GM2.1 | Environmental Seismology: advancing Earth surface process understanding through geophysical methods
EDI
Environmental Seismology: advancing Earth surface process understanding through geophysical methods
Co-organized by CR5/SM5
Convener: Josefine UmlauftECSECS | Co-conveners: Małgorzata ChmielECSECS, Fabian LindnerECSECS, Michael Dietze, Janneke van GinkelECSECS
Orals
| Wed, 17 Apr, 14:00–17:55 (CEST)
 
Room D3
Posters on site
| Attendance Thu, 18 Apr, 16:15–18:00 (CEST) | Display Thu, 18 Apr, 14:00–18:00
 
Hall X4
Orals |
Wed, 14:00
Thu, 16:15
Our planet is shaped by a multitude of physical, chemical and biological processes. Most of these processes and their effect on the ground’s properties can be sensed by seismic instruments – as discrete events or continuous signatures. Seismic methods have been developed, adopted, and advanced to study those dynamics at or near the surface of the earth, with unprecedented detail, completeness, and resolution. The community of geophysicists interested in Earth surface dynamics and geomorphologists, glaciologists, hydrologists, volcanologists, geochemists, biologists or engineering geologists interested in using arising geophysical tools and techniques is progressively growing and collaboratively advancing the emerging scientific discipline Environmental Seismology.

If you are interested in contributing to or getting to know the latest methodological and theoretical developments, field and lab scale experimental outcomes, and the broad range of applications in geomorphology, glaciology, hydrology, meteorology, engineering geology, volcanology and natural hazards, then this session would be your choice. We anticipate a lively discussion about standing questions in Earth surface dynamics research and how seismic methods could help solving them. We will debate about community based research opportunities and are looking forward to bringing together transdisciplinary knowledge and mutual curiosity.

Topical keywords: erosion, transient, landslide, rockfall, debris flow, fracturing, stress, granular flow, rock mechanics, snow avalanche, calving, icequake, basal motion, subglacial, karst, bedload, flood, GLOF, early warning, coast, tsunami, eruption, tremor, turbidity current, groundwater, soil moisture, noise, dv/v, HVSR, fundamental frequency, polarization, array, DAS, infrasound, machine learning, classification, experiment, signal processing.

We are happy to announce our solicited speakers Emma Pearce and Florent Gimbert!

Orals: Wed, 17 Apr | Room D3

Chairpersons: Małgorzata Chmiel, Josefine Umlauft
14:00–14:05
Ice & Water
14:05–14:25
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EGU24-13859
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ECS
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solicited
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Highlight
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On-site presentation
Emma Pearce, Dimitri Zigone, Andreas Fitchner, Coen Hofstead, Joachim Rimpot, Johannas Brehmer-Moltmann, and Olaf Eisen

In 2022 a network of 23 seismometers and Distributed Acoustic Sensing (DAS) fibre optic cable were deployed on the North East Greenland Ice Stream (NEGIS). Using a combination of environmental seismology methods, we were able to gain a comprehensive understanding of the ice streams internal structure, giving insight into its past and present dynamics.  

From ambient noise recording, we utilise the 9-component correlation tensors associated with all station pairs.  We derived dispersion curves for Rayleigh and Love wave group velocities with usable data in the frequencies from 1 to 25 Hz. These data are then inverted to obtain shear wave velocity measurements for the top 150 m of the ice stream using an MCMC approach. We reveal variations in the radial anisotropy for both the along and across-flow components.

Alternative methods of passive seismology were explored, such as using the seismic signal from an airplane landing. The recorded signals by the surface DAS cable displayed exceptional clarity, revealing at least 15 visible wave propagation modes, including various Rayleigh and pseudo-acoustic waves within the frequency range of 8 to 55 Hz.

Seismic While Drilling (SWD) methods utilising the noise from ice core drilling and cutting at NEGIS were investigated as an unconventional signal at the borehole camp. While not successful in this instance, recommendations for future deployments were provided to optimize the utilisation of these techniques.

These methods collectively offer insight into the layering of snow, firn, and ice within the ice stream, indicating the presence of seismic anisotropy. Demonstrating the effectiveness of short-duration (2-3 weeks) seismic deployments in glaciology.  

How to cite: Pearce, E., Zigone, D., Fitchner, A., Hofstead, C., Rimpot, J., Brehmer-Moltmann, J., and Eisen, O.: An overview of environmental seismology used to study the internal structure of the North East Greenland Ice Stream , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13859, https://doi.org/10.5194/egusphere-egu24-13859, 2024.

14:25–14:35
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EGU24-10380
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On-site presentation
Léonard Seydoux, Ugo Nanni, Lucien Goulet, Thomas Pauze, and Andreas Köhler
Glacier flow instability often results from changes at the ice-bed interface. However, understanding these processes is challenging due to limited access to the glacier bed. Our study focuses on Kongsvegen glacier in Svalbard, which shows signs of an upcoming rapid flow event. To investigate the potential causes of such acceleration, we installed 20 seismometers along the glacier flowline, from the surface down to 350 m near the ice-bed interface. We combined our seismic monitoring with measurements of surface velocity, basal water pressure, and basal sediment deformation.
First, we performed seismic noise interferometry between stations located along the glacier flowline with inter-station distances ranging from 1 to 12 km. We observed a multi-year decrease in seismic velocity, with a seasonal signal superimposed, showing a melt-season decrease in seismic velocity of 2 to 4%. We compared our observations with 1D models and concluded on the presence of damaged basal ice and/or a weakening of the subglacial sediments. This indicates a mechanical weakening of the ice-bed interface, promoting further glacier acceleration.
Second, we conducted unsupervised clustering of seismic waveforms using a novel approach based on a deep scattering network. Doing so, we observed a yearly increase in surface crevasses concomitant with an increase in basal events, likely indicating stick-slip and/or basal crevasses. This increase is particularly visible during winter, where the number of events steadily increases from year to year. We suggest that, in response to an initial glacier acceleration, new crevasses have opened, providing access pathways for surface meltwater to the base of the glacier, affecting the ice-bed coupling. This mechanism represents a positive hydro-mechanical feedback that fuels further acceleration and crevassing, potentially having wider implications for triggering glacier-wide instabilities, increasing short-term sea-level rise, and local hazards.

 

How to cite: Seydoux, L., Nanni, U., Goulet, L., Pauze, T., and Köhler, A.: Observing ice-bed weakening on a fast flowing glacier with seismic noise interferometry and unsupevised clustering., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10380, https://doi.org/10.5194/egusphere-egu24-10380, 2024.

14:35–14:45
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EGU24-17786
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On-site presentation
Antoine Guillemot, Eric Larose, Laurent Baillet, Agnès Helmstetter, Xavier Bodin, and Reynald Delaloye

Since last decades, coda wave interferometry (CWI) from ambient seismic noise has become an efficient method to probe continuous temporal changes of mechanical properties of the subsurface and crust. This method has successfully been used for environmental seismology issues, in a view of investigating the response of subsurface to environmental changes, in particular hydrological and thermal forcings (2). More, it has contributed to monitoring instabilities such rock slopes or landslides (3). Applying these methods to permafrost is then relevant to assess and monitor its mechanical response to environmental forcings.

As lobate or tongue-shaped superficial landforms composed of frozen rock debris, active rock glaciers are widespread features of mountain permafrost (4), potentially causing emerging hazards linked to permafrost thawing and debris flows.

Passive seismic instrumentation has been deployed for several years at Gugla, Tsarmine (Valais, Switzerland) and Laurichard (Hautes-Alpes, France) rock glaciers.

CWI has been applied to compute daily averaged dV/V (or relative change in velocity of the surface waves). For the three sites studied, seasonal variations of shear stiffness have been measured, associated with freeze-thawing cycles (5) (6). We located these daily fluctuations in depth by using a 1D coda wave inversion scheme. We also tracked water-induced power spectral density (PSD) and we detected microseismic events, highlighting the role of water inputs in changing the mechanical state, thus accelerating the whole rock glacier body. Also, we developed a viscoelastic model to explain the seasonal variability of the kinematics of rock glaciers. Combined with other geophysical methods, environmental seismology paves hence the way to deeply understand the mechanical response of mountain permafrost landforms to thermo-hydrological forcings.

 References

  • Richter, T., Sens‐Schönfelder, C., Kind, R., & Asch, G. (2014). Comprehensive observation and modeling of earthquake and temperature‐related seismic velocity changes in northern Chile with passive image interferometry. Journal of Geophysical Research: Solid Earth, 119(6), 4747-4765
  • Le Breton, M., Bontemps, N., Guillemot, A., Baillet, L., & Larose, É. (2021). Landslide monitoring using seismic ambient noise correlation: challenges and applications. Earth-Science Reviews, 216, 103518.
  • Haeberli, W., Hallet, B., Arenson, L., Elconin, R., Humlum, O., Kääb, A., ... & Mühll, D. V. (2006). Permafrost creep and rock glacier dynamics.Permafrost and periglacial processes, 17(3), 189-214.
  • Guillemot, A., Helmstetter, A., Larose, É., Baillet, L., Garambois, S., Mayoraz, R., & Delaloye, R. (2020). Seismic monitoring in the Gugla rock glacier (Switzerland): ambient noise correlation, microseismicity and modelling.Geophysical Journal International, 221(3), 1719-1735. https://doi.org/10.1093/gji/ggaa097
  • Guillemot, A., Baillet, L., Garambois, S., Bodin, X., Helmstetter, A., Mayoraz, R., and Larose, E.: Modal sensitivity of rock glaciers to elastic changes from spectral seismic noise monitoring and modeling, The Cryosphere, 15, 501–529, https://doi.org/10.5194/tc-15-501-2021, 2021.

How to cite: Guillemot, A., Larose, E., Baillet, L., Helmstetter, A., Bodin, X., and Delaloye, R.: Monitoring the mechanics of mountain permafrost using ambient noise seismology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17786, https://doi.org/10.5194/egusphere-egu24-17786, 2024.

14:45–14:55
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EGU24-9608
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ECS
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On-site presentation
Luc Illien, Jannik Kuehn, Christoff Andermann, and Niels Hovius

Groundwater storage monitoring is now one of the most promising application of seismic interferometry techniques. In steep mountain environments, where drilling wells is particularly challenging, the use of seismic stations to retrieve relative seismic velocity changes could fundamentally advance our understanding of groundwater dynamics. However, very few studies have looked at seismic velocity variations at the scale of a single steep topography unit. Here, we estimate velocity variations from six stations covering a distance of 3.5 km on a single mountain ridge in the county of Hualien, Taiwan. One station was placed at the top of a ridge (900m elevation), two at the mid-slope of the topography and two others at the bottom (200m elevation), near the river banks. The aim is twofold: Determining how homogenous these velocity changes are and understanding the possible impact of topography on groundwater variations in a mountainous setting. Results from auto-correlations and cross-correlations are compared with meteorological data and other geophysical analysis. We identify the average hydrological dynamics of the ridge unit and connect the residual velocity changes to local site characteristics and upstream weather conditions.

