Articles | Volume 7, issue 1
https://doi.org/10.5194/esurf-7-171-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/esurf-7-171-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks
Matthias Meyer
CORRESPONDING AUTHOR
Computer Engineering and Networks Laboratory, ETH Zurich, Zurich, Switzerland
Samuel Weber
Department of Geography, University of Zurich, Zurich, Switzerland
Computer Engineering and Networks Laboratory, ETH Zurich, Zurich, Switzerland
Jan Beutel
Computer Engineering and Networks Laboratory, ETH Zurich, Zurich, Switzerland
Lothar Thiele
Computer Engineering and Networks Laboratory, ETH Zurich, Zurich, Switzerland
Related authors
Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
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This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Samuel Weber, Jan Beutel, Reto Da Forno, Alain Geiger, Stephan Gruber, Tonio Gsell, Andreas Hasler, Matthias Keller, Roman Lim, Philippe Limpach, Matthias Meyer, Igor Talzi, Lothar Thiele, Christian Tschudin, Andreas Vieli, Daniel Vonder Mühll, and Mustafa Yücel
Earth Syst. Sci. Data, 11, 1203–1237, https://doi.org/10.5194/essd-11-1203-2019, https://doi.org/10.5194/essd-11-1203-2019, 2019
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In this paper, we describe a unique 10-year or more data record obtained from in situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland, at 3500 m a.s.l. By documenting and sharing these data in this form, we contribute to facilitating future research based on them, e.g., in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models.
Samuel Weber and Alessandro Cicoira
EGUsphere, https://doi.org/10.5194/egusphere-2024-2652, https://doi.org/10.5194/egusphere-2024-2652, 2024
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The properties of the permafrost ground depend on its temperature and composition. We used temperature data from 29 boreholes in Switzerland to study how heat moves through different types of mountain permafrost landforms. We found that it depends on where you are, whether there is water in the ground and what time of year it is. Understanding these changes is important because they can affect how stable mountain slopes are and how easy it is to build things in mountain areas.
Felix Pfluger, Samuel Weber, Joseph Steinhauser, Christian Zangerl, Christine Fey, Johannes Fürst, and Michael Krautblatter
EGUsphere, https://doi.org/10.5194/egusphere-2024-2509, https://doi.org/10.5194/egusphere-2024-2509, 2024
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Our study explores permafrost-glaciers interactions with a foucs on its implication for preparing/triggering high-volume rock slope failures. Using the Bliggspitze rock slide as a case study, we demonstrate a new type of rock slope failure mechanism triggered by the uplift of the cold/warm dividing line in polythermal alpine glaciers, a widespread and currently underexplored phenomenon in alpine environments worldwide.
Jacopo Boaga, Mirko Pavoni, Alexander Bast, and Samuel Weber
The Cryosphere, 18, 3231–3236, https://doi.org/10.5194/tc-18-3231-2024, https://doi.org/10.5194/tc-18-3231-2024, 2024
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Reversal polarity is observed in rock glacier seismic refraction tomography. We collected several datasets observing this phenomenon in Switzerland and Italy. This phase change may be linked to interferences due to the presence of a thin low-velocity layer. Our results are confirmed by the modelling and analysis of synthetic seismograms to demonstrate that the presence of a low-velocity layer produces a polarity reversal on the seismic gather.
Riccardo Scandroglio, Samuel Weber, Till Rehm, and Michael Krautblatter
EGUsphere, https://doi.org/10.5194/egusphere-2024-1512, https://doi.org/10.5194/egusphere-2024-1512, 2024
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Recent studies confirm that mountain permafrost is reducing, but there is little information on the role of water. This study looks at ten years of weather data and water flow in 50m-deep rock fractures. We precisely quantify the timing and quantities of this flow with a model. For the first time, we estimate pressures generated by water inside rock fractures. Pressures from snowmelt and rain events threaten slope stability; therefore, monitoring water's presence in permafrost areas is crucial.
Julie Wee, Sebastián Vivero, Tamara Mathys, Coline Mollaret, Christian Hauck, Christophe Lambiel, Jan Beutel, and Wilfried Haeberli
EGUsphere, https://doi.org/10.5194/egusphere-2024-1283, https://doi.org/10.5194/egusphere-2024-1283, 2024
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This study highlights the importance of a multi-method and multidisciplinary approach to better understand the influence of the internal structure of the Gruben glacier forefield-connected rock glacier and adjacent debris-covered glacier on their driving thermo-mechanical processes and associated surface dynamics. We were able to discriminate glacial from periglacial processes as their spatio-temporal patterns of surface dynamics and geophysical signatures are (mostly) different.
