To fully realize this potential, further methodological advances and larger datasets are necessary to establish seismic sensors as a robust tool for wildlife conservation.
How to cite: Steinmann, R., Nissen-Meyer, T., Cotton, F., Tilmann, F., and Mortimer, B.: Seismic Footsteps: Harnessing Machine Learning to Decode Wildlife in the African Savanna, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2842, https://doi.org/10.5194/egusphere-egu25-2842, 2025.
This pilot study evaluates the feasibility of recording low-frequency elephant rumbles at the Opel-Zoo near Frankfurt am Main, Germany, using non-invasive co-located seismic and infrasound sensors. Wave-based communication of African elephants (Loxodonta Africana) is well-documented, but its study in anthropogenic zoo environments - particularly with respect to seismic signals - remains limited compared to natural habitats. Over a period of several weeks, we recorded thousands of rumbles that reveal significant temporal variability. Rumble activity exhibits a diurnal correlation with visitor numbers, while many rumbles occur in rapid sequences, suggesting interaction and potential communication among the five elephants housed in the zoo. Additionally, most rumbles are accompanied by ground vibrations, resulting from locomotion or trampling, which are not detectable through sound-only measurements. This underscores the advantages of integrating seismic and acoustic data, revealing that rumbles rarely occur as isolated events. Moreover, this study identifies potential external factors that may trigger increased rumble activity. The collected dataset provides promising insights into temporal elephant activity, helping to deepen our understanding of their behaviour and welfare in zoo environments that are highly influenced by anthropogenic conditions.
How to cite: Limberger, F., Rümpker, G., Spengler, T., and Becker, M.: Monitoring Elephant Activity Patterns in a Zoo Using Co-located Seismic and Infrasound Sensors: A Pilot Study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8895, https://doi.org/10.5194/egusphere-egu25-8895, 2025.
Fin whales, the second-largest animals on Earth, produce some of the most intense vocalizations in the animal kingdom. Monitoring these sounds using ocean-based hydrophones is crucial for studying their distribution and social behaviour, although obtaining real-time data remains challenging. In this study, we explore whether vocalizing near-coastal fin whales can be detected and located widely using onshore seismometers. By analysing publicly available data from existing seismic stations, we show that fin whale songs can be detected with onshore seismometers up to 5.5 km inland across various marine environments worldwide. Through the analysis of seismic wave properties, individual whales can be located and tracked.
Additionally, we demonstrate that citizen science seismometers, like the affordable and widely used ‘Raspberry Shake’ devices, can reliably detect fin whale songs. These instruments, often placed in coastal areas, offer a cost-effective and accessible approach to monitoring coastal fin whale activity in real-time. The discovery that human habitats are ensonified by fin whale song presents an opportunity to increase public engagement with marine life and opens new possibilities for global monitoring. Given that fin whales are threatened by noise pollution, shipping collisions, and entanglement in fishing gear, the use of terrestrial seismometers could help improve early warning systems and enhance datasets on near-coastal whale vocalizations. This study highlights the significant, untapped potential of seismic data for monitoring near-coastal fin whales on a global scale.
How to cite: Möllhoff, M. and Bean, C.: Onshore seismometers detect fin whale songs, unlocking new opportunities for coastal cetacean monitoring and public engagement, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4452, https://doi.org/10.5194/egusphere-egu25-4452, 2025.
The retreat of Greenland’s glaciers is accelerating due to climate change, driven not only by rising temperatures but also by processes such as iceberg calving. These events contribute significantly to the Greenland Ice Sheet mass loss, a critical factor in global sea level rise. Identifying as many iceberg calving events as possible is essential for reducing the uncertainty in mass loss estimates, ultimately helping to improve our understanding of their cumulative impact on sea level rise and climate change.
We use seismic data to detect signals generated by time-varying forces during iceberg calving on marine-terminating glacier termini, known as glacial earthquakes. By applying a detection algorithm based on the Short-Time Average over Long-Time Average (STA/LTA) method, combined with a supervised machine learning approach (Random Forest), we successfully differentiate glacial earthquakes from tectonic earthquakes. Despite limited recordings per event, we can locate them using a non-linear location methodology (NonLinLoc).
Applying this methodology to continuous seismic data from 2013 to 2024, we identify more than 4500 previously undocumented glacial earthquakes along Greenland's coastline. While the yearly and monthly event counts are strongly influenced by the availability of seismic stations, seasonal variations in iceberg calving activity are clearly observed. This trend is further supported by an observed increase in detected events over time when focusing on a continuously available subset of stations. In addition, we will present the spatio-temporal evolution of detected events, providing further insights into the dynamics of iceberg calving activity.
These findings lay the groundwork for future work, including characterizing iceberg volume and shape to enhance our understanding of Greenland’s ice mass loss dynamics.
How to cite: Wetter, S., Mangeney, A., Hibert, C., and Stutzmann, E.: Tracking Iceberg Calving Events in Greenland from 2013 to 2024 Using Seismic Data and Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9652, https://doi.org/10.5194/egusphere-egu25-9652, 2025.
Crevassing plays an important role for the stability of glaciers and ice shelves. While dry crevasses are limited in their depth of propagation by the surrounding stress field, crevasses filled with water can become unstable and propagate far deeper, providing a route for meltwater to reach the glacier bed. Hydrofracture-driven crevassing therefore has the potential to destabilise glaciers and has also been shown to cause rapid ice shelf disintegration. However, the physical mechanisms associated with hydrofracture are seldom observed. Icequakes generated by crevasse fracture provide an ideal tool to directly interrogate the process. Here, we present crevasse-driven icequakes observed using a dense 2D grid Distributed Acoustic Sensing (DAS) deployment of fibre at Gornergletscher, Switzerland. This dataset was collected during a time of high meltwater production, providing an ideal opportunity to study the fundamental physical mechanisms associated with hydrofracture failure.
