Articles | Volume 5, issue 1
https://doi.org/10.5194/se-5-355-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/se-5-355-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
22 May 2014
Research article |
| 22 May 2014
Observation of a local gravity potential isosurface by airborne lidar of Lake Balaton, Hungary
A. Zlinszky
Balaton Limnological Institute, Centre for Ecological Research, Hungarian Academy of Sciences; Klebelsberg Kuno út 3, 8237 Tihany, Hungary
Vienna University of Technology, Department of Geodesy and Geoinformation; Gußhausstraße 27–29, 1040 Vienna, Austria
G. Timár
Eötvös Loránd University, Institute of Geography and Earth Science, Department of Geophysics and Space Science; Pázmány Péter Sétány 1/C, 1117 Budapest, Hungary
R. Weber
Vienna University of Technology, Department of Geodesy and Geoinformation; Gußhausstraße 27–29, 1040 Vienna, Austria
B. Székely
Interdisziplinäres Ökologisches Zentrum, TU Bergakademie Freiberg, Leipziger Str. 29, 09599 Freiberg, Germany
Eötvös Loránd University, Institute of Geography and Earth Science, Department of Geophysics and Space Science; Pázmány Péter Sétány 1/C, 1117 Budapest, Hungary
C. Briese
Ludwig Boltzmann Institute for Archaeological Prospection and Virtual Archaeology; Hohe Warte 38, 1190 Vienna, Austria
Vienna University of Technology, Department of Geodesy and Geoinformation; Gußhausstraße 27–29, 1040 Vienna, Austria
Vienna University of Technology, Department of Geodesy and Geoinformation; Gußhausstraße 27–29, 1040 Vienna, Austria
N. Pfeifer
Vienna University of Technology, Department of Geodesy and Geoinformation; Gußhausstraße 27–29, 1040 Vienna, Austria
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Günter Blöschl, Andreas Buttinger-Kreuzhuber, Daniel Cornel, Julia Eisl, Michael Hofer, Markus Hollaus, Zsolt Horváth, Jürgen Komma, Artem Konev, Juraj Parajka, Norbert Pfeifer, Andreas Reithofer, José Salinas, Peter Valent, Roman Výleta, Jürgen Waser, Michael H. Wimmer, and Heinz Stiefelmeyer
Nat. Hazards Earth Syst. Sci., 24, 2071–2091, https://doi.org/10.5194/nhess-24-2071-2024, https://doi.org/10.5194/nhess-24-2071-2024, 2024
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A methodology of regional flood hazard mapping is proposed, based on data in Austria, which combines automatic methods with manual interventions to maximise efficiency and to obtain estimation accuracy similar to that of local studies. Flood discharge records from 781 stations are used to estimate flood hazard patterns of a given return period at a resolution of 2 m over a total stream length of 38 000 km. The hazard maps are used for civil protection, risk awareness and insurance purposes.
Reuma Arav, Camillo Ressl, Robert Weiss, Thomas Artz, and Gottfried Mandlburger
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-2024, 9–16, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-9-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-9-2024, 2024
F. Pöppl, G. Mandlburger, and N. Pfeifer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W3-2023, 161–166, https://doi.org/10.5194/isprs-archives-XLVIII-1-W3-2023-161-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W3-2023-161-2023, 2023
Katharina Ramskogler, Bettina Knoflach, Bernhard Elsner, Brigitta Erschbamer, Florian Haas, Tobias Heckmann, Florentin Hofmeister, Livia Piermattei, Camillo Ressl, Svenja Trautmann, Michael H. Wimmer, Clemens Geitner, Johann Stötter, and Erich Tasser
Biogeosciences, 20, 2919–2939, https://doi.org/10.5194/bg-20-2919-2023, https://doi.org/10.5194/bg-20-2919-2023, 2023
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Primary succession in proglacial areas depends on complex driving forces. To concretise the complex effects and interaction processes, 39 known explanatory variables assigned to seven spheres were analysed via principal component analysis and generalised additive models. Key results show that in addition to time- and elevation-dependent factors, also disturbances alter vegetation development. The results are useful for debates on vegetation development in a warming climate.