How to cite: Illien, L., Kuehn, J., Andermann, C., and Hovius, N.: Monitoring groundwater dynamics in a mountain ridge using seismic interferometry: Influence of topography, local subsurface structure and meteorological conditions., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9608, https://doi.org/10.5194/egusphere-egu24-9608, 2024.

14:55–15:05
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EGU24-7576
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ECS
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On-site presentation
Selina Wetter, Clément Hibert, Anne Mangeney, and Eléonore Stutzmann

The Greenland ice sheet, a critical component of the global climate system, has played a substantial role in rising sea level, marked by a fourfold increase in mass loss due to iceberg calving between 1992-2000 and 2000-2011. Through the quantification of the spatio-temporal changes in Greenland’s ice mass loss resulting from iceberg calving, we gain a deeper understanding of the impacts of climate change.

The mass loss related to calving icebergs can be estimated by combining mechanical simulation of iceberg calving and inversion of seismic data. Seismic signals are generated by the time-varying force produced during iceberg calving on marine-terminating glacier termini. These events, known as glacial earthquakes, are recorded by the Greenland Ice Sheet Monitoring Network at tens of kilometres from the source.

However, differentiating these signals from tectonic events, anthropogenic noise, and other natural noise is challenging due to their complex frequency content (1-100s), multi-phase waveforms and low amplitude. To overcome this difficulty, we use a detection algorithm based on the Short-Time Average over Long-Time Average (STA/LTA) method and combine it with machine learning (Random Forests). By training the machine learning algorithm on seismic event catalogues containing more than 400 earthquakes and glacial earthquakes each, our approach is apt for identifying glacial earthquakes. Applying this methodology to continuous data offers the possibility to uncover smaller and previously undetected events. As a result, we present a comprehensive catalogue spanning several years and discuss its relevance and reliability. The generated catalogue allows us to develop new methods to better understand the spatio-temporal evolution of the ice-calving activity in the region. Among these, we will initially focus on locating and inverting the force of the largest events, providing a basis for testing new machine learning approaches for the characterisation of the source. This includes extracting properties like the iceberg volume and shape from both large and smaller events, ultimately advancing our understanding of Greenland's ice mass loss dynamics.

How to cite: Wetter, S., Hibert, C., Mangeney, A., and Stutzmann, E.: Constructing a New Catalogue of Greenland's Iceberg Calving Events through Seismic Data Analysis and Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7576, https://doi.org/10.5194/egusphere-egu24-7576, 2024.

15:05–15:15
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EGU24-8208
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ECS
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On-site presentation
Shun Zhao, Zheyi Cao, Yuanyuan Gu, Chen Lv, Zhitu Ma, Tong Hao, Gang Qiao, Benfeng Wang, and Rongxing Li

Icequakes are closely associated with glacier movement and rupture, and their temporal and spatial distribution patterns can portray the dynamics of glaciers. In this study, we used the seismic data recorded by 34 short-period Smartsolo seismometers deployed in Dålk Glacier, East Antarctica for about 60 days to detect and locate icequakes. The array was deployed at the edge of the Dålk Glacier and across the grounding line previously generated by satellite observations. The recorded data were strongly affected by Antarctica storms and we selected two days with little wind noise for preliminary analysis. Using time-frequency analysis and particle motion, we found that the seismic events are either dominated by body waves or surface waves, which likely correspond to deep icequakes or near-surface crevasse icequakes. Since the propagation of surface waves is easier to analyze and possible detections of crevasse icequakes are more likely to be verified from satellite images, we chose to focus on surface wave signals in this preliminary analysis. We first filtered records to 5-20 Hz and manually examined records with clear surface wave arrivals. We then produced templates using these events to scan through our records. We successfully identified 89 events within the two-day period. Lastly, these signals were located using a grid-search approach for their latitudes and longitudes, together with an average group velocity for each event. Nearly half of the incidents were concentrated on the edges of rock outcrops, which suggests they were generated by the relative movement between the glacier and outcrops. The other half of the events was found in the eastern region, where a large number of surface crevasses were observed on satellite imagery. In addition, the optimal velocity from the grid search is ~2.8 km/s for events from the North and West, while the optimal velocity for events from the East is ~1.8 km/s. The difference in wave velocity suggests the existence of a boundary between rock and ice at a depth of about 100-150m within or near our seismometer array. By analyzing the amplitude variations of incidents in different directions recorded at various stations, we observed that this boundary is within our array and its location and geometry can be estimated. Compared to the grounding line predicted from satellite observations, our result shows that the boundary is offset to the East by ~100 m. The reason for this discrepancy will be further discussed in the meeting.

How to cite: Zhao, S., Cao, Z., Gu, Y., Lv, C., Ma, Z., Hao, T., Qiao, G., Wang, B., and Li, R.: Icequake source location using seismic data in Dålk Glacier, East Antarctica, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8208, https://doi.org/10.5194/egusphere-egu24-8208, 2024.

15:15–15:25
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EGU24-6218
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ECS
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On-site presentation
Tifenn Le Bris, Guilhem Barruol, Florent Gimbert, Emmanuel Le Meur, and Dimitri Zigone

Cryoseismology, which records ice-induced seismic activity, is emerging as a powerful tool for studying the grounding zone - a critical spatio-temporal area where outlet glaciers grounded on the continent starts floating and interacting with the ocean underneath. The SEIS-ADELICE project supported by the French Polar Institute (IPEV) aims to characterise the dynamics of the Astrolabe glacier in Terre Adélie (East Antarctica), from its grounded part to its terminus in the ocean. Over the past 3 years, we deployed broad-band seismometers both at the grounding zone and on stable ice around the glacier, along with ocean bottom seismometers (OBS) close to the glacier terminus. In January 2023, the recording system was complemented by a dense array of 50 seismic nodes over the grounding zone. This allowed us to cover spatial scales from metres to several kilometres, providing a high-resolution observation of tidal forcing on the floating tongue and its repercussions on the glacier behaviour. The seismic records contain a wide range of signals, including icequakes, accepted to result from the brittle deformation of the ice. Although the seismic patterns at the different stations show clear modulation of icequakes by tidal cycles, their phasing with the tide depends on the location of the sensors, whether they are grounded or floating and on their distance from the active part of the glacier. This highlights the importance of the network typology and its proximity to the grounding line when characterising icequake occurrence patterns. Local icequakes detected at the grounding line exhibit a consistent occurrence during both rising and falling tides, with the peak activity observed during high tide. Source location analysis reveals that events are distributed across both the grounding line and the lateral shear zones of the glacier which are under strong stress from the ice-ocean interactions during tides.

How to cite: Le Bris, T., Barruol, G., Gimbert, F., Le Meur, E., and Zigone, D.: High resolution observations of tide induced icequake activity at the Astrolabe glacier grounding zone, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6218, https://doi.org/10.5194/egusphere-egu24-6218, 2024.

15:25–15:35
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EGU24-6637
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On-site presentation
Toshiro Tanimoto

The EarthScope Transportable Array (TA) in Alaska has been a unique seismic network since about 2014 because most stations are equipped with environmental sensors to record pressure, temperature, and wind (speed and direction). We will summarize some physical insights of near-surface properties in Alaska that can be gained from the combined analysis of seismic and environmental sensors. We also point out a possible effect of the thick sea ice on the climate in the North Slope region that faces the polar ocean.

First, the combined analysis of seismic data and pressure data allows us to separate two distinct types of seismic noise; one is the ordinary seismic noise, consisting of propagating body and surface waves, and the other is the deformation caused by the local pressure loading. This loading effect is observed at many stations when surface pressure becomes high. It can be confirmed based on two pieces of evidence; one from high coherence between seismic and pressure data and the other from the phase difference between pressure and vertical seismic displacement. By selecting data from a high-pressure range, we can apply the compliance method, similar to the compliance method applied to ocean bottom observations (e.g., Webb and Crawford, 1998). We will show a map of shallow rigidity variations for the depth range of 50-100m.

Second, the combined analysis of temperature and seismic noise allows us to identify the major effects caused by near-surface melting, primarily in the permafrost area. Some stations show a thousand-fold increase of horizontal noise in summer at 0.01-0.03 Hz in comparison to the frozen state. This anomalous horizontal noise can be seen at low frequency (< 0.1 Hz) and is undoubtedly related to tilt effects as its amplitude increases towards lower frequency.

Third, seasonal variation in horizontal noise shows a rapid increase in summer due to melting but the way the noise level returns to the frozen (low-noise) state varies from station to station. For most stations, this return occurs well after the surface temperature becomes negative in September or October. But some stations require time until March of next year to return to the low noise level. These data suggest that the melt layer remains at depth for a long time even after temperature drops below freezing, perhaps developing a sandwiched molten layer between the developing ice from the surface and the underlying permafrost ice.

How to cite: Tanimoto, T.: New Perspectives on the Shallow Environment in Alaska from co-located seismic, pressure, temperature, and wind sensors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6637, https://doi.org/10.5194/egusphere-egu24-6637, 2024.

15:35–15:45
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EGU24-10347
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Highlight
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On-site presentation
Heiner Igel, Sophie Brass, Fabian Lindner, Koen Van Noten, Raphael de Plaen, Joachim Wassermann, Felix Bernauer, and Thomas Lecocq

In March 2023 the annual winter school SKIENCE (www.skience.de) was held in the Bavaria alps, south-east of Munich. The topic was environmental seismology with a focus on seismic monitoring using ambient seismic noise. The winter school had strong practical training aspects. Prior to the meeting 12 5Hz nodes (SmartSolo) were deployed in the valley near Bayrischzell with the goal to explore local structure and site effects using interferometric methods. During the midweek free afternoon the 12 SmartSolo nodes were installed on both sides of a slalom run with several gates through which participants of the winterschool skied one after each other. First inspection of the data showed that clear signals of the skiers could be identified. Here, we report on attempts to use the seismic data records to recover the tracks of the skiers as moving seismic sources. Questions associated with this experiment are at which points in the tracks seismic energy is generated, where exactly the incoming signals propagate and with what velocities, and how well the source locations can be backprojected. A simple theoretical model is used to develop the inversion tools to recover the moving sources.   

How to cite: Igel, H., Brass, S., Lindner, F., Van Noten, K., de Plaen, R., Wassermann, J., Bernauer, F., and Lecocq, T.: The seismic signature of skiing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10347, https://doi.org/10.5194/egusphere-egu24-10347, 2024.

Coffee break
Chairpersons: Janneke van Ginkel, Josefine Umlauft
Sediments & Rocks
16:15–16:35
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EGU24-13007
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solicited
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Highlight
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On-site presentation
Florent Gimbert, Maarten Bakker, Marco Piantini, Alain Recking, and Michael Lamb

The field of fluvial seismology has undergone significant advances over the past decade. The development of dedicated physical theories and their applications in various contexts have allowed separating the respective contributions of turbulent flow and bedload transport, such that physical parameters like flow depth and sediment flux may be inferred from seismic observations. However, the quantitative link between signal characteristics (amplitude, frequency) and the underlying physics yet involves simplified considerations that do not necessarily apply to more complex situations, such as for example under rough flow conditions or during extreme floods.