We detect and locate 951 icequakes. We then use new full-waveform inversion methods to refine event depths and obtain focal mechanisms. Furthermore, we quantify fracture mode and volumetric opening extent. We find that events typically exhibit tensile crack opening, consistent with expected crevasse fracture mechanisms. As well as direct P-wave and surface-wave energy, the waveforms contain strong coda. We attempt to isolate the spatial origin of this coda, to decipher if it is associated with either: fluid resonance at the crevasse fracture site, or wavefield scattering off other crevasses within the wider crevasse field. While we cannot definitively confirm that individual crevasse failure is caused by hydrofracture, the dense sampling provided by fibreoptic sensing allows us to interrogate the fracture mechanisms in detail. These results therefore help us understand what controls crevasse fracture propagation. Our results also highlight the application of a new generation of tools for interrogating seismic sources using fibreoptic sensing techniques in other settings.
How to cite: Hudson, T., Noe, S., Walter, F., Lipovsky, B., Kendall, J.-M., and Fichtner, A.: Interrogating crevasse icequake source physics at an alpine glacier using Distributed Acoustic Sensing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8249, https://doi.org/10.5194/egusphere-egu25-8249, 2025.
An important consequence of the rapid retreat of Alpine glaciers associated with global warming is the increasing extent of proglacial areas. These environments are dominated by a heterogeneous and dynamic fluvial system, whose evolution mostly depends on the interplay between the varying water discharge and coarse sediment supply coming from the glacier terminus. Although understanding the impact of glacier retreat on bedload yield is essential for the preservation of high-mountain regions, long-lasting investigations on the processes occurring in proglacial areas are lacking. In this context, seismic sensors recording river-induced ground vibrations have been shown to constitute a valid monitoring technique (Mancini et al., 2023; Corte et al., 2024).
Here, we present the results of monitoring campaigns carried out in the proglacial area of the Rutor Glacier (Aosta Valley, Italy) during the ablation seasons of the last three years. Ground vibrations have been monitored using a network of three geophones installed next to a stable reach of the main proglacial torrent ∼150 m downstream of the glacier mouth. Direct measurements of bedload transport have been made in 2022 and 2023 by deploying portable bedload traps at the glacier mouth. In addition to meteorological data gathered at a weather station, water discharge has been estimated by means of a downstream gauge station. We have found that a varying and non-trivial relationship exists between the direct bedload measurements and the recorded seismic signals, indicating a potential strong buffering of sediment export exerted by the proglacial area. Moreover, for all the three monitoring campaigns but starting at different moments of the ablation season, we have observed quasi-periodic peaks of seismic power occurring at a sub-hourly scale during the afternoon. We advance that they could be related to water discharge fluctuations resulting from the dynamics of the subglacial drainage system. These observations show the effectiveness of using seismic methods to shed some light on the complex feedback mechanisms existing between glacier dynamics and the natural processes of proglacial areas.
References
Mancini, D., Dietze, M., Müller, T., Jenkin, M., Miesen, F., Roncoroni, M., et al. (2023). Filtering of the signal of sediment export from a glacier by its proglacial forefield. Geophysical Research Letters, 50, e2023GL106082. https://doi.org/10.1029/2023GL106082
Corte, E., Ajmar, A., Camporeale, C., Cina, A., Coviello, V., Giulio Tonolo, F., Godio, A., Macelloni, M. M., Tamea, S., and Vergnano, A. (2024): Multitemporal characterization of a proglacial system: a multidisciplinary approach, Earth Syst. Sci. Data, 16, 3283–3306, https://doi.org/10.5194/essd-16-3283-2024
How to cite: Piantini, M., Corte, E., Camporeale, C., Tamea, S., Crema, S., and Comiti, F.: Seismic monitoring of the Rutor proglacial stream: exploring the impact of glacier dynamics on water flow and bedload transport processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19268, https://doi.org/10.5194/egusphere-egu25-19268, 2025.
Identifying the seismic signature of rivers (e.g., flow and bedload) is a significant challenge due to the varying responses of the investigation site and the hydrodynamic parameters controlling river streams during flood events. Moreover, environmental noise, such as wind and rain components, is not always easily distinguishable from the signal generated by river motion, given their overlapping frequency ranges.
We analysed the seismic signature of the Tagliamento River, located in Friuli-Venezia Giulia (Northeast Italy), recognised as one of the "last large natural alpine rivers in Europe." This river is characterised by significant water level rises and gravel sediment transport during extreme meteorological events. Using data from level gauges and pluviometric sensors alongside seismic stations installed along the river, we examined the relationship between increasing water levels, rainfall indices, and the amplitude of seismic waves recorded by seismometers during multiple flood events from 2018 to 2024.
Additionally, we performed detailed analyses, including cross-correlation, time-of-concentration calculations, and seismic signal polarisation, to better characterise river behaviour. This preliminary study aims to understand the seismic signals generated by the turbulent flow of the river and the transported bedload using the collected data. Subsequently, we propose to develop an empirical model for water level estimation, enabling the evaluation of hydrogeological hazards during upstream floods with the assistance of machine learning algorithms.