B. Wild, G. Verhoeven, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-M-1-2023, 285–292, https://doi.org/10.5194/isprs-annals-X-M-1-2023-285-2023, https://doi.org/10.5194/isprs-annals-X-M-1-2023-285-2023, 2023
I. Cortesi, A. Masiero, N. Pfeifer, and G. Tucci
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 101–106, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-101-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-101-2023, 2023
F. Pöppl, H. Teufelsbauer, A. Ullrich, and N. Pfeifer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 403–410, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-403-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-403-2023, 2023
Livia Piermattei, Tobias Heckmann, Sarah Betz-Nutz, Moritz Altmann, Jakob Rom, Fabian Fleischer, Manuel Stark, Florian Haas, Camillo Ressl, Michael H. Wimmer, Norbert Pfeifer, and Michael Becht
Earth Surf. Dynam., 11, 383–403, https://doi.org/10.5194/esurf-11-383-2023, https://doi.org/10.5194/esurf-11-383-2023, 2023
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Jakob Rom, Florian Haas, Tobias Heckmann, Moritz Altmann, Fabian Fleischer, Camillo Ressl, Sarah Betz-Nutz, and Michael Becht
Nat. Hazards Earth Syst. Sci., 23, 601–622, https://doi.org/10.5194/nhess-23-601-2023, https://doi.org/10.5194/nhess-23-601-2023, 2023
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In this study, an area-wide slope-type debris flow record has been established for Horlachtal, Austria, since 1947 based on historical and recent remote sensing data. Spatial and temporal analyses show variations in debris flow activity in space and time in a high-alpine region. The results can contribute to a better understanding of past slope-type debris flow dynamics in the context of extreme precipitation events and their possible future development.
N. Homainejad, S. Zlatanova, S. M. E. Sepasgozar, and N. Pfeifer
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R. Arav, F. Pöppl, and N. Pfeifer
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N. Homainejad, S. Zlatanova, and N. Pfeifer
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G. Verhoeven, B. Wild, J. Schlegel, M. Wieser, N. Pfeifer, S. Wogrin, L. Eysn, M. Carloni, B. Koschiček-Krombholz, A. Molada-Tebar, J. Otepka-Schremmer, C. Ressl, M. Trognitz, and A. Watzinger
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2-W1-2022, 513–520, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-513-2022, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-513-2022, 2022
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Fabian Fleischer, Florian Haas, Livia Piermattei, Madlene Pfeiffer, Tobias Heckmann, Moritz Altmann, Jakob Rom, Manuel Stark, Michael H. Wimmer, Norbert Pfeifer, and Michael Becht
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A. Iglseder, M. Bruggisser, A. Dostálová, N. Pfeifer, S. Schlaffer, W. Wagner, and M. Hollaus
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 567–574, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-567-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-567-2021, 2021
J. Otepka, G. Mandlburger, W. Karel, B. Wöhrer, C. Ressl, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 35–42, https://doi.org/10.5194/isprs-annals-V-2-2021-35-2021, https://doi.org/10.5194/isprs-annals-V-2-2021-35-2021, 2021
J. Na, G. Tang, K. Wang, and N. Pfeifer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1485–1490, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1485-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1485-2020, 2020
J. Otepka, G. Mandlburger, M. Schütz, N. Pfeifer, and M. Wimmer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 293–300, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-293-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-293-2020, 2020
A-M. Loghin, N. Pfeifer, and J. Otepka-Schremmer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 525–532, https://doi.org/10.5194/isprs-annals-V-2-2020-525-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-525-2020, 2020
S. Flöry, C. Ressl, M. Hollaus, G. Pürcher, L. Piermattei, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 695–701, https://doi.org/10.5194/isprs-annals-V-2-2020-695-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-695-2020, 2020
W. Wagner, V. Freeman, S. Cao, P. Matgen, M. Chini, P. Salamon, N. McCormick, S. Martinis, B. Bauer-Marschallinger, C. Navacchi, M. Schramm, C. Reimer, and C. Briese
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 641–648, https://doi.org/10.5194/isprs-annals-V-3-2020-641-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-641-2020, 2020
Zohreh Adavi and Robert Weber
Adv. Geosci., 50, 39–48, https://doi.org/10.5194/adgeo-50-39-2019, https://doi.org/10.5194/adgeo-50-39-2019, 2019
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Here, three schemes have been considered for remedying the rank deficiency of the GNSS tomography problem. For this purpose, the Virtual Reference Stations (VRS) and horizontal and vertical constraints have been defined to analyze the impact of different constraints on the accuracy of the reconstructed refractivity field. The obtained results illustrate that applying VRS stations in the sparse GNSS Network can lead to a better solution compared to applying horizontal and vertical constraints.