In this talk I will present results from laboratory experiments that we designed specifically in order to quantify the seismic signature of flow turbulence and intense bedload transport under a range of conditions using force sensors coupled to the river bed. On one hand, I will show that existing theory regarding turbulent flow properly captures the main characteristics of the seismic source, but that additional dependencies on flow conditions and particle-wake development need to be included for more accurate predictions. On the other hand, I will show that existing theory regarding bedload transport fails at capturing the main characteristics of the seismic source under intense bedload transport conditions associated with complex changes in internal flow dynamics. In this case the seismic source appears to be a decreased function of solid concentration, as opposed to an increased function such as considered in current theories, which we suggest is due to grain impacts being agitation-controlled rather than bed-roughness controlled. Finally, I will discuss possible ways towards building more generic theories of ground motion induced by sediment transport.  

How to cite: Gimbert, F., Bakker, M., Piantini, M., Recking, A., and Lamb, M.: Assessing the seismic signature of turbulent flow and intense bedload transport from designed laboratory experiments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13007, https://doi.org/10.5194/egusphere-egu24-13007, 2024.

16:35–16:45
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EGU24-13569
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On-site presentation
Wei-An Chao, Chi-Yao Hung, and Yu-Shiu Chen

Reliable bedload flux estimations are necessary for a variety of applications such as sedimentation engineering, flood risk mitigation and river restoration. Several seismic physical models with considering different bedload transport mechanisms have been proposed, which provided an opportunity to have quantitative observation in practical. However, a lack of direct measurements of bedload fluxes in field application cause a challenge for the validation of seismic models. In the practical application, the bedload impact kinematics (elasticity and velocity) and particle dynamics assumed in models are crucial for achieving high accuracy in bedload inversion. In-situ seismic parameters such as shear-wave velocity and seismic quality factor are also required to reduce the uncertainty in model prediction. Thus, this study first conducts bedload transport experiments in a flume laboratory to understand the kinematics and mechanics of particle transport by using the smart rock embedded with accelerometer and gyroscope, geophone and hydrophone. For the field-scale experiments, we further studied distributed acoustic sensing (DAS) measurement during the experiments, which can record the dynamic strain in fiber optic cable under riverbed. Both case of laboratory flume and field-scale experiments, we will evaluate the performance of the different physical models by comparing in-situ measurements of bedload mass and impact forces recorded by the smart rock. In the case of field experiment, we adopted the active and passive seismic surface wave exploration to investigate the properties of wave propagation and attenuation. The effect of the process of rolling and/or sliding particles, as opposed to saltating particles, contributing in seismic signal generation, was also explored.   

How to cite: Chao, W.-A., Hung, C.-Y., and Chen, Y.-S.: Validation of seismic bedload saltation model: From laboratory flume to field-scale experiments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13569, https://doi.org/10.5194/egusphere-egu24-13569, 2024.

16:45–16:55
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EGU24-20192
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ECS
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On-site presentation
Ron Nativ, Jonathan Laronne, Jens Turowski, Jui-Ming Chang, Ci-Jian Yang, Niels Hovius, Wen-Sheng Chen, and Wen-Yen Chang

Turbulent flows capable of mobilizing sediments, despite being studied over the past 100 years, continue to constitute an elusive process. In environmental seismology, seismic waves generated by the interplay of surface processes and the Earth offer a key to unraveling the dynamics of river processes. We studied the seismic signals emitted during floods in two tributaries with large boulders. Early findings indicated an unusually high dominant seismic frequency, reaching 2-4 times the frequency observed in nearby channels with smoother beds. Consistent anomalous high-frequency content during times without sediment transport prompts our hypothesis that turbulence is the key process driving the frequency shift. We hypothesized that the most energetic turbulent eddies, dominating the signal, decrease in size in response to the boulder-influenced constrained flow geometry, and we argue that this effect possesses a first-order control on the frequency shift. A frequency scaling law with boulder spacing, approximating boulder-induced eddy size, shows good agreement with our field data. The dynamics of the eddies under changing flow velocity are well predicted by a power law function of seismic frequency with water depth. The trend breaks at the onset of bedload transport, indicating that energy is dissipated through the partitioning between turbulence and sediment transport. Our study emphasizes that seismic frequency effectively records the dominant morphology and fluvial processes, revealing the intricate interaction between roughness and seismic energy.

How to cite: Nativ, R., Laronne, J., Turowski, J., Chang, J.-M., Yang, C.-J., Hovius, N., Chen, W.-S., and Chang, W.-Y.: Boulder-induced Turbulence Drives Shift in Seismic Frequency, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20192, https://doi.org/10.5194/egusphere-egu24-20192, 2024.

16:55–17:05
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EGU24-5821
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On-site presentation
Paula Koelemeijer, Rudolf Widmer-Schnidrig, Kristian Svennevig, Stephen Hicks, Thomas Forbriger, Thomas Lecocq, Anne Mangeney, Clément Hibert, Niels Korsgaard, Antoine Lucas, Claudio Satriano, Robert Anthony, Aurélien Mordret, Sven Schippkus, Søren Rysgaard, Wieter Boone, Steven Gibbons, Kristen Cook, Sylfest Glimsdal, and Finn Løvholt and the VLPGreenland team

We report the discovery of an unprecedented, monochromatic low-frequency seismic source arising from the fjords of North-East Greenland. Following a landslide and tsunami event in Dickson fjord on 16 September 2023, the seismic waves were detected by broad-band seismometers worldwide. Here we focus on a detailed analysis of the long-period seismic signal, while a reconstruction of the dynamics of the landslide is presented by Svennevig et al. in session NH3.5. 

Both frequency and phase velocity of the waves are consistent with fundamental mode Rayleigh- and Love-waves. However, the decay rate of these waves is much slower than predicted for freely propagating surface waves so that we infer a long-lasting and slowly decaying source process. Although the 16 September 2023 event was by far the largest, analysis of historical seismic data has revealed five other previously undetected events, all with a fundamental frequency between 10.85 and 11.02 mHz. The signal of the largest two events initially decayed with a quality factor, Q close to Q=500, which increased to Q=3000 within the first 10 hours and could thus be detected for up to nine days. The smaller four events had a slow decay-rate (Q>1000) for their entire duration. In comparison, the global average attenuation of Rayleigh waves at these frequencies is Q=117 for PREM, thus precluding a single, impulsive source for these signals.

Gleaning archives of optical and SAR satellite images reveals that at least four out of six events could be associated with landslides in Dickson fjord, the two others remain unresolved. However, such rapid transient events cannot explain the long duration of the radiated seismic waves. Our modelling of the largest event shows that a transversal seiche in Dickson fjord, excited by a landslide induced tsunami, can account for both the monochromatic low frequency signal as well as its seismic signal amplitude and radiation pattern. However, the seiche modelling results in Q values lower than 250 and hence the seiche needs to be continuously driven for the entire duration of the observed seismic signal. Thus, a full understanding of the source process that produces the monochromatic signal remains enigmatic.

How to cite: Koelemeijer, P., Widmer-Schnidrig, R., Svennevig, K., Hicks, S., Forbriger, T., Lecocq, T., Mangeney, A., Hibert, C., Korsgaard, N., Lucas, A., Satriano, C., Anthony, R., Mordret, A., Schippkus, S., Rysgaard, S., Boone, W., Gibbons, S., Cook, K., Glimsdal, S., and Løvholt, F. and the VLPGreenland team: Global observations of an up to 9 day long, recurring, monochromatic seismic source near 10.9 mHz associated with tsunamigenic landslides in a Northeast Greenland fjord, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5821, https://doi.org/10.5194/egusphere-egu24-5821, 2024.

17:05–17:15
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EGU24-1972
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ECS
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Highlight
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On-site presentation
Eldert Fokker, Elmer Ruigrok, and Jeannot Trampert

Shallow soft sedimentary layers overlaying harder bedrock are known to amplify ground motion generated by earthquakes. Such an amplification occurs when seismic waves travel from high impedance (density times wave speed) to low impedance layers. Large impedance contrasts can lead to substantially larger earthquake damages. As the impedance contrast determines the amplification factor, variations in shallow shear-wave speed contribute directly to changes in site amplification.

Seasonal temperature fluctuations have been shown to induce shear-wave speed variations and, hence, affect site amplification factors. This naturally leads to the question: is the strength of earthquake damage season dependent? In this study we model by how much seasonal temperature variations affect site amplification. The site-specific physical properties determine whether site amplification is more pronounced during summer or winter. For parameters from the Groningen region of the Netherlands, affected by the gas extraction induced seismicity, we expect in the summer a relative increase in amplification of 8% with respect to the amplification factor in the winter.

How to cite: Fokker, E., Ruigrok, E., and Trampert, J.: Do earthquakes cause more damage in the summer?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1972, https://doi.org/10.5194/egusphere-egu24-1972, 2024.

17:15–17:25
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EGU24-15794
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ECS
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Highlight
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On-site presentation
Charlotte Bruland, Andreas Köhler, Anna Maria Dichiarante, Volker Oye, and Ivan Van Bever

Some of the more densely populated areas in Norway are in potential quick clay zones. When disturbed, the structure of quick clay can suddenly collapse, and behave and flow as a liquid, potentially having disastrous impact over large areas One of the triggering factors for quick clay slides is heavy rainfall. Here, we focus on passive seismic data from two Raspberry shake sensors located in an urban area in Oslo, Norway with quick clay in the subsurface. Using coda wave interferometry, near-surface velocity variations are estimated during the extreme weather ”Hans” (August 2023).

We compute auto-correlations and single station cross-correlations of anthropogenic seismic noise (> 1 Hz) over a two-year period leading up to ”Hans”. We observe environmental velocity fluctuations well correlated with air temperature, precipitation and the water level in a nearby river. In particular, freezing and thawing produces strong changes in seismic velocity (up to 4 %). Disregarding freezing, we see the largest change in seismic velocity following the heavy rainfall associated with ”Hans”. This extreme event is associated with a sharp velocity drop anti-correlated with pore pressure. The surface wave-coda is sensitive to changes in shear wave velocity, which in turn can be used to detect changes of the subsurface properties. Therefore, observed velocity variations at the site could have potential for monitoring and early warning of quick clay instabilities.

How to cite: Bruland, C., Köhler, A., Dichiarante, A. M., Oye, V., and Van Bever, I.: Monitoring subsurface changes in a quick clay area during extreme weather, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15794, https://doi.org/10.5194/egusphere-egu24-15794, 2024.

17:25–17:35
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EGU24-15165
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On-site presentation
Juliane Starke, Laurent Baillet, Eric Larose, Antoine Guillemot, and Laurence Audin

Rainfall, temperature variations, and chemical processes are well-known drivers of rock erosion. The impact of rainfall on rocks is not well-understood yet but may impact the mechanical properties (including damage, rigidity, deformation) of the rock. In this study, we exhibit the effect of rainfall events on the resonance frequency of a rock column.

Resonance frequencies of structures have been utilized to monitor rock columns due to their sensitivity to changes in the rock apparent rigidity (1). For instance, daily temperature changes induce stress variations in the rock column, resulting in a daily cycle of resonance frequency changes (thermal-acousto-elasticity, 2).