How to cite: Gangemi, M. V., Borzì, A. M., Cannata, A., Cannavò, F., Parolai, S., Spampinato, C., Zini, L., and Panzera, F.: Preliminary Seismic Signature Analysis of the Tagliamento River During Flood Events Using Machine Learning Algorithms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-906, https://doi.org/10.5194/egusphere-egu25-906, 2025.
In early 2023, California was struck by intense storms from a series of atmospheric rivers, inflicting extensive damage and hardship on Californians. These storms have also alleviated California's historical drought, rapidly refilling surface reservoirs; however, it remains unclear how much water California's depleted underground reservoirs have absorbed. Understanding these aspects is crucial for assessing the state's total water deficit and guiding sustainable water management.
Here we apply advanced seismic interferometry techniques to assess the natural recharge of aquifers in Greater Los Angeles from 2003 through the 2023 storms. The derived seismic hydrographs reveal that the expression of groundwater drought is distinct from that of surface-water drought: While surface-water storage nearly fully recovered in the epic wet season of 2023, less than 25% of the groundwater lost over the previous two decades was replenished. On a decadal scale, we find significant depletion with slight storm-related replenishment in aquifers below 50 m depth. Furthermore, seismic imaging across the study area shows prominent groundwater restoration in San Gabriel Valley, highlighting the role of mountain recharge for aquifer replenishment.
This study showcases the promise of seismic sensing for providing new insights into groundwater hydrology at different depths. Our findings emphasize the need to monitor deep aquifers for a more complete assessment of water resources, which is crucial for facilitating data-informed amidst extreme weather patterns.
How to cite: Mao, S., Ellsworth, W., Zheng, Y., and Beroza, G.: How Fast, How Deep, and How Much? — Seismic Sensing of Groundwater Recharge from the 2023 Atmospheric-River Storms, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12027, https://doi.org/10.5194/egusphere-egu25-12027, 2025.
Current methods employed to track the spatiotemporal evolution of ocean wave mainly include insitu buoys, numerical ocean wave modeling, and satellite altimetry. Each method has its own strengths and weaknesses in terms of spatial and temporal resolution. For example, buoys provide high temporal resolution, but lower spatial resolution compared to numerical wave forecast modeling and satellite altimetry.
This study explores an alternative method to investigate the feasibility of constructing an ocean wave monitoring system utilizing land-based seismic amplitudes. The proposed method relies on the correlation between secondary microseism amplitudes detected on land and their causative ocean wave heights. .
In this method, we implemented a supervised Artificial Neural Network (ANN) to quantify the nonlinear relationship between secondary microseism amplitudes recorded on land and the associated ocean wave heights.. The ANN was trained using seismic amplitudes data from seismic stations distributed across Ireland and Buoy data or numerical simulated ocean wave height data in the Northeast Atlantic. Subsequently, the trained ANN was utilized to estimate significant Wave Height (SWH) at specific location(s). The estimated wave heights exhibit a similar statistical distribution to in-situ wave height observations, with normally distributed differences. Since the approach is purely data-driven, its implementation is straightforward and holds potential as a reliable, low-cost operational tool.
The comparison between our results and the measured wave height data demonstrates a strong correlation, particularly for smaller wave heights, where the estimates show excellent accuracy. For larger wave heights, while the estimates are not as accurate, they still provide reasonably reliable approximations, highlighting the robustness of this approach, across a range of ocean wave conditions.
How to cite: baranbooei, S. and Bean, C. J.: Monitoring Ocean Wave height in the Northeast Atlantic Using Terrestrially based microseism data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5217, https://doi.org/10.5194/egusphere-egu25-5217, 2025.
The hamlet of La Bérarde, a touristic hotspot in the French Alps for hiking and mountaineering and an iconic site in the history of alpinism, was severely impacted by the catastrophic flooding of the Etançons torrent during the night of June 20–21, 2024. The event resulted in the evacuation of 114 people, affected 66 buildings, and resulted in the complete destruction of 16 structures. The flood, characterised afterwards with a centennial recurrence interval, was caused by a combination of intense precipitation over the 2 days, significant snowmelt, and the sudden drainage of the supraglacial lake of the Bonne Pierre glacier.
Field assessments revealed that up to 300,000 m³ of sediments were transported downstream by the torrent, explaining the landscape transformation that occurred in the hamlet. Due to the evacuation of the village during the middle of the night and to the destruction of the river gauge downstream during the event,reconstructing the sequence of events involving the torrent and the associated debris flows proved challenging.
The three closest seismic stations to La Bérarde (located 15-20 km away) were used in this study to better refine the timeline of the flood. Tools such as seismic signal polarisation and spectrograms helped us to constrain the hours of the night where we observed an increase in the recorded seismic energy and a shift in the polarisation toward the hamlet. These findings align with eyewitness accounts and measurements of the Véneon River flow prior to the destruction of the river gauge by the flood.
Eventually, we installed a seismic station shortly after the flood near the front of the Bonne Pierre glacier and at the cross section of the Bonne Pierre river and the Etançons torrent to have a better idea of the sediment’s availability in case of futur glacial lake drainage. This revealed that a large amount of sediment is available and could potentially be carried by the torrent in case of another rapid drainage of the glacier.
This work was funded by the European Research Council (ERC) under grant No. 101142154 - Crack The Rock project.
How to cite: Bontemps, N., Larose, E., Chmiel, M., and Blanc, A.: The June 2024 Flooding of La Bérarde: Insights from Seismic Data and Field Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6557, https://doi.org/10.5194/egusphere-egu25-6557, 2025.