Maike Offer, Samuel Weber, Michael Krautblatter, Ingo Hartmeyer, and Markus Keuschnig
EGUsphere, https://doi.org/10.5194/egusphere-2024-893, https://doi.org/10.5194/egusphere-2024-893, 2024
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We present a unique dataset of repeated electrical resistivity tomography and long-term borehole temperature measurements to investigate the complex seasonal water flow in permafrost rockwalls. Our joint analysis shows that permafrost rocks are subject to enhanced pressurised water flow during the melt period. In addition to slow thermal heat conduction, permafrost rocks are subject to push-like warming events, favouring accelerated permafrost degradation and reduced rockwall stability.
Marcia Phillips, Chasper Buchli, Samuel Weber, Jacopo Boaga, Mirko Pavoni, and Alexander Bast
The Cryosphere, 17, 753–760, https://doi.org/10.5194/tc-17-753-2023, https://doi.org/10.5194/tc-17-753-2023, 2023
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A new combination of temperature, water pressure and cross-borehole electrical resistivity data is used to investigate ice/water contents in an ice-rich rock glacier. The landform is close to 0°C and has locally heterogeneous characteristics, ice/water contents and temperatures. The techniques presented continuously monitor temporal and spatial phase changes to a depth of 12 m and provide the basis for a better understanding of accelerating rock glacier movements and future water availability.
Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
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This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Philipp Mamot, Samuel Weber, Saskia Eppinger, and Michael Krautblatter
Earth Surf. Dynam., 9, 1125–1151, https://doi.org/10.5194/esurf-9-1125-2021, https://doi.org/10.5194/esurf-9-1125-2021, 2021
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The mechanical response of permafrost degradation on high-mountain rock slope stability has not been calculated in a numerical model yet. We present the first approach for a model with thermal and mechanical input data derived from laboratory and field work, and existing concepts. This is applied to a test site at the Zugspitze, Germany. A numerical sensitivity analysis provides the first critical stability thresholds related to the rock temperature, slope angle and fracture network orientation.
Philipp Mamot, Samuel Weber, Maximilian Lanz, and Michael Krautblatter
The Cryosphere, 14, 1849–1855, https://doi.org/10.5194/tc-14-1849-2020, https://doi.org/10.5194/tc-14-1849-2020, 2020
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A failure criterion for ice-filled rock joints is a prerequisite to accurately assess the stability of permafrost rock slopes. In 2018 a failure criterion was proposed based on limestone. Now, we tested the transferability to other rocks using mica schist and gneiss which provide the maximum expected deviation of lithological effects on the shear strength. We show that even for controversial rocks the failure criterion stays unaltered, suggesting that it is applicable to mostly all rock types.
Christoph Rohner, David Small, Jan Beutel, Daniel Henke, Martin P. Lüthi, and Andreas Vieli
The Cryosphere, 13, 2953–2975, https://doi.org/10.5194/tc-13-2953-2019, https://doi.org/10.5194/tc-13-2953-2019, 2019
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The recent increase in ice flow and calving rates of ocean–terminating glaciers contributes substantially to the mass loss of the Greenland Ice Sheet. Using in situ reference observations, we validate the satellite–based method of iterative offset tracking of Sentinel–1A data for deriving flow speeds. Our investigations highlight the importance of spatial resolution near the fast–flowing calving front, resulting in significantly higher ice velocities compared to large–scale operational products.
Samuel Weber, Jan Beutel, Reto Da Forno, Alain Geiger, Stephan Gruber, Tonio Gsell, Andreas Hasler, Matthias Keller, Roman Lim, Philippe Limpach, Matthias Meyer, Igor Talzi, Lothar Thiele, Christian Tschudin, Andreas Vieli, Daniel Vonder Mühll, and Mustafa Yücel
Earth Syst. Sci. Data, 11, 1203–1237, https://doi.org/10.5194/essd-11-1203-2019, https://doi.org/10.5194/essd-11-1203-2019, 2019
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In this paper, we describe a unique 10-year or more data record obtained from in situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland, at 3500 m a.s.l. By documenting and sharing these data in this form, we contribute to facilitating future research based on them, e.g., in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models.
Jérome Faillettaz, Martin Funk, Jan Beutel, and Andreas Vieli
Nat. Hazards Earth Syst. Sci., 19, 1399–1413, https://doi.org/10.5194/nhess-19-1399-2019, https://doi.org/10.5194/nhess-19-1399-2019, 2019
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We developed a new strategy for real-time early warning of
gravity-driven slope failures (such as landslides, rockfalls, glacier break-off, etc.). This method enables us to investigate natural slope stability based on continuous monitoring and interpretation of seismic waves generated by the potential instability. Thanks to a pilot experiment, we detected typical patterns of precursory events prior to slide events, demonstrating the potential of this method for real-word applications.
Alessandro Cicoira, Jan Beutel, Jérome Faillettaz, Isabelle Gärtner-Roer, and Andreas Vieli
The Cryosphere, 13, 927–942, https://doi.org/10.5194/tc-13-927-2019, https://doi.org/10.5194/tc-13-927-2019, 2019
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Rock glacier flow varies on multiple timescales. The variations have been linked to climatic forcing, but a quantitative understanding is still missing.