This research involves long-term monitoring of the first resonance frequency of a 50 m high limestone cliff covering the Chauvet cave in the Ardèche plateau, SW France, exposed to climatic solicitations including daily solar radiation, air temperature fluctuations, and rain events. The rock column was equipped with seismic and meteorologic stations and monitored continuously during three years.

To demonstrate the effect of rainfall events on the mechanical properties of the rock, we calculated the resonance frequency depending only on air temperature and solar radiation, using a simple bivariate linear regression. The regression provides well-fitting results for dry periods but shows larger deviations during most rainy periods. This indicates that rain has an effect on the changes in rock resonance frequency. Identifying and quantifying these changes would be a key factor in understanding the evolution of damage.

 

1) Bottelin, P., Baillet, L., Larose, E., Jongmans, D., Hantz, D., Brenguier, O., ... & Helmstetter, A. (2017). Monitoring rock reinforcement works with ambient vibrations: La Bourne case study (Vercors, France). Engineering Geology, 226, 136-145.

2) 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.

How to cite: Starke, J., Baillet, L., Larose, E., Guillemot, A., and Audin, L.: Investigating Rainfall-Driven Resonance Frequency Changes in a Natural Rock Formation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15165, https://doi.org/10.5194/egusphere-egu24-15165, 2024.

17:35–17:45
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EGU24-3399
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ECS
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On-site presentation
Qi Zhou, Jens turowski, Hui Tang, Clément Hibert, Małgorzata Chmiel, Fabian Walter, and Michael Dietze

Machine learning can improve the accuracy of detecting mass movements in seismic signals and extend early warning times. However, we lack a profound understanding of the limitations of different machine learning methods and the most effective seismic features especially for the identifcation of debris flows. This contribution explores the importance of seismic features with Random Forest and XGBoost models. We find that a widely used approach based on more than seventy seismic features, including waveform, spectrum, spectrogram, and network metrics features, suffers from redundant input information. Our results show that six seismic features are sufficient to perform binary debris flow classification with equivalent or even better results., e.g., the Random Forest and XGBoost models achieve improvements over the benchmark of 0.09% and 1.10%, respectively, when validated on the ILL12 station. Considering models that aim to capture patterns in sequential data rather than information in the current time window, using the Long Short-Term Memory algorithm does not improve the binary classification performance over Random Forest and XGBoost models. However, in the early warning context, the Long Short-Term Memory model performs better and more consistently detects the initiation of debris flows. Our proposed framework simplifies seismic signal-driven early warning for debris flows and provides a proper workflow that can be used for detecting also other mass movements.

How to cite: Zhou, Q., turowski, J., Tang, H., Hibert, C., Chmiel, M., Walter, F., and Dietze, M.: Enhancing debris flow warning through seismic feature selection and machine learning model comparison, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3399, https://doi.org/10.5194/egusphere-egu24-3399, 2024.

17:45–17:55
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EGU24-6613
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On-site presentation
Yajing Liu, Eva Goblot, and Alexandre Plourde

The Lower St. Lawrence Seaway (LSLS) is part of a major marine shipping corridor in eastern Canada, and also an essential feeding ground for fin whales and blue whales. Understanding the whale migration and habitat usage in the LSLS is critical for informing conservation policies that minimize noise pollution and risk of collision to the whale populations. In this study we utilize continuous recordings of six broadband seismometers located on the north and south shores of the St. Lawrence River to characterize the frequency range, recurrence interval and duration of fin and blue whale calls. We further use the whale call detections to quantify their spatial and temporal variations along the LSLS between February 2020 and January 2022, with the caveat that the detection range at these land stations is probably limited to a few kilometers due to energy loss along the seismic wave travel paths through multiple interfaces. We identified higher whale call detection rates at stations near the northwest of St. Lawrence Gulf than the upstream Estuary, suggesting possible influences of ocean currents and ice conditions. Whale calls are detected year around, with majority in the fall/winter months (September to February), implying seasonal and annual variations that may be influenced by climate change. We are currently analyzing recordings from a temporary deployment of 48 nodal seismometers, at 10-km average spacing, along the shorelines of the LSLS between September-October 2023, to further quantify the spatial patterns of whale calls and identify possible linkages to coastal bathymetry, ocean currents and preferential diets for the baleen whales.

How to cite: Liu, Y., Goblot, E., and Plourde, A.: Tracking baleen whale calls in the Lower St. Lawrence Seaway, Canada, using land seismometers  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6613, https://doi.org/10.5194/egusphere-egu24-6613, 2024.

Posters on site: Thu, 18 Apr, 16:15–18:00 | Hall X4

Display time: Thu, 18 Apr 14:00–Thu, 18 Apr 18:00
Chairpersons: Janneke van Ginkel, Małgorzata Chmiel, Michael Dietze
X4.171
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EGU24-13798
Richard Gamez and Lei Zou

Solid Earth Sciences:SE07 Faults and Earthquakes: Networks, Precursors, Monitoring Systems and Numerical Modelling Techniques

Research on new methods and equipment for seismological monitoring of glaciers on the Qinghai-Tibet Plateau

Lei Zou1, Richard Games2, ……

1 SmartSolo Inc., China

2 SmartSolo Inc., Huston, USA

Abstract: Glacier seismology combines the advantages of glaciology and seismology to form a young interdisciplinary subject. Icequakes are vibrations produced during the movement and breakup of glaciers, ranging from small squeaks to sudden ruptures or slides equivalent to earthquakes (MW7). According to the location and mechanism of icequake occurrence, icequakes can be divided into five types: surface fissures, stick-slip movement, iceberg calving, subglacial flow, and hydraulic fracturing. In addition to traditional seismological methods, icequake research can also be conducted using multidisciplinary methods such as GPS, numerical simulation, and glacier physical properties. Icequake research can further explore the occurrence process and risk assessment of ice avalanches. We review advances in glacier seismology.

Our users use SmartSolo scientific instruments to successfully analyze ice avalanche events through vibration signals by observing multi-parameter glacier environment and climate changes, combined with seismological observation instruments. Provide a new and effective monitoring method for glacier seismic monitoring. It enriches the process observation and risk assessment methods of ice avalanche occurrence, and the combination of multiple parameters further improves the accuracy and effectiveness of ice avalanche event monitoring.

How to cite: Gamez, R. and Zou, L.: Research on new methods and equipment for seismological monitoring of glaciers on the Qinghai-Tibet Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13798, https://doi.org/10.5194/egusphere-egu24-13798, 2024.

X4.172
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EGU24-3861
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ECS
Janneke van Ginkel, Ana Nap, Adrien Wehrlé, Fabian Walter, and Martin Lüthi

Sermeq Kujalleq in Kangia, also known as Jakobshavn Isbræ, a major outlet glacier of the Greenland Ice Sheet, exhibits a flow speed higher than 30 m/day near the terminus. Basal sliding, iceberg calving, and subglacial hydraulics play pivotal roles in ice flow dynamics of this outlet glacier, and understanding these processes is crucial for predicting the impact of outlet glaciers on the Earth system in a changing climate.

 Seismic and geophysical field campaigns were conducted in 2021, 2022 and 2023 in the region of Sermeq Kujalleq in Kangia. The project has the aim to monitor the dynamic behavior of such a fast-flowing outlet glacier and its interaction with the surrounding shear margins. Shallow borehole seismic sensors and self-sufficient seismic boxes were deployed in multiple arrays on the fast-moving ice stream and its margin. The sensors capture seismic sources and monitor subglacial conditions and spatiotemporal variabilities throughout the ice mass. An on-rock broadband seismometer near the terminus records iceberg calving activity ideally complementing observations of a Terrestrial Radar Interferometer operating simultaneously.

 Here we report on first results of a seismic analysis that provides insights into details of ice dynamic variations of Sermeq Kujalleq. Power spectrograms of the 2023 upstream arrays feature a 4-day tremor-like signal between 2.5 and 6 Hz. This phenomenon was not observed for other calving events and was missing in the 2022 record. Beamforming techniques are employed to constrain the source location of this tremor as well as other seismic events. Potentially this multi-day tremor signal corresponds to the ice stream response to a major calving event. Additionally, beamforming and spectral analysis provide insights into hydraulic cycles of the glacier, such as widespread diurnal water drainage and the activity of moulins. By comparing these seismic observations with ice flow speed and satellite images we aim at understanding the details of short-term perturbations to ice flow, which may influence larger-scale ice stream dynamics.

How to cite: van Ginkel, J., Nap, A., Wehrlé, A., Walter, F., and Lüthi, M.: Capturing the short-term dynamics of outlet glaciers:  insights from seismic monitoring on Sermeq Kujalleq in Kangia, Greenland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3861, https://doi.org/10.5194/egusphere-egu24-3861, 2024.

X4.173
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EGU24-10147
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ECS
Johanna Zitt, Patrick Paitz, Fabian Walter, and Josefine Umlauft

One major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a multitude of environmental processes. Specifically, basal sources such as stick-slip events often stay unnoticed due to long travel paths to surface sensors and accompanied wave attenuation. Yet, stick-slip events play a crucial role in understanding glacier sliding and therefore, it is of great interest to investigate their spatio-temporal evolution, across the entire glacier from its ablation to its accumulation zone.
Distributed Acoustic Sensing (DAS) is a technology for measuring strain rate by using common fiber-optic cables in combination with an interrogation unit. This technology enables us to acquire seismic data over an entire glacier with great spatial and temporal resolution. To unmask stick-slip events, new techniques are required that effectively and efficiently denoise large cryoseismological DAS data sets. 
Here, we propose an autoencoder, a type of deep neural network, which is able to separate the incoherent environmental noise from the temporally and spatially coherent signals of interest (e.g., stick-slip events or crevasse formations). We trained the autoencoder in order to denoise a DAS data set acquired on Rhonegletscher, Switzerland, in July 2020. Due to the highly active and dynamic cryospheric environment as well as non-ideal cable-ground coupling the collected DAS data are characterized by a low signal to noise ratio compared to classical point sensors.
Several models were trained on a variety of data subsets, differing in recording positions (ablation or accumulation zone), event types (stick-slip event or surface event) and the quantity of training events. We compare and discuss the denoising capabilities of these models with several metrics, such as inter-channel coherence, similarity between seismometer and DAS recordings, and visual assessment. This evaluation is conducted while considering different data types in a qualitative and quantitative manner. All models show an increase in inter-channel coherence of the seismic records after denoising. Further, all models uncover previously undetected stick-slip events, whereby models trained on manually picked training data perform better than models trained on randomly picked training data. We believe that the application of our models can improve the understanding of basal stick-slip information in cryoseismological DAS datasets, potentially uncovering previously hidden information.

How to cite: Zitt, J., Paitz, P., Walter, F., and Umlauft, J.: Uncovering Stick-Slip Events: Denoising Cryoseismological Distributed Acoustic Sensing Data with an Autoencoder, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10147, https://doi.org/10.5194/egusphere-egu24-10147, 2024.