In recent years, Oman has faced increasing challenges with flash floods, driven by climate change and rapid urbanization. Climate change has intensified the water cycle, causing more frequent and severe precipitation in this arid region. Urban expansion into wadi floodplains, which historically acted as natural flood channels, has worsened the situation. Oman's flood preparedness is critically hindered by the lack of effective early warning systems. While sensor networks could monitor rainfall and wadi flow to provide flood alerts and water management data, their implementation is limited by the country's vast territory, complex terrain, and high infrastructure costs.
The existing wadi monitoring infrastructure in Oman relies on two primary types of measurement devices: pressure gauges and radar sensors. However, each technology presents distinct operational challenges in the dynamic wadi environment. Pressure gauges, which must be installed directly within the wadi bed to measure water levels, are vulnerable to damage or complete loss during powerful flood events. Radar gauges, while avoiding direct water contact, face different limitations. These devices are typically mounted on structures along the wadi banks to measure water levels from above. However, this positioning becomes problematic due to the naturally shifting nature of wadi channels, which can migrate significantly over time through erosion and sediment deposition.
Image-based monitoring systems offer a promising solution to the challenges of wadi measurement. Cameras can be safely installed outside the channel while maintaining visibility across the entire river cross-section. Different studies have shown that cameras can accurately measure water levels, even with low-cost equipment. Moreover, these systems can measure flow velocities by analysing short video sequences, enabling discharge estimation. However, image-based methods have a significant limitation: they perform poorly in challenging lighting conditions, e.g. at night, during heavy rain or dust events.
Recently, seismic observations were utilized to infer river level and bedload flux, using low cost sensors (e.g. Raspberry Shake) installed at safe distance to the hazardous flood corridor. These studies employed physical models, which predict the seismic frequency spectra created by bedload transport and turbulent flow. Those models rely on a large number of parameters to be set, including water level. Therefore, Monte Carlo approaches are used to randomly sample parameters for synthetic spectra calculation to be compared against the empirical one, ultimately leading to the water level.
The integration of cameras and seismic sensors can allow for a robust and synergetic measurement system. Optical measurements of water level and surface velocities can effectively constrain the parameters used in seismic signal analysis, significantly improving water level estimation accuracy when image-based methods are not available, particulary during night time operations. With the increasing availability of low-cost seismometers, we have developed and implemented a combined low-cost seismo-optical monitoring system. To evaluate this approach, the setup was installed at two reaches of Wadi Al-Hawasinah in Oman. Our study examines initial results from flow events of varying magnitudes and assesses the practical applicability of this integrated monitoring solution.
How to cite: Krüger, R., Dietze, M., Grundmann, J., Al-Rawas, G., and Eltner, A.: Low-cost instrumentation for monitoring wadi discharge: A Raspberry Shake and time-lapse camera system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16371, https://doi.org/10.5194/egusphere-egu25-16371, 2025.
River-ice affects hydraulics and sediment transport that may in turn influence channel morphology. However, scientific understanding of sub-ice flows is limited by the difficulty of accessing the ice-covered channel bed and banks. During periods of stable ice cover, hydraulic studies usually assume that the stable ice cover is free-floating and can therefore move vertically to accommodate changes in river discharge. However, ice cover is often fixed in place, attached to the channel banks. In this case, increasing discharge is forced under the ice cover causing pressurized flows typified by higher flow velocities and sediment transport. The identification and study of pressurized flows is difficult due to the challenges of measuring flows in ice-covered rivers during high discharges; in particular since common methods of drilling holes to measure velocities will disrupt any potential pressurization.
We aim to determine if environmental seismology can be used to identify pressurized flows in rivers and to interpret the characteristics of seismic signals to inform knowledge of hydraulic processes during pressurized flow events. Thus, we set up a flume experiment to compare the hydraulic seismic signature of free-surface flow with pressurized flow under fixed ice-covered conditions. Using a 7m-long transparent 10 x 10 cm PVC tube and fixing roughness elements onto the riverbed (sand and gravel), we test three configurations varying the discharge and the distance between the bed and the bottom of the ice cover (simulated by the upper surface of the inside of the tube). The slope is 0.3 % to represent prototype low-slope subarctic river channels. Two PE6/B three-component 4.5 Hz geophones record millisecond resolution seismic data: one is installed on top of the water-filled flume, and the other on an empty 1m-long section of the same type of PVC tube placed next to the flume, to record background noise. We can pressurize the water-filled flume by increasing the discharge for a given treatment, and record discharge and video data to identify and describe pressurization events.
Comparing seismic and discharge data confirms that we can identify hydraulic signals in the seismic record. We observe a scaling relationship between discharge data and seismic power, and are investigating its coherence with existing theoretical models and its dependency on apparent bed roughness. We expect pressurized flows to appear as high-energy signals due to increased water velocity, with a decrease in background noise due to complete contact between the water and the pipe.
These results can help resolve a long-term aim of identifying the occurrence of sub-ice pressurized flows from seismic field data. Such understanding has implications for using seismic signals to calculate stage in ice-covered rivers or subglacial channels and calculating ice-related bedload transport. These techniques provide unparalleled opportunities for non-intrusive and continuous measurements of hydraulic processes under ice.
How to cite: Laporte, S., Gimbert, F., Buffet, A., Bellot, H., Polvi, L. E., and Mason, R. J.: Identifying pressurized flows under river-ice using seismology: insights from a flume experiment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16277, https://doi.org/10.5194/egusphere-egu25-16277, 2025.