We use a 1-D numerical modelling approach coupling heat conduction to a creep model in order to study the influence of temperature variations on rock glacier flow. Our results show that heat conduction alone cannot explain the observed variations. Other processes, likely linked to water, must dominate the short-term velocity signal.
Philipp Mamot, Samuel Weber, Tanja Schröder, and Michael Krautblatter
The Cryosphere, 12, 3333–3353, https://doi.org/10.5194/tc-12-3333-2018, https://doi.org/10.5194/tc-12-3333-2018, 2018
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Most of the observed failures in permafrost-affected alpine rock walls are likely triggered by the mechanical destabilisation of warming bedrock permafrost including ice-filled joints. We present a systematic study of the brittle shear failure of ice and rock–ice contacts along rock joints in a simulated depth ≤ 30 m and at temperatures from −10 to −0.5 °C. Warming and sudden reduction in rock overburden due to the detachment of an upper rock mass lead to a significant drop in shear resistance.
Samuel Weber, Jan Beutel, Jérome Faillettaz, Andreas Hasler, Michael Krautblatter, and Andreas Vieli
The Cryosphere, 11, 567–583, https://doi.org/10.5194/tc-11-567-2017, https://doi.org/10.5194/tc-11-567-2017, 2017
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We present a 8-year continuous time series of measured fracture kinematics and thermal conditions on steep permafrost bedrock at Hörnligrat, Matterhorn. Based on this unique dataset and a conceptual model for strong fractured bedrock, we develop a novel quantitative approach that allows to separate reversible from irreversible fracture kinematics and assign the dominant forcing. A new index of irreversibility provides useful indication for the occurrence and timing of irreversible displacements.
V. Wirz, S. Gruber, R. S. Purves, J. Beutel, I. Gärtner-Roer, S. Gubler, and A. Vieli
Earth Surf. Dynam., 4, 103–123, https://doi.org/10.5194/esurf-4-103-2016, https://doi.org/10.5194/esurf-4-103-2016, 2016
Related subject area
Cross-cutting themes: Quantitative and statistical methods in Earth surface dynamics
Introducing standardized field methods for fracture-focused surface process research
Full four-dimensional change analysis of topographic point cloud time series using Kalman filtering
Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites
Inverse modeling of turbidity currents using an artificial neural network approach: verification for field application
Automated quantification of floating wood pieces in rivers from video monitoring: a new software tool and validation
Particle size dynamics in abrading pebble populations
Computing water flow through complex landscapes – Part 3: Fill–Spill–Merge: flow routing in depression hierarchies
A photogrammetry-based approach for soil bulk density measurements with an emphasis on applications to cosmogenic nuclide analysis
Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct
Identifying sediment transport mechanisms from grain size–shape distributions, applied to aeolian sediments
Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset
Earth's surface mass transport derived from GRACE, evaluated by GPS, ICESat, hydrological modeling and altimetry satellite orbits
The R package “eseis” – a software toolbox for environmental seismology
Bayesian inversion of a CRN depth profile to infer Quaternary erosion of the northwestern Campine Plateau (NE Belgium)
A new CT scan methodology to characterize a small aggregation gravel clast contained in a soft sediment matrix
Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics
An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics
Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains
Constraining the stream power law: a novel approach combining a landscape evolution model and an inversion method
Martha Cary Eppes, Alex Rinehart, Jennifer Aldred, Samantha Berberich, Maxwell P. Dahlquist, Sarah G. Evans, Russell Keanini, Stephen E. Laubach, Faye Moser, Mehdi Morovati, Steven Porson, Monica Rasmussen, and Uri Shaanan
Earth Surf. Dynam., 12, 35–66, https://doi.org/10.5194/esurf-12-35-2024, https://doi.org/10.5194/esurf-12-35-2024, 2024
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All rocks have fractures (cracks) that can influence virtually every process acting on Earth's surface where humans live. Yet, scientists have not standardized their methods for collecting fracture data. Here we draw on past work across geo-disciplines and propose a list of baseline data for fracture-focused surface process research. We detail the rationale and methods for collecting them. We hope their wide adoption will improve future methods and knowledge of rock fracture overall.
Lukas Winiwarter, Katharina Anders, Daniel Czerwonka-Schröder, and Bernhard Höfle
Earth Surf. Dynam., 11, 593–613, https://doi.org/10.5194/esurf-11-593-2023, https://doi.org/10.5194/esurf-11-593-2023, 2023
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We present a method to extract surface change information from 4D time series of topographic point clouds recorded with a terrestrial laser scanner. The method uses sensor information to spatially and temporally smooth the data, reducing uncertainties. The Kalman filter used for the temporal smoothing also allows us to interpolate over data gaps or extrapolate into the future. Clustering areas where change histories are similar allows us to identify processes that may have the same causes.