X4.174
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EGU24-9822
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ECS
Ana Nap, Fabian Walter, Martin P. Lüthi, Adrien Wehrlé, Janneke van Ginkel, Andrea Kneib-Walter, and Hugo Rousseau

In traditional glacier flow laws and consequently glacier models, a widely used assumption is that the ice behaves as a non-Newtonian viscous fluid that slides either across hard bedrock or via deforming subglacial till. Elastic effects and brittle deformation within the ice are often neglected for simplicity, as even ubiquitous surface crevasses are difficult to capture in numerical schemes. While there is ample seismological evidence that stick-slip motion plays a significant role in basal sliding of both alpine and polar glaciers, similar evidence is lacking for brittle deformation within the ice mass itself. Instead, it is commonly assumed that the ice moves and deforms in a purely viscous or ductile manner, which may not be an accurate representation of reality.

Here, we present observations of high-frequency (>50Hz) signals of intermediate-depth seismic sources occurring along the fast ice-stream of Sermeq Kujalleq in Kangia (Jakobshavn Isbræ), Greenland’s fastest flowing outlet glacier. The waveform characteristics of these events closely resemble the known characteristics of waveforms associated with basal stick-slip events, making them easily distinguishable from the more prevalent icequake signals generated by surface crevasse opening and propagation. However, differences in P and S wave arrival times as well as probabilistic source locations show that these events occur at ~170-400 m depth, whereas at those locations the glacier has a total depth of approximately 2000 m. Hence, these events cannot be caused by stick-slip motion at the base of the glacier, but must originate from englacial dislocations such as e.g., thrust faulting. Hundreds of these englacial icequakes are observed at several seismic arrays that were temporarily deployed in 2022 and 2023 along the fast ice-stream of Sermeq Kujalleq. Using waveform clustering and source mechanism analysis, we discuss the role of these events in ice dynamics and in particular englacial deformation.

 

How to cite: Nap, A., Walter, F., Lüthi, M. P., Wehrlé, A., van Ginkel, J., Kneib-Walter, A., and Rousseau, H.: Intermediate-depth icequakes at Greenland’s fastest outlet glacier: evidence for englacial thrust faulting?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9822, https://doi.org/10.5194/egusphere-egu24-9822, 2024.

X4.175
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EGU24-10525
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ECS
Julia Peters, Felix Roth, and Josefine Umaluft

Cryoseismological records consist of numerous signals generated by various sources within or surrounding glacial ice, including icequakes, water flow, avalanches, rockfalls, wind, or precipitation. This results in a notably high noise level within the data, posing a significant challenge in detecting and distinguishing individual seismic events and sources.

Our research employs Deep Embedded Clustering (DEC) to address this challenge, focusing on the analysis of a Distributed Acoustic Sensing (DAS) dataset acquired on Rhonegletscher (Switzerland) in 2020.

To visualize and efficiently streamline the DEC processing of this substantial volume of data, we reorganize the numerous continuous DAS channels as a 3D data cube featuring the three dimensions: time, space, and frequency. The DEC approach involves first transforming high-dimensional seismic data into a more manageable lower-dimensional latent space using an autoencoder. This transformation is vital in emphasizing the essential characteristics of the data, thereby enabling more effective clustering. Subsequently, the DEC algorithm autonomously categorizes these seismic signals into distinct clusters based on their unique spatio-temporal characteristics, without the prerequisite of manual annotation.

The primary aim of this approach is to utilize DEC for the effective mapping of clearly defined spatio-temporal clusters within cryoseismological records. This approach is geared towards achieving a more nuanced understanding of the various sources contributing to these records and their complex dynamics. By successfully segregating these clusters, the aim is to reveal new insights into the complex processes and interactions in glacial environments.

Both the DAS data and the clustering results can be explored interactively using the data cube viewer Lexcube. Come find us at the poster stand!

How to cite: Peters, J., Roth, F., and Umaluft, J.: Deep Embedded Clustering of a Cryo-Data-Cube, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10525, https://doi.org/10.5194/egusphere-egu24-10525, 2024.

X4.176
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EGU24-480
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ECS
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Rebeca Ursu, Mark Naylor, Hui Tang, and Jens M. Turowski

During rockfall events, the seismic waves are generated in response to the time-varying normal and tangential forces between the Earth and colliding and sliding mass. These forces carry information about the nature of the generative seismic source; hence, the source dynamics can be estimated. Several studies have used forward modeling to determine the amplitude and duration of these forces and, implicitly, the source process that could generate the observed seismic waves. Through running multiple forward models, the force history inversion involves adjusting the force amplitude and duration to minimize the misfit between the proposed source model, convoluted with the force-impulse Green’s functions, and the observations. In the Bayesian framework, the normal likelihood function is traditionally used to measure the misfit between the observed and predicted waveforms with respect to amplitude. However, the normal likelihood function is insensitive to the potential misalignment of the waveforms in time. Moreover, the relevant parameter space often exhibits multiple local minima, which may lead to a convergence to a minimum that does not present the global optimum. Optimal transport distances-driven exponential likelihoods were recently proposed as alternatives thanks to their ability to capture the time structure of the signals. We employed a Metropolis-Hastings sampling strategy in the probabilistic framework to reconstruct the 2012 Palisades rockfall seismic source using two implementations of the Wasserstein distance-based exponential likelihood function. The first implementation transforms between density functions, which are always positive and integrate to one. Therefore, it requires the transformation of the signals into probability density functions, which is done here via a modified graph-space transform scheme. The second method is applied directly to the signals. We evaluated the robustness of the two implementations of the Wasserstein distance-based exponential likelihood function in simulating the source characteristics with respect to the normal likelihood. Preliminary results show that contrary to the expectations, using optimal transport distances-driven exponential likelihoods leads to negligible improvement in the fit to the observed waveform.

How to cite: Ursu, R., Naylor, M., Tang, H., and Turowski, J. M.: Probabilistic optimal transport-driven inversion of the 2012 Palisades rockfall seismic source, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-480, https://doi.org/10.5194/egusphere-egu24-480, 2024.

X4.177
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EGU24-13208
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Highlight
Sibashish Dash, Michael Dietze, Fabian Walter, Marcel Fulde, Wandi Wang, Mahdi Motagh, and Niels Hovius

The early detection of slope instability and the monitoring of frequent hazard processes in mountainous regions is of paramount importance due to their sudden occurrence, and the risk of causing numerous fatalities and significant economic damage. The recent collapse of the Brienz/Brinzauls rockslide on June 15, 2023, in an active, deep-seated mountain slope deformation complex in Switzerland, provides a unique opportunity to investigate the evolution of precursors leading up to the collapse. Early identification of accelerating rockmass enabled us to set up a network of five broadband seismometers, strategically deployed to systematically record seismic signals in close proximity, reducing information loss due to attenuation of seismic waves. 

The internal rock damage dynamics in the displacing rock mass were interacting with external seasonal forcings, such as snow melt and rainfall, for years preceding the collapse at approximately 21:38:00 UTC on June 15, 2023. Seismic events of various types have been detected in the entire landslide complex, characterised by the recurrence of identical seismic events that aggregate prominently within the most rapid compartment, referred to as the "Insel," positioned directly above the village of Brienz. This study aims to investigate the influence of seasonal forcings on accelerating the rate of displacements and to understand how the nature of detected precursors changes over time. We systematically examine the feedback loop between seasonal triggers and gravity-driven internal rock damage under changing stress conditions during fluctuations in compartment velocity. Initially, events exhibit accelerations following periods of precipitation, but subsequently, a runaway acceleration in seismic events was noted even during dry periods. The locations detected reveal communication between the upper and lower parts of the “Insel” mass in the build-up to the main collapse. From June 1 onward, there is a consistent and gradual increase in the mean spectral power of the recurring seismic events, with a rapid escalation observed in the three days leading up to the collapse. Interestingly, on the final day preceding the main collapse, a significant decrease in the mean spectral power was identified. To complement seismic observations, the spatial and temporal changes in pre-failure slope instability for the period 05.2014-06.2023 were also analyzed using Sentinel-1 synthetic aperture radar (SAR) data using a multi-temporal interferometric (MTI) approach. MTI analysis indicates several patches of instability and surface deformation on the slope, along with signs of significant surface displacement of a few centimetres per year, also manifesting in the village of Brienz. To facilitate automatic detection and classification, we apply data science methods to various statistical seismic attributes of the identified precursors. This study contributes to advancing our understanding of the mechanisms leading to rockslide collapses, with the potential to significantly enhance warning system effectiveness.

How to cite: Dash, S., Dietze, M., Walter, F., Fulde, M., Wang, W., Motagh, M., and Hovius, N.: Analysis of Precursors and Collapse of June 15, 2023, Brienz/Brinzauls Rockslide in Switzerland: Integrating Seismic and Remote Sensing Observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13208, https://doi.org/10.5194/egusphere-egu24-13208, 2024.

X4.178
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EGU24-15365
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Jiahui Kang, Fabian Walter, Patrick Paitz, Johannes Aichele, Pascal Edme, Andreas Fichtner, and Lorenz Meier

Distributed Acoustic Sensing (DAS) represents a leap in seismic monitoring capabilities. Compared to traditional single-seismometer stations, DAS measures seismic strain at meter to sub-meter intervals along fiber-optic cables thus offering unprecedented temporal and spatial resolution. Leveraging the resolution of DAS enables us to monitor and detect seismogenic processes in the domain of hazardous mass-movements, including catastrophic rock avalanches.

Here, we present a semi-supervised neural network algorithm for screening DAS data related to mass movements at the Brienz landslide in Eastern Switzerland, which partially failed on 15 June 2023. A DAS interrogator connected to a 10 km-long dark fiber provided by Swisscom Broadcast AG near the landslide recorded seismic data from 16 May to 30 June 2023, with a sampling frequency of 200 Hz and a channel spacing of 4m. During a test period from June 1 to June 19, 2023, a total of 634 characteristic waveforms potentially related to slope failures, including the 15 June 2023 event, were detected, along with vehicle and other anthropogenic noise sources with characteristic diurnal and weekday/weekend variations.

For information extraction, we selected a subset of adjacent DAS channels, which include cable sections that were parallel to the failure event trajectory and thus particularly sensitive to mass movement activity. To facilitate efficient processing, we downsampled the data to 20 Hz, considering that slope failure events predominantly excite seismicity at below 10 Hz. We conceptualize the DAS data as a series of images representing consecutive strain rate data in the two dimensions of time and space. To bring out signal coherence between DAS channels, we transform the waveforms into cross-spectral density matrices (CSDM’s) which serve as the input image for unsupervised feature learning using an autoencoder (AE). Leveraging the features learned from the AE, we focus on activity classification using approximately 1500 samples. As ground truth for the slope failure class, we utilize concurrent Doppler radar data. The radar provides an event magnitude, which scales with failure volume and the number of individual rockfalls. Furthermore, the radar provides a measure of the moving mass’s trajectory length and front speed. The radar detected 516 slope failures during the test period.

Our algorithm captures 41.09 % of the slope failures recorded by the Doppler radar. The undetected events mainly have low radar magnitudes suggesting that they are associated with mass movements generating reduced seismic activity. Among the slope failure-type signals detected by DAS, 87.85% are also present in the radar catalogue. Interference from vehicle or human-triggered seismic waves, deteriorating the signal-to-noise ratio significantly, poses a challenge for our algorithm to differentiate between slope failures and those activities. Our study thus provides a benchmark for future natural hazard monitoring and suggests that using existing fiber optic infrastructure has a high potential for early warning purposes.