In April 2024, a M7.2 earthquake struck the east coast of the Taiwanese island near the city of Hualien. Being the largest earthquake in the region for more than 25 years, the Hualien earthquake offers a unique opportunity to study the landscape and subsurface response to strong ground motion. Extraordinarily high precipitations during the following monsoon season put additional pressure on the near-surface and subsurface hydrological systems. Here, we combine multidisciplinary environmental and hydrological datasets with seismological data products recorded by a network continuously active since 2016 around the Liwu River catchment. We analyse, for example, seismic velocity change time series (dv/v) or horizontal over vertical spectral ratios (H/V) to shed light on the mechanisms causing increased river discharge and changes in water composition following strong ground motion events. In the data, we not only find a strong response to the M7.2 earthquake but also clear evidence of seasonal variation corresponding to the biannual cycles in temperature and rainfall. This study will put further constraints on the reaction of aquifers and aquitards in mountainous environments to large earthquakes. Mountain freshwater reservoirs are a primary resource for the Taiwanese population and economy. Understanding its dynamics will shed light on the chances and limitations of its exploitation and sensitivity to climate change.
How to cite: Makus, P., Hovius, N., Turowski, J., and Chang, J.-M.: Investigating Modifications in the Hydrological System Following the M7.2 Hualien Earthquake with Seismic Methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5684, https://doi.org/10.5194/egusphere-egu25-5684, 2025.
Glacier grounding zones, where ice transitions from resting on land to floating on ocean, are critical to understanding ice sheet dynamics and stability. Despite their importance, these regions are challenging to study directly due to their inaccessibility and the inherent risks of fieldwork. To address this, we conducted seismic investigations at Eastwind Glacier, Antarctica, an accessible grounding zone near McMurdo Station and Scott Base, as part of the EGGS on TOAST project. Our fieldwork included deploying 330 three-component seismic nodes across the grounding zone during the austral summer of 2022/23, capturing continuous data for nine days on all nodes, with extended recordings of 19 days on 150 nodes. Active-source seismic data were acquired using hammer-and-plate shots, both densely spaced along the array's centerline and at individual node locations. In the following field season (2023/24), we supplemented these observations with distributed acoustic sensing (DAS) using a fiber optic cable positioned downstream of the grounding line for cross- and along-flow imaging. Initial analyses of the seismic data reveal key features, such as the flotation point of ice and ice and firn thickness variations. Additionally, passive seismic methods provide insights into icequake activity and ambient noise characteristics. This comprehensive dataset offers a new perspective on grounding zone processes and serves as a valuable resource for testing innovative cryo-seismological techniques.
How to cite: Young, T. J., Pearce, E., Agnew, R., Karplus, M., Ranganathan, M., Hoffman, A., Hunt, M., Pretorius, A., Shanly, S., Beres, M., Pradhan, K., Seldon, Y., Booth, A., and Clark, R.: Active and passive seismic surveys over the grounding zone of Eastwind Glacier, Antarctica, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17047, https://doi.org/10.5194/egusphere-egu25-17047, 2025.
Alpine glaciers have been retreating at increasing rates in recent decades due to climate warming. As a consequence, large amounts of suspended and bedload flux are exported from subglacial channels to proglacial environments, such as proglacial forefields. To date, our understanding of subglacial sediment export by subglacial streams has been predominantly shaped by suspended sediment dynamics recorded in front of shrinking glaciers, primarily due to difficulties in measuring bedload transport. Bedload transport is typically monitored far downstream from glacier termini at permanent monitoring stations (e.g. water intakes), leaving significant uncertainties regarding the absolute quantities and temporal patterns of transport in both glacial and proglacial environments, as well as its relative importance compared to suspended sediment in the context of proglacial morphodynamic filtering. Recent advancements in environmental seismology have addressed this knowledge gap. Given this, the aim of this project was to develop a novel technique for calibrating the Fluvial Model Inversion (FMI) model of Dietze et al. (2019) to quantify, for the first time, the total subglacial bedload export from an Alpine glacier and to investigate the physical mechanisms driving it.
This work focuses on a large Alpine glacier, the Glacier d’Otemma, located in the Southwestern Swiss Alps (Canton Valais). Continuous seismic data were collected in close proximity to the glacier terminus using a DATA-CUBE type 2 datalogger connected to a three-component PE-6/B geophone, over two entire melt seasons (June to September 2020 and 2021) experiencing different climatic conditions: the first year was warm and relatively dry, while the second was cold and relatively wet.
The seismic ground parameter values of the FMI model used to invert the raw seismic data into bedload transport were determined by adopting a Monte Carlo simulation based on a Generalized Likelihood Uncertainty Estimation (GLUE) approach. This involved iteratively running thousands of inversions within predefined ranges of possible ground seismic parameter values. The methodology was validated by comparing parameter values and model outputs to those obtained using a more conventional active seismic survey.
Results indicate that the developed methodology for calibrating the inversion model is promising and comparable to those derived from the more demanding active seismic survey technique. Scientifically, findings reveal a strong agreement between subglacial bedload export rates and the snowline altitude during the melt season. Extremely warm summers are associated with the exhaustion of subglacial bedload sources as the progressive rise of the snowline altitude fully exposes the glacier's bare ice, while cooler summers show the opposite pattern. This highlights the existence of a link between atmospheric temperature, subglacial drainage network extension, and bedload output rates. These results are crucial for advancing our understanding of the relationship between subglacial sediment export and meteorological conditions in a warming climate.
