Pascal Edme

Last Name

Edme

First Name

Pascal

ORCID

0000-0002-3041-0559

Organisational unit

02818 - Schweiz. Erdbebendienst (SED) / Swiss Seismological Service (SED)

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Publications1 - 10 of 40

Observing Avalanche Dynamics with Distributed Acoustic Sensing
Lindner, Nadja; Edme, Pascal; Paitz, Patrick; et al. (2021)
AGU Fall Meeting Abstracts
In Situ Regolith Seismic Velocity Measurement at the InSight Landing Site on Mars
Brinkman, Nienke; Schmelzbach, Cédric; Sollberger, David Andres; et al. (2022)
Journal of Geophysical Research: Planets
Interior exploration using Seismic Investigations, Geodesy and Heat Transport's (InSight) seismometer package Seismic Experiment for Interior Structure (SEIS) was placed on the surface of Mars at about 1.2 m distance from the thermal properties instrument Heat flow and Physical Properties Package (HP3) that includes a self-hammering probe. Recording the hammering noise with SEIS provided a unique opportunity to estimate the seismic wave velocities of the shallow regolith at the landing site. However, the value of studying the seismic signals of the hammering was only realized after critical hardware decisions were already taken. Furthermore, the design and nominal operation of both SEIS and HP3 are nonideal for such high-resolution seismic measurements. Therefore, a series of adaptations had to be implemented to operate the self-hammering probe as a controlled seismic source and SEIS as a high-frequency seismic receiver including the design of a high-precision timing and an innovative high-frequency sampling workflow. By interpreting the first-arriving seismic waves as a P-wave and identifying first-arriving S-waves by polarization analysis, we determined effective P- and S-wave velocities of vP = 119(+45)(-21) m/s and vS = 63(+11)(-7) m/s, respectively, from around 2,000 hammer stroke recordings. These velocities likely represent bulk estimates for the uppermost several 10s of cm of regolith. An analysis of the P-wave incidence angles provided an independent vP/vS ratio estimate of 1.84(+0.89)(-0.35) that compares well with the traveltime based estimate of 1.86(+0.42)(-0.25). The low seismic velocities are consistent with those observed for low-density unconsolidated sands and are in agreement with estimates obtained by other methods.
Phenomenology of Avalanche Recordings From Distributed Acoustic Sensing
Paitz, Patrick; Lindner, Nadja; Edme, Pascal; et al. (2023)
Journal of Geophysical Research: Earth Surface
Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Hence, understanding and continuously monitoring mass movements are crucial to mitigate their risk. We propose to use Distributed Acoustic Sensing (DAS) to measure strain rate along a fiber-optic cable to characterize ground deformation induced by avalanches. We recorded 12 snow avalanches of various dimensions at the Vallee de la Sionne test site in Switzerland, utilizing existing fiber-optic infrastructure and a DAS interrogation unit during the winter 2020/2021. By training a Bayesian Gaussian Mixture Model, we automatically characterize and classify avalanche-induced ground deformations using physical properties extracted from the frequency-wavenumber and frequency-velocity domain of the DAS recordings. The resulting model can estimate the probability of avalanches in the DAS data and is able to differentiate between the avalanche-generated seismic near-field, the seismo-acoustic far-field, and the mass movement propagating on top of the fiber. By analyzing the mass-movement propagation signals, we are able to identify group velocity packages within an avalanche that propagate faster than the phase velocity of the avalanche front, indicating complex internal structures. Importantly, we show that the seismo-acoustic far-field can be detected before the avalanche reaches the fiber-optic array, highlighting DAS as a potential research and early warning tool for hazardous mass movements.
Cryoseismic Event Analysis on Distributed Strain Recordings Leveraging Unsupervised Clusterin
Grimm, Julius; Paitz, Patrick; Martin, Eileen Rose; et al. (2021)
AGU Fall Meeting Abstracts
Toward automatic avalanche detection with Distributed-Acoustic-Sensing leveraging telecommunication infrastructure
Edme, Pascal; Paitz, Patrick; Fichtner, Andreas; et al. (2024)
EGUsphere