Ross Pidoto, Nejc Bezak, Hannes Müller-Thomy, Bora Shehu, Ana Claudia Callau-Beyer, Katarina Zabret, and Uwe Haberlandt
Earth Surf. Dynam., 10, 851–863, https://doi.org/10.5194/esurf-10-851-2022, https://doi.org/10.5194/esurf-10-851-2022, 2022
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Erosion is a threat for soils with rainfall as the driving force. The annual rainfall erosivity factor quantifies rainfall impact by analysing high-resolution rainfall time series (~ 5 min). Due to a lack of measuring stations, alternatives for its estimation are analysed in this study. The best results are obtained for regionalisation of the erosivity factor itself. However, the identified minimum of 60-year time series length suggests using rainfall generators as in this study as well.
Hajime Naruse and Kento Nakao
Earth Surf. Dynam., 9, 1091–1109, https://doi.org/10.5194/esurf-9-1091-2021, https://doi.org/10.5194/esurf-9-1091-2021, 2021
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This paper proposes a method to reconstruct the hydraulic conditions of turbidity currents from turbidites. We investigated the validity and problems of this method in application to actual field datasets using artificial data. Once this method is established, it is expected that the method will elucidate the generation process of turbidity currents and will help to predict the geometry of resultant turbidites in deep-sea environments.
Hossein Ghaffarian, Pierre Lemaire, Zhang Zhi, Laure Tougne, Bruce MacVicar, and Hervé Piégay
Earth Surf. Dynam., 9, 519–537, https://doi.org/10.5194/esurf-9-519-2021, https://doi.org/10.5194/esurf-9-519-2021, 2021
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Quantifying wood fluxes in rivers would improve our understanding of the key processes in river ecology and morphology. In this work, we introduce new software for the automatic detection of wood pieces in rivers. The results show 93.5 % and 86.5 % accuracy for piece number and volume, respectively.
András A. Sipos, Gábor Domokos, and János Török
Earth Surf. Dynam., 9, 235–251, https://doi.org/10.5194/esurf-9-235-2021, https://doi.org/10.5194/esurf-9-235-2021, 2021
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Abrasion of sedimentary particles is widely associated with mutual collisions. Utilizing results of individual, geometric abrasion theory and techniques adopted in statistical physics, a new model for predicting the collective mass evolution of large numbers of particles is introduced. Our model uncovers a startling fundamental feature of collective particle dynamics: collisional abrasion may either focus size distributions or it may act in the opposite direction by dispersing the distribution.
Richard Barnes, Kerry L. Callaghan, and Andrew D. Wickert
Earth Surf. Dynam., 9, 105–121, https://doi.org/10.5194/esurf-9-105-2021, https://doi.org/10.5194/esurf-9-105-2021, 2021
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Existing ways of modeling the flow of water amongst landscape depressions such as swamps and lakes take a long time to run. However, as our previous work explains, depressions can be quickly organized into a data structure – the depression hierarchy. This paper explains how the depression hierarchy can be used to quickly simulate the realistic filling of depressions including how they spill over into each other and, if they become full enough, how they merge into one another.
Joel Mohren, Steven A. Binnie, Gregor M. Rink, Katharina Knödgen, Carlos Miranda, Nora Tilly, and Tibor J. Dunai
Earth Surf. Dynam., 8, 995–1020, https://doi.org/10.5194/esurf-8-995-2020, https://doi.org/10.5194/esurf-8-995-2020, 2020
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In this study, we comprehensively test a method to derive soil densities under fieldwork conditions. The method is mainly based on images taken from consumer-grade cameras. The obtained soil/sediment densities reflect
truevalues by generally > 95 %, even if a smartphone is used for imaging. All computing steps can be conducted using freeware programs. Soil density is an important variable in the analysis of terrestrial cosmogenic nuclides, for example to infer long-term soil production rates.
Mariela Perignon, Jordan Adams, Irina Overeem, and Paola Passalacqua
Earth Surf. Dynam., 8, 809–824, https://doi.org/10.5194/esurf-8-809-2020, https://doi.org/10.5194/esurf-8-809-2020, 2020
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We propose a machine learning approach for the classification and analysis of large delta systems. The approach uses remotely sensed data, channel network extraction, and the analysis of 10 metrics to identify clusters of islands with similar characteristics. The 12 clusters are grouped in six main classes related to morphological processes acting on the system. The approach allows us to identify spatial patterns in large river deltas to inform modeling and the collection of field observations.
Johannes Albert van Hateren, Unze van Buuren, Sebastiaan Martinus Arens, Ronald Theodorus van Balen, and Maarten Arnoud Prins
Earth Surf. Dynam., 8, 527–553, https://doi.org/10.5194/esurf-8-527-2020, https://doi.org/10.5194/esurf-8-527-2020, 2020
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In this paper, we introduce a new technique that can be used to identify how sediments were transported to their place of deposition (transport mode). The traditional method is based on the size of sediment grains, ours on the size and the shape. A test of the method on windblown sediments indicates that it can be used to identify the transport mode with less ambiguity, and therefore it improves our ability to extract information, such as climate from the past, from sediment deposits.