How to cite: Mancini, D., Dietze, M., Jenkin, M., Müller, T., Miesen, F., Roncoroni, M., and Lane, S. N.: Subglacial bedload export quantification and subglacial drainage network evolution inferred using environmental seismology techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9517, https://doi.org/10.5194/egusphere-egu25-9517, 2025.
Subglacial processes are difficult to monitor due to their inaccessibility with conventional hydrological probes. We know relatively little about when and at what rate the products of subglacial erosion are evacuated, especially for coarse sediment (bedload). Environmental seismology is contributing to close this knowledge gap, providing some of the first, seasonal-scale datasets on bedload evacuation by subglacial streams. The advantage of seismic monitoring of subglacial sediment transport is that it does not need to be installed directly into the water.
The location of a static subglacial channel can be found using techniques such as GPR surveys, but rapid changes in the subglacial channel system require continuous data sets on channel location. Monitoring seismic amplitudes and applying beamforming methods to seismic array records, one can locate noise sources and thus identify variations in activity and location of subglacial streams and bedload transport. This may be done using arrays of seismic nodes and/or distributed acoustic sensing (DAS) along an optical fiber. To identify the best use of such methods for monitoring the subglacial stream, the present study compares conventional seismic sensors and fiber optic cables for beamforming source location.
The field site of this study is Glacier d’Otemma in Valais, Switzerland. Given two data sets of seismic nodes and DAS, as well as ancillary observations, we can identify the location of the subglacial river and track changes in its discharge and bedload transport rate. These findings mainly relate to variations in seismic noise throughout the diurnal cycle of glacier melt. Depending on frequency band and daytime, the location of the most intense seismic noise, averaged over two hours, varies. These variations relate to processes such as surface melt, which stops during night, and subglacial flow, which continues but is less intense. Seismology proves to be a temporally and spacially rich tool to monitor this constantly changing activity of glaciers.
How to cite: Wolf, E., Stutzmann, E., Metaxian, J.-P., Nanni, U., Miesen, F., Ballu, A., Walter, F., Mangeney, A., Arbeu, R., Schimmel, M., Dietze, M., and Lane, S.: Comprehensive monitoring of the subglacial stream of Glacier d'Otemma using seismic nodes and distributed acoustic sensing data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16515, https://doi.org/10.5194/egusphere-egu25-16515, 2025.
Changing climate, especially the increase in frequency and intensity of extreme events such as heat waves and droughts, places many forests under significant pressure. However, we lack methods to efficiently track stress responses of trees across large scales. Real-time monitoring of physiological and structural stress indicators of trees, for instance via sap flow, stomatal conductance, or photosynthetic activity are often expensive, require high maintenance, and are therefore not efficient on a larger spatio-temporal scale.
We propose to investigate whether the stress responses of trees can be approximated as a function of the seismic power generated by tree sway - referred to as the tree’s seismic fingerprint. These wind-induced sway signals are intrinsically linked to the material properties of leaves, branches, and trunks, which are influenced by changes in cell water content and corresponding turgor pressure. Seismic measurements offer scalability and low maintenance, making them viable for extensive long-term monitoring. Moreover, the data’s high temporal resolution provides detailed and characteristic sway frequency information that could be linked to tree individuals, species or traits.
Using complementary observations from ground-based seismometers and tree-attached accelerometers collected at the ECOSENSE site in the Black Forest, we successfully isolated and analysed the seismic fingerprint of tree sway through frequency analyses and signal correlations. We further integrated these sway data with direct tree traits and meteorological time series using machine learning techniques. We present the first results of this innovative approach, marking a significant step towards understanding the intricate relationship between tree motion and their immediate surrounding ecosystem.
How to cite: Umlauft, J., Mora, K., Kattenborn, T., Wirth, C., and Werner, C.: The Seismic Fingerprint of Tree Sway, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15658, https://doi.org/10.5194/egusphere-egu25-15658, 2025.
Assessing and quantifying bedload dynamics and sediment transport rates in rivers is critical for evaluating the landscape evolution, which in turn controls channel morphology and catchment erosion. In the last decades, seismic observations emerged as one of the most promising tools for monitoring river dynamics. In particular, recorded seismic energy has been shown to correlate with river discharge and with the amount of transported sediments. However, uncertainties persist in quantifying bedload transport using recorded seismic signals. This lack is particularly relevant for small mountain streams, where sediment mobilisation begins, that have been to date poorly studied.
In this study we present the first outcomes of two years of continuous seismic monitoring of the Re della Pietra, a small stream in Tuscan Appennines. Specifically, we analyse data collected by two triaxial seismometers placed in two different channel sections, deployed on the riverbank, ~3 meters from the stream. Root-mean-square amplitude analysis (RMSA) is used for computing the envelopes on recorded data as well as analysis on frequency domain is performed for investigating the spectral content of the signal. Over the two years of observations many flood events were recorded, ranging from small and short (few hours) events to massive and long (days) ones related to exceptional storms. Recorded seismic data shows peculiar waveform and spectral footprints. To investigate how flow dynamics affect seismic radiation, collected seismic data are compared with flow depth data and video images acquired by during the events. Preliminary results highlight important constraints on the mobilizations of the solid particles within small creeks thus suggesting how seismic sensors can be successfully used for monitoring the bedload transport.