Snow avalanches pose significant threats in alpine regions, leading to considerable human and economic losses. The ability to promptly identify the locations and timing of avalanche events is essential for effective prediction and risk mitigation. Conventional automatic avalanche detection systems typically rely on radars and/or seismo-acoustic sensors. While these systems operate successfully regardless of weather conditions, their coverage is often confined to a single slope or a small catchment (distances < 3 km). In our study, we demonstrate the feasibility of detecting snow avalanches using Distributed Acoustic Sensing (DAS) through existing fiber-optic telecommunication cables. Our pilot experiment, conducted over the 2021/2022 winter, involved a 10km long fiber-optic dark cable running parallel to the Flüelapass road in the eastern Swiss Alps close to Davos. The DAS data reveal distinct evidence of numerous dry- and wet-snow avalanches, even when they do not reach the cable, as confirmed photographically. We show that avalanches can be distinguished from other signals (e.g., vehicle traffic) using a frequency-dependent STA/LTA attribute, enabling their detection with high spatiotemporal resolution. These findings pave the way for cost-effective and near-real-time avalanche monitoring over extensive distances, leveraging existing fiber-optic infrastructure.
Distributed Fiber-Optic Sensing for Local Ground-Roll Estimation and Attenuation
Edme, Pascal; Kiers, Tjeerd; Paitz, Patrick; et al. (2022)
83rd EAGE Annual Conference & Exhibition 2022
Distributed acoustic sensing usually aims at collecting spatially unaliased inline strain over tens of kilometers with a single optical fiber cable and associated interrogator. Here we explore the possibility of using this cost-effective technology to capture noise models and subsequently attenuate ground-roll originating from any directions (therefore including scattered noise) without relying on the traditional requirement of dense spatial sampling of geophones. We discuss a fiber cable layout composed of horizontal rings around the vertical geophones to measure the pseudo-pressure fluctuations, or equivalently the omni-directional divergence of the wavefield. Synthetic and field data confirm that loop circumference fluctuations are mainly generated by the Rayleigh wave train, in contrast to reflection signal. In addition, the proposed sensing approach seems more robust than finite-differencing additional horizontal geophone recordings. This suggests that a single additional component (complementary divergence via the interrogation of multiple fiber-optic rings) will allow sparser acquisition with reduced field effort and associated costs. Copyright © 2022 by the European Association of Geoscientists & Engineers (EAGE). All rights reserved
High-resolution 3D seismic characterization of an Alpine slope instability using a 1'000 node array
Kiers, Tjeerd; Schmelzbach, Cédric; Maurer, Hansruedi; et al. (2024)
EGUsphere
Slope instabilities, further destabilized by global warming and extreme weather conditions, pose increasing risks to life and property. Hence, understanding these potentially destructive phenomena is crucial to mitigate associated losses. Established approaches like remote sensing and radar-based observations yield important information on surface displacement. However, seismic imaging and monitoring techniques offer complementary insights into subsurface structures, physical properties and internal time-dependent processes that drive the slope instability evolution. The ‘Cuolm da Vi’ slope near Sedrun in Central Switzerland is one of the largest mass movements in the Alps (100-200 million m3) and is moving by up to 20cm/year. Even though it currently does not pose an immediate threat, the surface displacement of the slope instability is closely monitored. Yet, knowledge about its internal structure is limited such as, for example, the vertical extent of the unstable section which is suspected to reach several hundred meters in depth. The main objective of our project is to gain new insights into the slope instability structure and evolution. Furthermore, we aim to extend this towards innovative seismic strategies for the characterization and monitoring of large-scale mass movements in general. In summer 2022, we deployed an extensive seismic sensor network at Cuolm da Vi covering an area of approximately 0.6 km2. This network consisted of over 1'000 autonomous nodes arranged in a hexagonal grid pattern. In addition, we installed a 6-kilometer-long fiber-optic cable, targeted for long-term Distributed Acoustic Sensing (DAS) and Distributed Strain Sensing (DSS) measurements. This unique multi-sensor geophysical network enables us to investigate the unstable slope with an unprecedented level of spatial and temporal resolution, allowing us to monitor