Taylor Smith, Aljoscha Rheinwalt, and Bodo Bookhagen
Earth Surf. Dynam., 7, 475–489, https://doi.org/10.5194/esurf-7-475-2019, https://doi.org/10.5194/esurf-7-475-2019, 2019
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Representing the surface of the Earth on an equally spaced grid leads to errors and uncertainties in derived slope and aspect. Using synthetic data, we develop a quality metric that can be used to compare the uncertainties in different datasets. We then apply this method to a real-world lidar dataset, and find that 1 m data have larger error bounds than lower-resolution data. The highest data resolution is not always the best choice – it is important to consider the quality of the data.
Christian Gruber, Sergei Rudenko, Andreas Groh, Dimitrios Ampatzidis, and Elisa Fagiolini
Earth Surf. Dynam., 6, 1203–1218, https://doi.org/10.5194/esurf-6-1203-2018, https://doi.org/10.5194/esurf-6-1203-2018, 2018
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By using a set of evaluation methods involving GPS, ICESat, hydrological modelling and altimetry satellite orbits, we show that the novel radial basis function (RBF) processing technique can be used for processing the Gravity Recovery and Climate Experiment (GRACE) data yielding global gravity field models which fit independent reference values at the same level as commonly accepted global geopotential models based on spherical harmonics.
Michael Dietze
Earth Surf. Dynam., 6, 669–686, https://doi.org/10.5194/esurf-6-669-2018, https://doi.org/10.5194/esurf-6-669-2018, 2018
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Environmental seismology is the study of the seismic signals emitted by Earth surface processes. This emerging research field is at the intersection of many Earth science disciplines. The overarching scope requires free integrative software that is accepted across scientific disciplines, such as R. The article introduces the R package "eseis" and illustrates its conceptual structure, available functions, and worked examples.
Eric Laloy, Koen Beerten, Veerle Vanacker, Marcus Christl, Bart Rogiers, and Laurent Wouters
Earth Surf. Dynam., 5, 331–345, https://doi.org/10.5194/esurf-5-331-2017, https://doi.org/10.5194/esurf-5-331-2017, 2017
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Over very long timescales, 100 000 years or more, landscapes may drastically change. Sediments preserved in these landscapes have a cosmogenic radionuclide inventory that tell us when and how fast such changes took place. In this paper, we provide first evidence of an elevated long-term erosion rate of the northwestern Campine Plateau (lowland Europe), which can be explained by the loose nature of the subsoil.
Laurent Fouinat, Pierre Sabatier, Jérôme Poulenard, Jean-Louis Reyss, Xavier Montet, and Fabien Arnaud
Earth Surf. Dynam., 5, 199–209, https://doi.org/10.5194/esurf-5-199-2017, https://doi.org/10.5194/esurf-5-199-2017, 2017
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This study focuses on the creation of a novel CT scan methodology at the crossroads between medical imagery and earth sciences. Using specific density signatures, pebbles and/or organic matter characterizing wet avalanche deposits can be quantified in lake sediments. Starting from AD 1880, we were able to identify eight periods of higher avalanche activity from sediment cores. The use of CT scans, alongside existing approaches, opens up new possibilities in a wide variety of geoscience studies.
Daniel E. J. Hobley, Jordan M. Adams, Sai Siddhartha Nudurupati, Eric W. H. Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, and Gregory E. Tucker
Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, https://doi.org/10.5194/esurf-5-21-2017, 2017
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Many geoscientists use computer models to understand changes in the Earth's system. However, typically each scientist will build their own model from scratch. This paper describes Landlab, a new piece of open-source software designed to simplify creation and use of models of the Earth's surface. It provides off-the-shelf tools to work with models more efficiently, with less duplication of effort. The paper explains and justifies how Landlab works, and describes some models built with it.
Andrew Valentine and Lara Kalnins
Earth Surf. Dynam., 4, 445–460, https://doi.org/10.5194/esurf-4-445-2016, https://doi.org/10.5194/esurf-4-445-2016, 2016
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Learning algorithms are powerful tools for understanding and working with large data sets, particularly in situations where any underlying physical models may be complex and poorly understood. Such situations are common in geomorphology. We provide an accessible overview of the various approaches that fall under the umbrella of "learning algorithms", discuss some potential applications within geomorphometry and/or geomorphology, and offer advice on practical considerations.
E. Pohl, M. Knoche, R. Gloaguen, C. Andermann, and P. Krause
Earth Surf. Dynam., 3, 333–362, https://doi.org/10.5194/esurf-3-333-2015, https://doi.org/10.5194/esurf-3-333-2015, 2015
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A semi-distributed hydrological model is used to analyse the hydrological cycle of a glaciated high-mountain catchment in the Pamirs.
