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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 (V_R) 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 V_R 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.

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.

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.