time-dependent changes over a broad spectrum of scales in space and time. During 2022 and 2023, we collected an extensive data set, including extended periods of continuous acquisition using the nodal, DAS, and DSS systems. During the summer 2022 acquisition period, we conducted a controlled-source seismic experiment to characterize the 3D subsurface structure using seismic imaging techniques. Recordings of 163 dynamite shots by the 1’000 node array resulted in more than 30’000 P-wave first-arrival travel-time picks. Using 3D travel-time tomography, we established a first 3D subsurface P-wave velocity model of the Cuolm da Vi body. The resultant tomograms exhibit strong lateral and vertical velocity contrasts, which correlate at the surface with mapped tectonic features and identified instable sections. Furthermore, velocity anomalies within the slope instability volume indicate significant structural and/or geological variations in space. In combination with the other seismic and geotechnical information, the 3D seismic velocity model allows us to, for example, revise hazard scenarios.
Monitoring of an Alpine landslide using dense seismic observations: combining Distributed Acoustic Sensing and 1000 autonomous seismic nodes
Kiers, Tjeerd; Schmelzbach, Cédric; Edme, Pascal; et al. (2023)
EGUsphere
Landslides are a major natural hazard that can cause significant loss of life and property damage around the world. As global temperatures rise and weather extremes become more frequent, we can expect an increase in the hazard emanating from landslides too. In order to better understand and mitigate landslide risks, a variety of strategies have been developed to characterize and monitor landslide activity. Many established approaches provide valuable information about surface displacement and surface properties, but are not suited to inspect the subsurface parts of a landslide body. In contrast, seismic imaging and monitoring methods allow us to study subsurface structures, properties, and internal processes that control landslide behaviour. In our project, we develop novel seismic data acquisition and interpretation approaches to characterize and monitor one of the largest active unstable slopes in the Alps, the Cuolm da Vi landslide, with an unprecedented spatial resolution. We achieve this by combining an array of over 1’000 seismic nodes with fiber-optic based monitoring techniques such as Distributed Acoustic (DAS) and Strain Sensing (DSS). The deep-seated Cuolm da Vi landslide is located near Sedrun (Central Switzerland) and consists of approximately 100-200 million m3 of unstable rock reaching displacement rates up to 10-20 cm/yr with clear seasonal cycles. In summer 2022, we buried over 6 kilometres of fiber-optic cable in this alpine environment covering the most active part of the landslide with multiple cable orientations. Additionally, we deployed a nodal array of 1046 accelerometers in a hexagonal grid covering around 1km2 with a nominal spacing of 28 meters. Seismic data were acquired with the nodes and the DAS system continuously for four weeks. This time period included the blasting of 163 dynamite shots for calibration and active-source imaging purposes. In 2023, we plan to conduct data acquisition for longer periods using primarily fibre-optic based techniques with a focus on the temporal evolution of the landslide dynamics. Our first goal is to resolve the internal structure of the landslide based on the controlled-source data acquired in summer 2022 to construct, for example, a seismic velocity model. Based on the models derived from the active-source seismic data, we plan to exploit the continuous seismic recordings of ambient vibrations and potential seismic signals produced by the landslide activity to complement structural models and study the landslide dynamics. We will present our current results and discuss their implications for the next steps towards monitoring this landslide over time.
In Situ Snow Avalanche Monitoring and Characterization
Aichele, Johannes; Simeon, Andri; van herwijnen, Alec; et al. (2025)
EGUsphere