How to cite: Kang, J., Walter, F., Paitz, P., Aichele, J., Edme, P., Fichtner, A., and Meier, L.: Automatic Monitoring of Seismogenic Slope Failure Activity at Brienz (Switzerland) Using Distributed Acoustic Sensing and Semi-Supervised Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15365, https://doi.org/10.5194/egusphere-egu24-15365, 2024.

X4.179
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EGU24-11086
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ECS
Chi En Hi, Kate Huihsuan Chen, Wei Peng, Wan-Ru Huang, Hsiang Han Chen, Ko Chih Wang, and Kuo En Ching

Can we use environmental data to predict changes in surface displacement fields? Do severe weather events alter the near-surface geomechanical properties? The seasonal variations in GPS time series and crustal seismic velocities have been frequently observed at different study areas. Such variation has been tied closely to the cyclic hydrological loads [e.g., Costain et al., 1987; Heki, 2003; Roth et al., 1992], which its association with tectonic deformation remains debated. Using the 15 years meteorological, geodetic, and seismic data recorded in southern Taiwan (near Chaozhou fault where the background seismicity level is low), we aim to explore the possibility of predicting surface displacement and vibration using climatic variables (time series of temperature, precipitation, and wind velocity) and groundwater levels. Here the Support Vector Regression (SVR) model is developed for the prediction of the GNSS and seismic signals, while 15-yr datasets are divided into groups of 75%  and 25% datasets for model calibration and testing. When the predicted surface displacement is compared with the real data, the R-square values reach 95%, indicating the applicability of SVR model on long-term surface deformation prediction. In the future, long-term prediction model will be conducted to target several extreme weather events in Taiwan.

How to cite: Hi, C. E., Chen, K. H., Peng, W., Huang, W.-R., Chen, H. H., Wang, K. C., and Ching, K. E.: Support vector regression-based model for the prediction of surface displacement and vibration using meteorological data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11086, https://doi.org/10.5194/egusphere-egu24-11086, 2024.

X4.180
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EGU24-12879
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ECS
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Highlight
Array seismologic insights into Skaftá jökulhlaups (GLOFs), Iceland, 2014-2016.
(withdrawn)
Thoralf Dietrich, Eva P.S. Eibl, Finnur Pálsson, Eyjólfur Magnússon, Wolfgang Schwanghart, Matthias Ohrnberger, Sebastian Heimann, Fabian Lindner, and Sigrid Roessner
X4.181
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EGU24-13722
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ECS
Marco Piantini, Matthias Bonfrisco, Rudi Nadalet, Roberto Dinale, Gianluca Vignoli, Gianluca Antonacci, Silvia Simoni, Fabrizio Zanotti, Stefano Crema, Marco Cavalli, Alessandro Sarretta, Velio Coviello, and Francesco Comiti

Bedload transport plays a key role in the morphodynamics of mountain rivers by regulating erosion and aggradation processes. However, it is still challenging to estimate and predict bedload transport rates with reliability because of a complex interplay between different types of sediment supply, hydrological forcing, and fluvial morphologies. In the last two decades, passive sensors recording the seismic signals generated by coarse particles impacting the riverbed have been proposed to provide a continuous indirect measure of bedload transport. Among them, geophone plates and seismometers have been demonstrated to be valid tools.

Here, we present the preliminary results from the new monitoring station of Stilfserbrücke/Ponte Stelvio designed and built to monitor both water and sediment fluxes in the Solda River (Italian Alps). The station, mainly financed through two ERDF 2014-2020 projects of the Autonomous Province of Bolzano South-Tyrol, is part of the operational gauging network of the Civil Protection Agency of Bolzano (Italy). Bedload transport is indirectly monitored by sixteen geophone plates covering the downstream side of a consolidation check dam. The signal associated with the vibrations generated by particle impacts on the steel plates is recorded continuously with a sampling frequency of 5 kHz. In order to calibrate the instruments, direct bedload measurements have been carried out through an innovative bridge-like structure (BLS) consisting of an electronically controlled mobile trap. The collected samples have been sieved by hand to characterize their grain size distribution. At the end of summer 2023 we have also explored the possibility to additionally monitor the river with seismometers installed on the left bank at the monitoring station. We have analyzed the signal from the geophone plates by counting the number of times its amplitude exceeds a preselected threshold expressed in volts (i.e. the impulses, Rickenmann et al., 2014), and by computing its power (Coviello et al., 2022). The best correlation is found between impulses (threshold of 0.04 V) and the bedload transport rates of particles larger than 22 mm, with a power law regression characterized by a coefficient of determination (R2) of 0.85 and a low root mean square error (RMSE) of 3.3 kg/min against peak bedload transport rates reaching 41 kg/min.

These findings pave the way towards ensuring the continuous quantification of coarse sediment transport in the Solda River, allowing for the evaluation of the impact of glacier retreat and slope instabilities associated with global warming on river dynamics. Finally, the simultaneous use of seismometers may provide a unique opportunity to test existing theoretical models on bedload-induced ground vibrations through the indirect measurements provided by the geophone plates.

References

Coviello, V., Vignoli, G., Simoni, S., Bertoldi, W., Engel, M., Buter, A., et al. (2022). Bedload fluxes in a glacier-fed river at multiple temporal scales. Water Resources Research, 58, e2021WR031873.

Rickenmann, D., Turowski, J.M., Fritschi, B., Wyss, C., Laronne, J., Barzilai, R., Reid, I., Kreisler, A., Aigner, J., Seitz, H. and Habersack, H. (2014), Bedload transport measurements with impact plate geophones: comparison of sensor calibration in different gravel-bed streams. Earth Surf. Process. Landforms, 39: 928-942.

How to cite: Piantini, M., Bonfrisco, M., Nadalet, R., Dinale, R., Vignoli, G., Antonacci, G., Simoni, S., Zanotti, F., Crema, S., Cavalli, M., Sarretta, A., Coviello, V., and Comiti, F.: Investigating bedload transport in mountain rivers through seismic methods: the new monitoring station in the Solda River (South Tyrol, Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13722, https://doi.org/10.5194/egusphere-egu24-13722, 2024.

X4.182
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EGU24-16318
Analysis of debris-flow dynamics based on seismic signals: Insights from laboratory experiments and field monitoring
(withdrawn after no-show)
Yifei Cui, Xinzhi Zhou, Yan Yan, and Hui Tang
X4.183
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EGU24-18262
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ECS
Zheng Chen, Alexandre Badoux, and Dieter Rickenmann

Predicting bedload transport is a key element of water-related hazard assessment and hydraulic engineering applications. However, knowledge of bedload transport processes remains limited, particularly in steep mountain streams. Previous studies have revealed that bedload transport rates in mountain streams exhibits a large spatio-temporal variability for given flow conditions. This results from the direct influence of streambed structure on bedload transport, where sediment movement, in turn, interacts with streambed evolution. Furthermore, variations in sediment availability contribute to the spatio-temporal bedload variability. The complex interactions between water flow, bedload transport, and bed structure are not yet fully understood. In this work, systematic flume experiments were conducted to investigate the acoustic signal responses of impact plate geophone systems generated by bedload particles impacting on the flume bed during experimental flows in the transitional regime. The experiments varied in the grain size distribution of the transported particles and the bed material, and the compactness and the water content of the flume bed. Geophones were installed on the underside of steel plates flush with the flume bed both upstream and downstream to effectively capture the changes in vibration signals generated by the moving bedload mass impacting on the bed. Triaxial force sensors were utilized to measure the impact forces of the bedload particles on the bed material layer. Pore-water pressure sensors were embedded at different depths in the bed material to measure the change in pore-water pressure in the bed under the influence of the bedload mass. Flow velocities and depths of the moving bedload mass were recorded using a binocular high-speed camera and were analyzed with an image processing method. The observed vibration signals and fluctuating forces were used to calculate the characteristic parameters of bedload transport using calibrated relationships and seismic theory. In addition, a high-precision Digital Elevation Model (DEM) of the bed was constructed using the photography and 3D modeling techniques. The results of this work show that geotechnical material parameters of the bed such as compactness, compression modulus, and grain size distribution may affect the changes of bed structure caused by bedload transport This in turn influences the spatio-temporal variability of the transport rate. The findings of this work may help to explain the variability of the bedload transport process in mountain streams.

How to cite: Chen, Z., Badoux, A., and Rickenmann, D.: Quantitative measurement of bedload transport variability with acoustic monitoring systems: Insight from controlled laboratory flume experiments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18262, https://doi.org/10.5194/egusphere-egu24-18262, 2024.

X4.184
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EGU24-5306
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ECS
Eva Wolf, Michael Dietze, and Stuart Lane

Bedload export from Alpine glaciers by rivers is a geomorphological process of increasing interest given the high retreat rates of temperate ice masses in the context of global warming. Access and measurement difficulties make it very poorly known and contradictory hypotheses exist about how it might respond to receding glaciers. In subglacial channels, bedload transport is a key mechanism for evacuating one of the products of glacial erosion. It likely constrains glacial erosion rates as removal of the products of erosion is needed so as to yield fresh bedrock for further erosion. Environmental seismology may be a valuable tool in understanding rates of subglacial bedload export.
Previous studies have considered subglacial bedload export in glacial forefields using seismic sensors and tracked particles moving underneath the ice sheet. We are taking former studies forward and extend the monitoring of bedload export detecting coarse grain impacts using seismometers right at the glacial terminus. The project aims to determine diurnal as well as seasonal sediment export quantities and compare results among different field sites.
We studied subglacial bedload export for the Otemma and Arolla glacier in Valais, Switzerland in the summer of 2023 by installing two seismic stations (PE-6/B geophones) close to each glacier terminus throughout the melt season. These four-month records of seismic signals were processed using fluvial inversion algorithms of the eseis package implemented in R. The algorithm is refined with wave propagation- and ground properties determined through active seismic experiments as well as measured grain size distributions from field sampling. We are able to separate turbulent water noise and bedload noise in the seismic signal and estimate water stage as well as bedload transport rates. Results are validated by comparing the water stage estimates to measurements from a discharge gauging station. Over a full season, we compare the behaviour of the two different glaciers regarding sediment export taking into account their size, orientation, elevation and other factors. We relate the detected bedload export events to meteorological conditions and shifts in seasonal melt processes from snow melt to ice melt.
The results of this study help to get a clearer picture of diurnal as well as seasonal patterns of bedload export from glaciers, impacting downstream riverbed erosion and deposition in the light of increasingly rapid glacier melt. These geomorphological processes are of interest for different infrastructural facilities such as hydropower plants.

How to cite: Wolf, E., Dietze, M., and Lane, S.: Quantifying snout marginal bedload export from alpine glaciers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5306, https://doi.org/10.5194/egusphere-egu24-5306, 2024.

X4.185
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EGU24-7490
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Highlight
Michael Dietze, Mateja Jemec Auflič, Sašo Petan, and Nejc Bezak

Excessive and sustained rainfall can trigger regional floods with a large propagation range. Their non-linear onset, rapid evolution and massive impact make prediction, mitigation and posteriour anatomy efforts difficult.