We overcome data scarcity by utilising various raster data sets as meteorological input. Temperature in combination with the amount of snow provided in winter play the key role in the annual cycle.
This implies that expected Earth surface processes along precipitation and altitude gradients differ substantially.
T. Croissant and J. Braun
Earth Surf. Dynam., 2, 155–166, https://doi.org/10.5194/esurf-2-155-2014, https://doi.org/10.5194/esurf-2-155-2014, 2014
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado,
G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp,
A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M.,
Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C.,
Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P.,
Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P.,
Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale
Machine Learning on Heterogeneous Systems, available at:
http://tensorflow.org (last access: 30 January 2019), 2015. a
Aguiar, A. C. and Beroza, G. C.: PageRank for Earthquakes, Seismol. Res.
Lett., 85, 344–350, https://doi.org/10.1785/0220130162, 2014. a
Amitrano, D., Grasso, J. R., and Senfaute, G.: Seismic Precursory Patterns
before a Cliff Collapse and Critical Point Phenomena, Geophys. Res. Lett.,
32, L08314, https://doi.org/10.1029/2004GL022270, 2005. a
Amitrano, D., Arattano, M., Chiarle, M., Mortara, G., Occhiena, C., Pirulli,
M., and Scavia, C.: Microseismic activity analysis for the study of the
rupture mechanisms in unstable rock masses, Nat. Hazards Earth Syst. Sci.,
10, 831–841, https://doi.org/10.5194/nhess-10-831-2010, 2010. a, b
Amitrano, D., Gruber, S., and Girard, L.: Evidence of Frost-Cracking Inferred
from Acoustic Emissions in a High-Alpine Rock-Wall, Earth Planet. Sc. Lett.,
341–344, 86–93, https://doi.org/10.1016/j.epsl.2012.06.014, 2012. a
Anchal, S., Mukhopadhyay, B., and Kar, S.: UREDT: Unsupervised Learning
Based Real-Time Footfall Event Detection Technique in Seismic Signal, IEEE
Sensors Letters, 2, 1–4, https://doi.org/10.1109/LSENS.2017.2787611, 2018. a, b
Bartholomaus, T. C., Amundson, J. M., Walter, J. I., O'Neel, S., West, M. E.,
and Larsen, C. F.: Subglacial Discharge at Tidewater Glaciers Revealed by
Seismic Tremor, Geophys. Res. Lett., 42, 6391–6398,
https://doi.org/10.1002/2015GL064590, 2015. a, b
Brown, J. R., Beroza, G. C., and Shelly, D. R.: An Autocorrelation Method to
Detect Low Frequency Earthquakes within Tremor, Geophys. Res. Lett., 35,
L16305, https://doi.org/10.1029/2008GL034560, 2008. a
Burjánek, J., Moore, J. R., Molina, F. X. Y., and Fäh, D.: Instrumental
Evidence of Normal Mode Rock Slope Vibration, Geophys. J. Int., 188,
559–569, https://doi.org/10.1111/j.1365-246X.2011.05272.x, 2012. a
Chollet, F.: Keras, Python Framework, available at:
https://github.com/keras-team/keras (last access: 29 January 2019),
2015. a
Colombero, C., Comina, C., Vinciguerra, S., and Benson, P. M.:
Microseismicity of an Unstable Rock Mass: From Field Monitoring to Laboratory
Testing, J. Geophys. Res.-Sol. Ea., 123, 1673–1693,
https://doi.org/10.1002/2017JB014612, 2018. a, b
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L.:
ImageNet: A Large-Scale Hierarchical Image Database, 2009 IEEE Conference on
Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009,
https://doi.org/10.1109/CVPR.2009.5206848, 2009. a
Eibl, E. P. S., Lokmer, I., Bean, C. J., and Akerlie, E.: Helicopter Location
and Tracking Using Seismometer Recordings, Geophys. J. Int., 209, 901–908,
https://doi.org/10.1093/gji/ggx048, 2017. a, b
Fei-Fei, L., Fergus, R., and Perona, P.: One-Shot Learning of Object
Categories, IEEE T. Pattern Anal., 28, 594–611, https://doi.org/10.1109/TPAMI.2006.79,
2006. a
Geometrics: Geode Exploration Seismograph Specification Sheet, version
GeodeDS_v1 (0518), available at:
ftp://geom.geometrics.com/pub/seismic/DataSheets/Geode_spec_sheet.pdf
(last accessed 29 January 2019), 2018. a
Gibbons, S. J. and Ringdal, F.: The Detection of Low Magnitude Seismic Events
Using Array-Based Waveform Correlation, Geophys. J. Int., 165, 149–166,
https://doi.org/10.1111/j.1365-246X.2006.02865.x, 2006. a
Gischig, V. S., Eberhardt, E., Moore, J. R., and Hungr, O.: On the Seismic