This study is being carried out within the interdisciplinary project TRANSFORM (“A new interdisciplinary approach to advance understanding of sediment and large wood TRANSport in FORested Mountain catchments”- https://florenceuniversity.wixsite.com/transform).
How to cite: Marchetti, E., Belli, G., Gheri, D., Innocenti, L., Murgia, I., Chirici, D., Verdone, M., Nicoletti, S., Solari, L., Morandi, O., and Penna, D.: Seismic analysis of bedload transport in a small mountain creek, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21164, https://doi.org/10.5194/egusphere-egu25-21164, 2025.
Understanding bedload transport is crucial for predicting sediment flux and managing fluvial systems. Previous studies, such as those by Burtin et al., (2016); Gimbert et al., (2016), and Piantini et al., (2022), have explored fluvial dynamics using dense arrays with up to 80 sensors in alpine regions like the Himalayas and Alps. However, these approaches are less adaptable to a wider variety of ecosystems. Our study addresses this gap by developing a seismic array geometry tailored to diverse fluvial environments, optimizing signal location while maintaining scalability and adaptability.
We introduce a framework for optimizing array geometry and integrating beamforming as well as directivity analyses to enhance accuracy of signal detection. Results indicate that strategic seismic sensor placement significantly improves location precision and minimizes ambiguities caused by overlapping signals. These findings establish a robust methodology for continuous, non-invasive monitoring of fluvial bedload transport, applicable across morphologically diverse river systems.
Preliminary results from the Arroyo de los Pinos, New Mexico—a semi-arid, flash-flood-prone environment— are promising with interactive positive components. An optimized array comprising 17 seismic nodes, covering frequencies from 1 Hz to 100 Hz, was deployed and optimized for signal processing with numerical modelling. Future efforts will extend this framework to other ecosystems, refining predictive capabilities and advancing sediment management strategies.
Reference
- Burtin, Arnaud, et al. "Spectral analysis of seismic noise induced by rivers: A new tool to monitor spatiotemporal changes in stream hydrodynamics." Journal of Geophysical Research: Solid EarthB5 (2008).
- Burtin, Arnaud, Niels Hovius, and Jens M. Turowski. "Seismic monitoring of torrential and fluvial processes." Earth Surface Dynamics2 (2016): 285-307.
- Gimbert, Florent. "Using array seismology to quantify river physics." AGU Fall Meeting Abstracts. Vol. 2016. 2016.
- Piantini, Marco, et al. "Using a dense seismic array to study fluvial processes in a braided river reach under flood conditions." LHB1 (2022): 2053314.
How to cite: Padmadas, A., Laronne, J., Walter, F., Bilek, S., and Turowski, J.: Precision in Seismic Detection of Bedload Transport: Visualizing Array Geometry for Optimal Source Localization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16545, https://doi.org/10.5194/egusphere-egu25-16545, 2025.
The Åknes rockslide is located on the slope of a steeply dipping fjord in Norway in the proximity of urban areas, posing a significant hazard due to its potential to trigger a massive tsunami. This study utilizes data from eight vertically aligned borehole geophones and one broadband seismometer on the surface, collected over a period of approximately 22 months. Previous research has demonstrated that passive seismic monitoring, specifically tracking changes in seismic velocities, can provide precursory indicators of landslide failure. This study aims to assess the potential of this method for monitoring and identifying seasonal patterns in the subsurface properties of the slope. To achieve this, we perform seismic interferometry on various frequency bands to calculate relative seismic velocity changes near the borehole and broadband station.
By integrating meteorological data from the study area, we can relate these velocity variations to environmental factors. Our analysis indicates that measurements from borehole sensors demonstrate a positive correlation between temperature and seismic velocity changes during snow-covered months, and a negative correlation during the summer, highlighting the sensitivity of seismic waves to seasonal changes and therefore different environmental regimes. Additionally, results from the broadband sensor reveal a clear decrease in seismic velocities during the melting period, and an increase in seismic velocities with increased precipitation and the reemergence of snow cover, suggesting the seismic velocities being influenced by changes in the water content. These findings advance our understanding of the relationship between calculated relative velocity changes and their connection to complex environmental interactions. This is essential for incorporating seismic velocity monitoring as a tool for assessing the stability of the Åknes slope.
How to cite: Bogner, L., Bruland, C., Langet, N., Oye, V., Hadziioannou, C., and Kiel, A.: Seismic Investigation of the Åknes Rockslide: Using Ambient Seismic Noise to Identify Possible Rockslide Movement, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7093, https://doi.org/10.5194/egusphere-egu25-7093, 2025.
Landslides pose a significant threat to settlements, infrastructure and communities globally. In order to mitigate the potential impact and damages caused by these phenomena, various approaches and methodologies have been developed and implemented. Among these, the continuous monitoring of slope instabilities is crucial for understanding landslide dynamics and gaining information in predisposing and triggering factors. In this context, the use of passive seismic sensors has emerged as a powerful tool for monitoring, as they can detect subtle transient slope mechanical and hydrological changes as well as unpredictable episodes of signal emission associated with slope deformation processes. By continuously recording such microseismic activity, seismometers can provide data on landslide movements, offering valuable insights into the state of activity and allowing a better understanding of the relationships between the driving mechanisms.
This study provides a preliminary attempt on the investigation of slow-moving processes occurring in the region of Lower Austria, which is known to be highly prone to landslides due to its complex geological characteristics. The lithological transition between the Flysch and Klippen Unit formations consists predominantly of mechanically weak components, such as intercalated limestones and marlstones to claystone and deeply weathered materials. Combined with hydrological factors, changes in land use, and anthropogenic influences, these predisposing conditions contribute to the region's susceptibility to slope instability.