The atmospheric low “Petar” that struck Europe in early August 2023 was one drastic example of such flood triggering rain events. It was able to gain abundant moisture and heat over an exceptionally warm Mediterranean Sea, before it moved to continental Europe, crossing Slovenia, Austria, and Germany. It caused severe flooding as a result of locally more than 350 mm rain within less than two days. We focus on Slovenian examples, where the event was perceived the most devastating natural hazard in the last decades.

Here, we follow a seismic approach to study the spatially contrasting effects of the rain signal from available FDSN data (SL network). We study the time variant spectral signatures of reaches in steep mountain, graded upland and wide basin landscapes across northern Slovenia and exemplarily invert the seismic data for key flood parameters: water level and debris flux, and propagation velocity. We discuss the detection range of existing earthquake seismometer networks and the potential to improve those with respect to flood quantification. Our analysis highlights the compound effects of channel geometry, event magnitude and network density for flood detection and signature consistency.

How to cite: Dietze, M., Jemec Auflič, M., Petan, S., and Bezak, N.: August 2023 Slovenian flood anatomy from national seismometer network data analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7490, https://doi.org/10.5194/egusphere-egu24-7490, 2024.

X4.186
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EGU24-9067
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ECS
Marjolein Gevers, Stuart N. Lane, Floreana Miesen, Davide Mancini, Matthew Jenkin, Chloé Bouscary, Faye Perchanok, and Ian Delaney

Current climatic warming is causing accelerated melt of the Greenland Ice Sheet. Whilst the changing hydrological response is well known, the sediment export as well as the geomorphic changes in the proglacial area remain uncertain.  

Here we present records of sediment transport from melt seasons 2022 and 2023 in the proglacial area of Leverett glacier, a land terminating glacier outlet on the Western part of the Greenland Ice Sheet. The proglacial area here is very well denifed by a waterfall cutting through bedrock functioning as terminal gauge, which allows for the installation of hydrological stations. These hydrological gauging stations, containing turbidity and pressure sensors, allow for estimation of discharge and suspended sediment concentrations over the melt season. Variations in bedload transport can be analysed using the sesimic data obtained from the geophones placed on the river bank close to the hydrological gauging stations. To convert the recorded seismic data into bedload flux, a Fluvial Inversion Model is used, which is calibrated using active seismics surveys and the water stage data from the hydrological gauging stations.

The dataset allows us to investigate the relationships between bedload, suspended sediment, and water discharge from the Leverett glacier as well as sediment transport and deposition in the proglacial area. We observe several spring events in the first half of July, where suspended sediment concentration and water discharge increase simultaneously at the start of the melt season. During the first half of August, we observe a clear dilution signal, where increase in water discharge coincides with a decrease in suspended sediment concentration From insights about the relationship between water and sediment discharge from the ice sheet, we can speculate about the sediment export response to increased water discharge from the Ice Sheet.

How to cite: Gevers, M., Lane, S. N., Miesen, F., Mancini, D., Jenkin, M., Bouscary, C., Perchanok, F., and Delaney, I.: Seasonal variations in sediment transport from ice sheet terminus through a proglacial forefield. A case study from Leverett glacier, Western Kalaallit Nunaat (Greenland). , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9067, https://doi.org/10.5194/egusphere-egu24-9067, 2024.

X4.187
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EGU24-14117
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ECS
Guan-Syun Huang and Wei-An Chao

Southern Taiwan often experienced abundant monsoon seasons during seasonal transitions, and monsoons and typhoons controlled the rainfall patterns to be complex and varied, resulting the high intensity, prolonged duration, and high concentration. The aforementioned rainfall characteristics can increase the risk of water-and-sediment-related disasters.  To explore the correlation between rainfall patterns and water-and-sediment events, this study employs micro-seismic monitoring network, and the selected Putanpunuas River in southern Taiwan as a case study site. Frequent landslides in the middle and upper watershed supply the river with stable source of sediment materials. Consequently, during the periods with strong precipitation, our study site the shows high susceptibility of water-and-sediment events.  The seismic network comprises one station (BNAR) on the right bank and two stations (BNAL, BNAS) on the left bank downstream of the Putanpunuas River, and an additional station (BNAF) at the confluence of the Putanpunuas River and the Laonong River.  By conducting a series of spectrogram analysis, the average power spectral density (PSD) time series of each station can be computed. Then, we further quantified the seismic signal characteristic parameters for each water-and-sediment events.  This study initially employs various machine learning algorithms (Decision Tree, KNN, K-means, Auto-sklearn) to develop an optimized model for identifying water-and-sediment events, classifying different types of events, such as flooding (FD), debris flooding (DFD) and debris flow (DF), then providing a 4-year-length (2019~2023) catalog of water-and-sediment events.  Rainfall data including hourly precipitation and LiDAR estimated rainfall are collected from the rain gauge stations nearby study area. Using a certain definition (e.g., 4 mm/hr threshold for picking start time) of rain episodes, we calculated total number of episodes and established a rain episodes catalog.  The aforementioned datasets allow us to probe the relationship between rainfall patterns and water-and-sediment events, aiding in inferring the main rain episodes characteristics associated with water-and-sediment events . The  results of this study can be applied to predict potential water-and-sediment event types in Putanpunuas River using rainfall information as input. This can facilitate relevant early warning operations, reducing the societal impact of water-and-sediment disasters.

Key words : Rainfall Patterns, Rain Episode, Micro-seismic monitoring network, Putanpunuas River, Water-and-Sediment Events, Machine Learning

How to cite: Huang, G.-S. and Chao, W.-A.: Probing relation between rainfall pattern and seismic detected water-and-sediment events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14117, https://doi.org/10.5194/egusphere-egu24-14117, 2024.

X4.188
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EGU24-14125
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ECS
You-Lin Hou, Wei-An Chao, Chi-Yao Hung, Su-Chin Chen, and Tzu-Yao Chang

A dam is the natural damming of a river by the geohazards, such as landslides and debris flows. When the dam materials are eroded or washed away due to scour, erosion, and/or an increasing in water level of dam lake, leading dam breach and catastrophic outburst of flooding, which affect the downstream area. Therefore, real-time monitoring of dam failure would facilitate relevant early warning message for the impending floods. The conventional approach using image-based analysis and hydrological measurements is for providing timely warnings of breach; however, landslide dams often occur in mountainous areas, where the methods may face limitations of in-situ measurement. Additionally, the observations of landslide dam breach process are rare and cause the large uncertainties in scientific research. Hence, this study utilizes seismic signals to study the overtopping breach process of field-scale dams. Seismic signals serve as a monitoring tool while simultaneously monitoring the seismic characteristics of overtopping failure in the field-scale dams. In fact, there is a scarcity of observed seismic signal records related to dam breach process in field. Even if some observational data is available, there is a lack of corresponding image analysis or hydrological information for comprehensive discussions. Thus, this study aims to observe and understand overtopping failure through a series of field-scale dam breach experiments. In this study, we first investigate the time-frequency characteristics of seismic power spectral density (PSD) corresponding to the dam breaches primarily involves retrogression erosion, longitudinal and lateral erosion, and the stabilization period. Then, the results of photographic analysis (surface flow velocity, breach geometry), discharge measurements and the time-frequency characteristics of PSD are integrated to discuss the phenomena associated with dam breach. Finally, a series of comparison between compacted and non-compacted dams for PSD spectrogram patterns. The time-series of mean PSD and flow discharge data for the compacted dam exhibit a single-peak and short-term signal duration. Notably, the mean PSD time-series recorded by the seismic station located at the left bank showed a similar trend with flow discharge. Furthermore, during the retrogression erosion period, significant high-frequency PSD energy can be observed only in a case of the compacted dam. In contrast, the PSD energy for the non-compacted dam is concentrated in a relatively lower frequency range (between 10 to 30 Hz). The PSD and flow time series data for the non-compacted dam present a bimodal shape with longer time duration. Based on the flow velocity of breach notch, both in the compacted and non-compacted dams, the maximum velocity occurred during the transition from longitudinal to lateral erosion. In practical application, the results of seismic characteristics for the non-compacted dam case can be applied to the monitoring of dams formed by natural landslides in the field. Our results not only advance in understanding of the field-scale dam breach process but also can be directly applied to breach flooding warnings.
Key words : field-scale dam breach experiments, overtopping breach, power spectral density, time-frequency characteristic

How to cite: Hou, Y.-L., Chao, W.-A., Hung, C.-Y., Chen, S.-C., and Chang, T.-Y.: Studying field-scale dam breach due to overtopping by using seismic signals, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14125, https://doi.org/10.5194/egusphere-egu24-14125, 2024.

X4.189
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EGU24-16742
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ECS
Robert Krüger, Michael Dietze, Xabier Blanch, Jens Grundmann, Issa El-Hussain, Ghazi Al-Rawas, and Anette Eltner

In Oman, the frequency of flash floods has significantly increased in recent years. This phenomenon is correlated with climate change, resulting in an intensification of the atmospheric water cycle. Consequently, a further escalation of flash floods can be anticipated in the future. In Oman, the issue of flash floods is exacerbated by the frequent occurrence of tropical cyclones. Furthermore, the rapid expansion of urban areas, in some cases extending directly into wadis, coupled with the advancing sealing of the ground and insufficient drainage systems, leads to an increased risk of flooding. This is accompanied by substantial property damage and recurring loss of life.

Despite the growing danger posed by flash floods, there is currently no early warning system for precise prediction of these events in Oman. To establish such a system, densely distributed networks for rainfall and water level measurements would be required. However, due to the challenging topography and vastness of the country, implementing such networks is currently not feasible.

Recent studies have shown that seismic sensors could be used for measuring flow conditions. Further, seismic networks could be utilized to detect and track extreme flow events. The increasing availability of low-cost seismic sensors opens up the possibility of instrumenting previously ungauged wadi systems. However, the question remains if seismic networks can pick up smaller flow events and flow events happening in multiple smaller catchments at the same time.

In this study we used flow data from wadi gauge stations in the Al-Batinah Region (NW Oman) and data from broadband seismometers of the Earthquake Monitoring Center to research how flow events of various sizes can be detected by seismic networks. Initial results suggest that flow regimes in wadi systems offer favourable conditions for detection, as they mainly change between flow and no flow conditions. As the amplitude of seismic signals decreases with distance from the source, detection range is limited by background noise. To overcome this, low-cost seismic sensors have recently been installed in a wadi system together with camera based river gauges. Further work utilizing this data is currently ongoing.

How to cite: Krüger, R., Dietze, M., Blanch, X., Grundmann, J., El-Hussain, I., Al-Rawas, G., and Eltner, A.: Detection and localisation of wadi flow events utilizing seismic sensors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16742, https://doi.org/10.5194/egusphere-egu24-16742, 2024.