Response of Deep-Seated Rock Slope Instabilities –Insights from
Numerical Modeling, Eng. Geol., 193, 1–18,
https://doi.org/10.1016/j.enggeo.2015.04.003, 2015. a
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, Adaptive
computation and machine learning, The MIT Press, Cambridge, Massachusetts,
2016. a
Grosse, C.: Acoustic emission testing: Basics for research – Aplications in
civil engineering, Springer-Verlag Berlin Heidelberg, 3–10, 2008. a
Grosse, C. U. and Ohtsu, M. (Eds.): Acoustic Emission Testing,
Springer-Verlag, Berlin Heidelberg, 2008. a
Hardy, H. R.: Acoustic Emission/Microseismic Activity, CRC Press, London,
2003. a
Hershey, S., Chaudhuri, S., Ellis, D. P. W., Gemmeke, J. F., Jansen, A.,
Moore, R. C., Plakal, M., Platt, D., Saurous, R. A., Seybold, B., Slaney, M.,
Weiss, R. J., and Wilson, K.: CNN Architectures for Large-Scale Audio
Classification, International Conference on Acoustics, Speech and Signal
Processing (ICASSP), arXiv:1609.09430, 2016. a
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T.,
Andreetto, M., and Adam, H.: MobileNets: Efficient Convolutional Neural
Networks for Mobile Vision Applications, Computer Vision and Pattern
Recognition, arXiv:1704.04861 [cs], 2017. a
Ioffe, S. and Szegedy, C.: Batch Normalization: Accelerating Deep
Network Training by Reducing Internal Covariate Shift, Machine
Learning, arXiv:1502.03167 [cs], 2015. a
Keller, M., Yuecel, M., and Beutel, J.: High Resolution Imaging for
Environmental Monitoring Applications, in: International Snow Science
Workshop 2009: Programme and Abstracts, Davos, Switzerland, 197–201, 2009. a
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization, Proc.
3rd Int. Conf. Lern. Representations, arXiv:1412.6980 [cs], 2014. a
Kislov, K. V. and Gravirov, V. V.: Use of Artificial Neural Networks for
Classification of Noisy Seismic Signals, Seismic Instruments, 53, 87–101,
https://doi.org/10.3103/S0747923917010054, 2017. a, b
Kong, Q., Allen, R. M., Schreier, L., and Kwon, Y.-W.: MyShake: A smartphone
seismic network for earthquake early warning and beyond, Sci. Adv., 2,
e1501055, https://doi.org/10.1126/sciadv.1501055, 2016. a
Kuyuk, H. S., Yildirim, E., Dogan, E., and Horasan, G.: An unsupervised
learning algorithm: application to the discrimination of seismic events and
quarry blasts in the vicinity of Istanbul, Nat. Hazards Earth Syst. Sci., 11,
93–100, https://doi.org/10.5194/nhess-11-93-2011, 2011. a
Labuz, J. F., Cattaneo, S., and Chen, L.-H.: Acoustic emission at failure in
quasi-brittle materials, Constr. Build Mater., 15, 225–233, 2001. a
Levy, C., Jongmans, D., and Baillet, L.: Analysis of Seismic Signals Recorded
on a Prone-to-Fall Rock Column (Vercors Massif, French Alps), Geophys. J.
Int., 186, 296–310, https://doi.org/10.1111/j.1365-246X.2011.05046.x, 2011. a
Li, Z., Meier, M.-A., Hauksson, E., Zhan, Z., and Andrews, J.: Machine
Learning Seismic Wave Discrimination: Application to Earthquake
Early Warning, Geophys. Res. Lett., 45, 4773–4779,
https://doi.org/10.1029/2018GL077870, 2018. a
Meyer, M. and Weber, S.: Code for classifier training and evaluation using
the micro-seismic and image dataset acquired at Matterhorn Hörnligrat,
Switzerland, Zenodo, https://doi.org/10.5281/zenodo.1321176, 2018. a
Meyer, M., Weber, S., Beutel, J., Gruber, S., Gsell, T., Hasler, A., and
Vieli, A.: Micro-seismic and image dataset acquired at Matterhorn
Hörnligrat, Switzerland, Data set, Zenodo, https://doi.org/10.5281/zenodo.1320835,
2018. a
Michlmayr, G., Cohen, D., and Or, D.: Sources and Characteristics of Acoustic
Emissions from Mechanically Stressed Geologic Granular Media – A Review,
Earth-Sci. Rev., 112, 97–114, https://doi.org/10.1016/j.earscirev.2012.02.009, 2012. a
Occhiena, C., Coviello, V., Arattano, M., Chiarle, M., Morra di Cella, U.,
Pirulli, M., Pogliotti, P., and Scavia, C.: Analysis of microseismic signals
and temperature recordings for rock slope stability investigations in high
mountain areas, Nat. Hazards Earth Syst. Sci., 12, 2283–2298,
https://doi.org/10.5194/nhess-12-2283-2012, 2012. a, b
Olivier, G., Chaput, J., and Borchers, B.: Using Supervised Machine
Learning to Improve Active Source Signal Retrieval, Seismol. Res.