In this work, we present the results from an ongoing monitoring conducted across three well established landslide observatories in this region, which have been co-instrumented with a total of 26 geophones to monitor landslide activity. The deployed compact seismic stations consist of geophones, installed at 25 cm depth in dug pits, and a DataCube data logger recording ground velocity values at 200 Hz sampling frequency. This setup is powered by a 55Ah 9V battery and periodically visited to extract data and check the station status. Here, we evaluate and discuss the seismic expression of external drivers, co-registered slope deformation and spatio-temporal patterns of slope activity. In addition, taking advantage of the sensors included in the monitoring network installed in each site (e.g. inclinometers, piezometers), we examine the possibility of analysing the relationship with possible drivers and reactions on nested temporal scales. The findings of this work contribute to advancing the application of passive seismic monitoring technologies in landslide research, particularly in the context of slow-moving landslides.
How to cite: Marr, P., Dietze, M., Carraro, E., Kanta, R., and Glade, T.: Exploring the potential of seismic sensors in monitoring slow moving landslides in Lower Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6236, https://doi.org/10.5194/egusphere-egu25-6236, 2025.
The water cycle impacts geophysical signals, influencing our ability to monitor subsurface hydrology. At the Membach geophysical station in Belgium, we integrate gravity and ambient seismic noise data to study hydrological variations and develop a numerical hydrological model at a local scale. Our findings reveal that gravity observations at Membach station exhibit gradual changes, reaching a peak at ± 2-day after rainfall, reflecting subsurface water redistribution and storage processes. Concurrently, increased soil saturation corresponds with a decrease in HVSR (Horizontal-to-Vertical Spectral Ratio), indicating reduced stiffness and changes in seismic wave propagation. Furthermore, relative velocity changes (dv/v) show frequency-dependent time delays, with deeper layers exhibiting slower responses compared to shallower regions. These results highlight the dynamic relationship between rainfall, soil saturation, and geophysical responses, providing new insights into critical zone processes. By combining gravimetry and ambient seismic noise, we address challenges in studying deep and complex subsurface zones, where traditional hydrological methods often fall short. This approach not only enhances our understanding of subsurface hydrology but also improves water resource management and critical zone studies. The integration of geophysical methods offers a comprehensive framework for monitoring hydrological dynamics, advancing our ability to interpret geophysical signals influenced by the water cycle and providing a valuable tool for managing environmental and climatic impacts on subsurface water storage.
How to cite: Saraswati, A., Lecocq, T., and Vanclooster, M.: Investigating Soil Saturation Changes through Geophysical Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18249, https://doi.org/10.5194/egusphere-egu25-18249, 2025.
The Mansfelder Mulde (German for trough) around Lutherstadt Eisleben is considered as an active subsidence area. Deep-seated subrosion is the potential reason for large- and small-scale earth surface deformation. The appearance of sinkholes as potentially hazardous surface expression of karst has led to increasing interest in the area. Specifically, in the study area Neckendorf in the southwest of the Mansfelder Mulde, two major sinkholes occurred in the early 2000s, affecting a federal road and an allotment garden site. At the end of 2021, surface cracks formed again along a main road just 800 meters away from the previous sinkholes. This process is attributed to ongoing subsidence in the adjacent field. The continuous ground movement, coupled with significant surface cracking along an additional road causing severe traffic problems, necessitated the complete closure of both roads in December 2022.
The subsidence area has been investigated by the State Office for Geology and Mining (LAGB) Saxony-Anhalt since 2022 and is since March 2024 a research topic of the UL. During first field measurements, the edge areas of the subsidence were surveyed using Electrical Resistivity Tomography (ERT). In addition, three seismic stations were recently (November 2024) installed to investigate the ground movements in the context of the large-scale subsidence. Currently, no results from the seismic data are available. Once the field data has been retrieved, it will be analyzed in conjunction with the existing ERT data to discuss the subsidence event. With the help of long-distance (deep) ERT we aim to decipher the hydrogeologic conditions of the Anhydrite and Gypsum Zechstein layers, at the supposed base of the subrosion. One objective was to detect, cracks and loosening zones also in the overlying lower Buntsandstein layers. Several profiles were created along the neighbouring fields and the affected roads. Due to different electrical material properties compared to the surrounding soil material, the suspected subrosion features appear as anomalies. ERT showed a clear difference between farmed and abandoned, non-farmed areas. Higher resistivities indicate a deformed subsoil, and with high probability an extension of the loosening zones beyond the crack formation visible on the surface. Near vertical lower-resistance structures could indicate water-saturated fracture zones in context of the main subsidence. Furthermore, the effects of a defective water pipe were possibly detected with ERT. As it is currently not possible to estimate how the subsidence will develop, the evaluation of geophysical data is significant for local hazard assessment and should, above all, provide the affected farmers with clarity about the subsoil situation of their fields and inform local stakeholders about the ongoing process.
How to cite: Silbermann, M., Khosravichenar, A., Aalijahan, M., Ginga, M., Rappsilber, I., Sänger, N., Gauert, C., Seidemann, J., Umlauft, J., and Al-Halbouni, D.: Geoelectrical and seismic investigation of a subsidence geohazard zone in Neckendorf, Saxony-Anhalt, Germany , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20756, https://doi.org/10.5194/egusphere-egu25-20756, 2025.