Snow avalanches in alpine regions pose significant risks to people and infrastructure. To mitigate these risks, early warning systems based on seismic sensors can provide real-time data on avalanche activity, crucial for avalanche forecasting. Additionally, forecasting and risk management require a thorough understanding of avalanche processes. However, avalanche release mechanism and dynamics are only partly understood due to the multi-physics processes involved, spanning from dynamic crack propagation to granular and turbulent flow. Traditional seismic monitoring systems have relied on far-field signals or sparse point measurements along the flow path, limiting our ability to fully capture the processes at play. On the one hand, this makes investigating avalanche release very challenging. For example, identifying dynamic fracture propagation through seismic sensors in the near-field is crucial to advance real-time avalanche prediction. On the other hand, far-field measures are often insufficient for effective risk mitigation. Mitigation requires a thorough characterization of avalanche flow regimes and entrainment throughout the entire heterogeneous avalanche path. To address these challenges, we deployed a unique dense array of seismic sensors at the avalanche test site Vallée de la Sionne, Valais, Switzerland, covering the release zone to the runout area. The setup consists of a Distributed Acoustic Sensing (DAS) system interrogating 14 parallel downslope fiber optic lines (~100 m in length, spaced by 2 m) within the release zone at ~2400 m a.s.l., and a quasi linear fiber optic cable down to ~1500 m a.s.l., which follows the avalanche track and covers the entire runout corridor. The ~4 km long cable is embedded beneath the first snow layer, providing innovative in situ measurements of seismic and aseismic signals in the near field. Sampled at 400 Hz, at every 2 m with 4 m gauge length, this deployment represents one of the most comprehensive in situ avalanche monitoring efforts to date. We present preliminary results from the 2024/2025 season. The avalanches act as moving seismic sources whose far and near-field seismic signals allow us to characterize the spatio-temporal avalanche evolution from release to arrest. We are able to differentiate different flow regimes along the avalanche path, and the grid will potentially capture fracture propagation in the release zone. Our DAS derived information will be benchmarked against concurrent measurements at Vallée de la Sionne which include optical and radar measurements. This makes our setup the ideal field experiment to advance avalanche characterization and lay the groundwork for real-time hazard monitoring with fibre optic cables.
Adjoint-Source Inversion of Microseismic Sources with DAS in Boreholes
Tuinstra, Katinka Barbara; Lanza, Federica; Noe, Sebastian; et al. (2024)
EGUsphere
Microseismic source processes can be closely monitored during hydraulic stimulations with optical fiber deployed behind borehole casing, using Distributed Acoustic Sensing (DAS). The Bedretto Underground Laboratory for the Geosciences and Geoenergies (BULGG) provides a test site at the scale of hundreds of meters (meso-scale), where multiple boreholes are instrumented with fibers around a stimulation well. This enables the characterization of source properties of induced seismicity thanks to the dense sampling of the wavefield close to the stimulated region. In 2023 various stimulation activities in the BedrettoLab produced M<-1 events that were recorded on three fibers surrounding the stimulated region. The interrogated fibers are running through the stimulated seismicity zone, and surround the majority of the events. Some of these events are recorded with high coherency and signal-to-noise ratio, making them suitable for further source characterization, such as location and moment tensor inversion. These events were at the same time recorded with other co-located point sensors such as geophones and acoustic emission sensors, which enables comparison to other instruments. In this work, we select and process a subset of events with clear DAS recordings, and invert for their location, source time and moment tensor components using an adjoint inversion method. This includes computing the full forward and adjoint wavefield and gradient using a spectral-element solver. Using the full waveforms to invert for these events greatly improves the resolution of the source estimates, allows for incorporation of the full velocity model, and only two simulations per iteration are required: a forward and adjoint simulation, and gradient computation. The receiver coverage of the focal spheres by the surrounding optical fibers is an excellent test bed for the method, and the simulated domain remains on the order of hundreds of meters, which means that the simulation can be pushed to high frequencies (>100 Hz). This study provides a step forward to monitoring microseismicity in hydraulic stimulations with fiber-optic measurements.

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Pascal Edme

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