Lett., 89, 1023–1029, https://doi.org/10.1785/0220170239, 2018. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-Learn: Machine Learning in Python, J. Mach. Learn. Res., 12,
2825–2830, 2011. a
Perol, T., Gharbi, M., and Denolle, M.: Convolutional Neural Network for
Earthquake Detection and Location, Sci. Adv., 4, e1700578,
https://doi.org/10.1126/sciadv.1700578, 2018. a, b, c
Reynen, A. and Audet, P.: Supervised Machine Learning on a Network Scale:
Application to Seismic Event Classification and Detection, Geophys. J. Int.,
210, 1394–1409, https://doi.org/10.1093/gji/ggx238, 2017. a
Ross, Z. E., Meier, M.-A., and Hauksson, E.: P Wave Arrival Picking and
First-Motion Polarity Determination With Deep Learning, J. Geophys. Res.-Sol.
Ea., 123, 5120–5129, https://doi.org/10.1029/2017JB015251, 2018. a
Ross, Z. E., Meier, M.-A., and Hauksson, E.: P Wave Arrival Picking and
First-Motion Polarity Determination With Deep Learning, J. Geophys.
Res.-Sol. Ea., 123, 5120–5129, https://doi.org/10.1029/2017JB015251, 2018. a
Ruano, A. E., Madureira, G., Barros, O., Khosravani, H. R., Ruano, M. G., and
Ferreira, P. M.: Seismic Detection Using Support Vector Machines,
Neurocomputing, 135, 273–283, https://doi.org/10.1016/j.neucom.2013.12.020, 2014. a
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang,
Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L.:
ImageNet Large Scale Visual Recognition Challenge, Int. J. Comput.
Vision, 115, 211–252, https://doi.org/10.1007/s11263-015-0816-y, 2015. a, b
Senfaute, G., Duperret, A., and Lawrence, J. A.: Micro-seismic precursory
cracks prior to rock-fall on coastal chalk cliffs: a case study at
Mesnil-Val, Normandie, NW France, Nat. Hazards Earth Syst. Sci., 9,
1625–1641, https://doi.org/10.5194/nhess-9-1625-2009, 2009. a
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov,
R.: Dropout: A Simple Way to Prevent Neural Networks from
Overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014. a
van Herwijnen, A. and Schweizer, J.: Monitoring Avalanche Activity Using a
Seismic Sensor, Cold Reg. Sci. Technol., 69, 165–176,
https://doi.org/10.1016/j.coldregions.2011.06.008, 2011. a, b
Walter, F., Deichmann, N., and Funk, M.: Basal Icequakes during Changing
Subglacial Water Pressures beneath Gornergletscher, Switzerland, J.
Glaciol., 54, 511–521, https://doi.org/10.3189/002214308785837110, 2008. a
Weber, S., Fäh, D., Beutel, J., Faillettaz, J., Gruber, S., and Vieli, A.:
Ambient Seismic Vibrations in Steep Bedrock Permafrost Used to Infer
Variations of Ice-Fill in Fractures, Earth Planet. Sc. Lett., 501, 119–127,
https://doi.org/10.1016/j.epsl.2018.08.042, 2018a. a
Werner-Allen, G., Lorincz, K., Johnson, J., Lees, J., and Welsh, M.:
Fidelity and Yield in a Volcano Monitoring Sensor Network, in: Proceedings of
the 7th Symposium on Operating Systems Design and Implementation, OSDI '06,
USENIX Association, Berkeley, CA, USA, 6–8 November 2006, 381–396, 2006. a
Withers, M., Aster, R., Young, C., Beiriger, J., Harris, M., Moore, S., and
Trujillo, J.: A Comparison of Select Trigger Algorithms for Automated Global
Seismic Phase and Event Detection, B. Seismol. Soc. Am., 88, 95–106, 1998. a
Yoon, C. E., O'Reilly, O., Bergen, K. J., and Beroza, G. C.: Earthquake
detection through computationally efficient similarity search, Sci. Adv., 1,
e1501057, https://doi.org/10.1126/sciadv.1501057, 2015. a
Yuan, S., Liu, J., Wang, S., Wang, T., and Shi, P.: Seismic Waveform
Classification and First-Break Picking Using Convolution Neural Networks,
IEEE Geosci. Remote Sens., 15, 272–276, https://doi.org/10.1109/LGRS.2017.2785834,
2018. a
Short summary
Monitoring rock slopes for a long time helps to understand the impact of climate change on the alpine environment. Measurements of seismic signals are often affected by external influences, e.g., unwanted anthropogenic noise. In the presented work, these influences are automatically identified and removed to enable proper geoscientific analysis. The methods presented are based on machine learning and intentionally kept generic so that they can be equally applied in other (more generic) settings.
Monitoring rock slopes for a long time helps to understand the impact of climate change on the...
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