X4.190
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EGU24-18668
Simon Cook, Darrel Swift, Kristen Cook, Christoff Andermann, Michael Dietze, William Wenban, and Rory White

Glaciated landscapes are showing an amplified reaction to global climate change. Glacial streams are the primary conveyor belts of the incipient sediment cascade, implementing the export of glacially scoured sediment to lower reaches, where the exported sediment controls fluvial geometry, valley floor evolution and ecosystem functioning, water reservoir lifetime and energy production in several alpine countries. Despite that importance, especially of the coarse bedload fraction, there is a striking lack of knowledge about the timing, magnitude and control factors of bedload flux in glacial streams. This is predominantly due to the difficulties to obtain such flux data by classic empirical approaches that require direct in-stream sampling. Here, we pursue a seismic approach to bedload transport quantification, where geophysical sensors are installed along the banks of glacial streams that continuously record ground motion caused by both the turbulent flow of the stream and coarse particle impact on the river bed. We installed small geophone networks along straight reaches of streams draining the glacierised catchments of Oberaargletscher and Steingletscher in Switzerland and recorded the target signals for several days in August 2022, when the melt driven, diurnal river stage fluctuated significantly. River level, turbidity and stream geometry were also observed. Ground parameters for the inverse seismic-model approach were determined using an active seismic survey. We present results of the instrumentation concepts, parameter estimation and data inversion. This allows a discussion of the temporal variability, non-linearity and site-specific nature of hydraulic and sediment transport patterns in catchments where sediment export is dominated by glacial processes.

How to cite: Cook, S., Swift, D., Cook, K., Andermann, C., Dietze, M., Wenban, W., and White, R.: Bedload sediment dynamics in two contrasting alpine glacier headwater catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18668, https://doi.org/10.5194/egusphere-egu24-18668, 2024.

X4.191
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EGU24-11562
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ECS
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Highlight
Jana Roth, Karin Mora, Djamil Al-Halbouni, Ronny Richter, Teja Kattenborn, Sebastian Johannes Wieneke, Ana Bastos, Alexandra Weigelt, Christian Wirth, and Josefine Umlauft

Changing climate, especially the increase in frequency and intensity of extreme events such as heat waves and droughts, poses a significant challenge to the biosphere, threatening biodiversity overall and specifically exacerbating tree mortality. Countermeasures and management actions often prove insufficient due to delayed visual indicators of tree stress. 

Real-time monitoring of physiological and structural changes in tree characteristics and related abiotic parameters, such as sap flow, leaf angle, or soil moisture, plays a crucial role in tracking the trees’ overall vitality. However, conventional monitoring approaches are often expensive, require high maintenance and are therefore not feasible on a larger spatio-temporal scale.     

In a groundbreaking approach, we propose to measure the seismic oscillation generated by tree sway under specific weather conditions, potentially reflecting tree vitality. Specifically, oscillations are related to material properties of leaves, branches, and trunks, which change when they become dry. Seismic measurements offer scalability and low maintenance, making them viable for extensive spatio-temporal coverage. Through integrated observations from dense seismic arrays, direct tree trait measurements, and meteorological parameters collected at the research arboretum (ARBOfun) during autumn 2023, we successfully isolated the seismic fingerprint of tree sway.

However, the unique nature of this novel data introduces challenges, for example noise from human and animal activities, allowing for only time series snapshots. To overcome these challenges, we explored various time series and frequency related analysis methods to separate the tree signal from other influences.

How to cite: Roth, J., Mora, K., Al-Halbouni, D., Richter, R., Kattenborn, T., Wieneke, S. J., Bastos, A., Weigelt, A., Wirth, C., and Umlauft, J.: How trees sway and what it tells us about their overall vitality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11562, https://doi.org/10.5194/egusphere-egu24-11562, 2024.

X4.192
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EGU24-17219
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ECS
Rene Steinmann, Tarje Nissen-Meyer, Fabrice Cotton, Frederik Tilmann, and Beth Mortimer

Our planet experiences ongoing unrest across various scales, from human footsteps to the powerful forces of volcanic eruptions and megathrust earthquakes. Seismic sensors, typically employed for geophysical studies, record diverse phenomena, including ground vibrations caused by the movement of terrestrial animals, known as footfall signals. The recently released SeisSavanna dataset comprises approximately 70,637 footfall signals from 11 different species in the African savanna. Consequently, ground-based vibrations might represent an underexplored sensory mode for continuously monitoring habitat usage and undisturbed animal behavior. To gain a deeper understanding of footfall signals, we conduct exploratory data analysis on the SeisSavanna dataset. Utilizing a scattering transform, we capture the distinctive features of footfall signals, creating a high-level and interpretable data representation for subsequent analyses. Seismogram atlases and clustering enable us to group similar types of footfall signals and investigate the signal-altering path and site effects, providing a comprehensive overview of the entire dataset. Moreover, this data-driven approach serves as a quality check for the species labels retrieved from co-located camera traps with a limited angle of view.

How to cite: Steinmann, R., Nissen-Meyer, T., Cotton, F., Tilmann, F., and Mortimer, B.: Towards seismic monitoring of terrestial ecosystems: an exploratory data analysis of the SeisSavanna dataset, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17219, https://doi.org/10.5194/egusphere-egu24-17219, 2024.

X4.193
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EGU24-8304
José Cunha Teixeira, Ludovic Bodet, Agnès Rivière, Marine Dangeard, Amélie Hallier, Alexandrine Gesret, Amine Dhemaied, and Joséphine Boisson Gaboriau

Monitoring underground water reservoirs is challenging due to limited spatial and temporal observations. This study presents an innovative approach utilizing supervised deep learning (DL), specifically a multilayer perceptron (MLP), and continuous passive-Multichannel Analysis of Surface Waves (passive-MASW) for constructing 2D water table height maps. The study site, geologically well-constrained, features two 20-meter-deep piezometers and a permanent 2D geophone array capturing train-induced surface waves. For each point of the 2D array, dispersion curves (DCs), displaying Rayleigh-wave phase velocities (VR) across a frequency range of 5 to 50 Hz, have been computed each day between December 2022 and September 2023. In the present study, these DCs are sampled in wavelengths ranging from 4.5 to 10.5 m in order to focus the monitoring on the expected water table depths. All VR data around one of the two piezometers is used to train the MLP model. Water table heights are then predicted across the entire geophone array, generating daily 2D piezometric maps. Model's performance is tested through cross-validation and comparisons with water table data at the second piezometer. Model’s efficiency is quantified with the root-mean-square error (RMSE) and the coefficient of determination (R²). A R² is estimated above 80 % for data surrounding the training piezometer and above 55 % for data surrounding the test piezometer. Additionally, the RMSE is impressively low at 0.03 m at both piezometers. Results showcase the effectiveness of DL in generating predictions of water table heights from passive-MASW data. This research contributes to advancing our understanding of subsurface hydrological dynamics, providing a valuable tool for water resource management and environmental monitoring. The ability to predict 2D piezometric maps from a single piezometer is particularly noteworthy, offering a practical and efficient solution for monitoring water table variations across broader spatial extents.

How to cite: Cunha Teixeira, J., Bodet, L., Rivière, A., Dangeard, M., Hallier, A., Gesret, A., Dhemaied, A., and Boisson Gaboriau, J.: Water table height maps prediction from passive surface-wave dispersion using deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8304, https://doi.org/10.5194/egusphere-egu24-8304, 2024.

X4.194
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EGU24-5947
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ECS
Chi-Ling Wei and Li-Pen Wang

Raindrop size distribution (DSD) is a key factor to derive reliable rainfall estimates. It is highly related to a number of integral rainfall variables, such as rain intensity (R), rain water content (W) and radar echo (Z) and thus can contribute to a range of hydrological and meteorological applications, such as rainfall-induced landslide warnings and radar rainfall calibration. Disdrometers are commonly used to measure DSDc. Well-known disdrometer sensors include JWD, Parsivel and 2DVD . These sensors may have their own strengths and weaknesses, but their costs are all much higher than that of widely-deployed catching gauges (e.g. tipping bucket and weighing gauges). This makes it infeasible to have a widespread, or dense, DSD monitoring network. To address this issue, our ultimate goal is to develop a lightweight and low-cost disdrometer with descent accuracy.

In this work, we have prototyped a disdrometer with a piezoelectric cantilever. It is not new to use piezoelectric materials as rain sensors because of its low cost and low maintenance. It is however not trivial to ‘calibrate’ this type of sensors, and various calibration methods have been proposed in the literature. However, whereas most of these sensors associate received signal with rainfall properties directly (via statistical or machine learning approaches), we propose to formulate the drop sensing process as a ‘mechanical’ problem. More specifically, we first form a physical model that can well simulate the signal response of continuous excitation force on a piezoelectric cantilever based on an existing theoretical model. We then analytically derive the inverse function of the model which can obtain the excitation force directly from the measured signals. The derived force-time signal is found to linearly associate with DSD and can also be used for other purposes including kinetic energy analysis.

In spite of the sound underlying theory, the real-world signal is far from perfect, containing a considerable amount of noise. Additionally, as our physical model requires conducting differentiation and second-order differentiation, to which the impact of noise is even destructive. Although we have made efforts to improve the quality of signal from the source, it does not fully solve the problem because the physical model is highly sensitive to signal gradients. To effectively deduce the impact of noise, we then introduced various signal ‘noise’ models, which were reported to well resemble the behavior of real-world signal noises, to train a machine learning (ML) model, such that the actual excitation force function can be derived from various weather conditions.

To verify the proposed sensor and signal processing model, we have set up lab experiments using an in-house device with micropumps and high-voltage raindrop detachment devices to control the required size, drop location, and timing of the drops. Preliminary results from a given range of drop sizes have shown the potential of the proposed sensor and ML-based signal processing model to well derive drop sizes from our experimental device. We plan on further testing our sensor outdoor and compare the measurements with those collected from a co-located Parseval2 disdrometer.

How to cite: Wei, C.-L. and Wang, L.-P.: Low-cost raindrop sizing with piezoelectric sensor: A mechanical approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5947, https://doi.org/10.5194/egusphere-egu24-5947, 2024.

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EGU24-17787
Lars Wiesenberg, Sunke Schmidtko, and Thomas Meier

Microseism is one of the biggest parts of ambient seismic noise and has a huge effect on seismic measurements on almost every regular broad band seismometer, but especially in coastal areas. Generally, microseism describes the interaction of water waves and the seafloor. Its variation over time is from huge interest. It is often used on short-period scales to investigate local weather effects, like storm events or seasonal variations. In this work, we are investigating variations in the microseism of the Northern Atlantic on multiannual scales. For that reason, we utilize up to 50 years of seismic data from several onshore stations across Central and Northern Europe. The focus is on secondary microseism of the Northern Atlantic which is normally sensitive at periods of ≈10 to 5 s. It is estimated over two-hour segments of seismic data, separately. Secondary microseism is post processed to eliminate effects of data gaps or outliers before lowpass filtering for the periods of interest. Besides of a dominant peak at one year period, secondary microseism shows also distinct variations at several year of periods. These variations clearly correlate with the North-Atlantic-Oscillation Index (NAO), not only visually, but also quantitatively and might therefore be relatable to climate variations affecting the North Atlantic.

How to cite: Wiesenberg, L., Schmidtko, S., and Meier, T.: (Multi)annual variations in the microseism of the Northern Atlantic, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17787, https://doi.org/10.5194/egusphere-egu24-17787, 2024.