N. Li and N. Pfeifer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1033–1037, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1033-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1033-2019, 2019
A. Walicka, N. Pfeifer, G. Jóźków, and A. Borkowski
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1149–1154, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1149-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1149-2019, 2019
J. Na, X. Yang, X. Fang, G. Tang, and N. Pfeifer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 469–473, https://doi.org/10.5194/isprs-archives-XLII-2-W13-469-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-469-2019, 2019
M. Bruggisser, M. Hollaus, D. Kükenbrink, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 325–332, https://doi.org/10.5194/isprs-annals-IV-2-W5-325-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-325-2019, 2019
G. Mandlburger, H. Lehner, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 397–404, https://doi.org/10.5194/isprs-annals-IV-2-W5-397-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-397-2019, 2019
P. Glira, N. Pfeifer, and G. Mandlburger
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 567–574, https://doi.org/10.5194/isprs-annals-IV-2-W5-567-2019, https://doi.org/10.5194/isprs-annals-IV-2-W5-567-2019, 2019
N. Li, N. Pfeifer, and C. Liu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 107–114, https://doi.org/10.5194/isprs-annals-IV-2-W4-107-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-107-2017, 2017
G. Mandlburger, N. Pfeifer, and U. Soergel
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 123–130, https://doi.org/10.5194/isprs-annals-IV-2-W4-123-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-123-2017, 2017
A. Roncat, N. Pfeifer, and C. Briese
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 131–137, https://doi.org/10.5194/isprs-annals-IV-2-W4-131-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-131-2017, 2017
D. Wang, M. Hollaus, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 157–164, https://doi.org/10.5194/isprs-annals-IV-2-W4-157-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-157-2017, 2017
G. Mandlburger, K. Wenzel, A. Spitzer, N. Haala, P. Glira, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 259–266, https://doi.org/10.5194/isprs-annals-IV-2-W4-259-2017, https://doi.org/10.5194/isprs-annals-IV-2-W4-259-2017, 2017
M. Pöchtrager, G. Styhler-Aydın, M. Döring-Williams, and N. Pfeifer
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W2, 195–202, https://doi.org/10.5194/isprs-annals-IV-2-W2-195-2017, https://doi.org/10.5194/isprs-annals-IV-2-W2-195-2017, 2017
A. Zlinszky, B. Deák, A. Kania, A. Schroiff, and N. Pfeifer
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1293–1299, https://doi.org/10.5194/isprs-archives-XLI-B8-1293-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-1293-2016, 2016
B. Székely, A. Kania, T. Standovár, and H. Heilmeier
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 93–99, https://doi.org/10.5194/isprs-annals-III-8-93-2016, https://doi.org/10.5194/isprs-annals-III-8-93-2016, 2016
Livia Piermattei, Luca Carturan, Fabrizio de Blasi, Paolo Tarolli, Giancarlo Dalla Fontana, Antonio Vettore, and Norbert Pfeifer
Earth Surf. Dynam., 4, 425–443, https://doi.org/10.5194/esurf-4-425-2016, https://doi.org/10.5194/esurf-4-425-2016, 2016
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We investigated the applicability of the SfM–MVS approach for calculating the geodetic mass balance of a glacier and for the detection of the surface displacement rate of an active rock glacier located in the eastern Italian Alps. The results demonstrate that it is possible to reliably quantify the investigated glacial and periglacial processes by means of a quick ground-based photogrammetric survey that was conducted using a consumer grade SRL camera and natural targets as ground control points.
Mathias Harzhauser, Ana Djuricic, Oleg Mandic, Thomas A. Neubauer, Martin Zuschin, and Norbert Pfeifer
Biogeosciences, 13, 1223–1235, https://doi.org/10.5194/bg-13-1223-2016, https://doi.org/10.5194/bg-13-1223-2016, 2016
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We present the first analysis of population structure and cohort distribution in a fossil oyster reef. Data are derived from Terrestrial Laser Scanning of a Miocene shell bed covering 459 m². A growth model was calculated, revealing this species as the giant oyster Crassostrea gryphoides was the fastest growing oyster known so far. The shell half-lives range around few years, indicating that oyster reefs were geologically short-lived structures, which were degraded on a decadal scale.
A. Zlinszky and G. Timár
Hydrol. Earth Syst. Sci., 17, 4589–4606, https://doi.org/10.5194/hess-17-4589-2013, https://doi.org/10.5194/hess-17-4589-2013, 2013
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Candidates for multiple impact craters?: Popigai and Chicxulub as seen by the global high resolution gravitational field model EGM2008
Mesay Geletu Gebre and Elias Lewi
Solid Earth, 14, 101–117, https://doi.org/10.5194/se-14-101-2023, https://doi.org/10.5194/se-14-101-2023, 2023
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In this work, a gravity inversion method that can produce compact and sharp images is presented. An auto-adaptive regularization parameter estimation method, improved error-weighting function and combined stopping rule are the contributions incorporated into the presented inversion method. The method is tested by synthetic and real gravity data, and the obtained results confirmed the potential practicality of the method.
Francesco Pintori, Enrico Serpelloni, and Adriano Gualandi
Solid Earth, 13, 1541–1567, https://doi.org/10.5194/se-13-1541-2022, https://doi.org/10.5194/se-13-1541-2022, 2022
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We study time-varying vertical deformation signals in the European
Alps by analyzing GNSS position time series. We associate the deformation
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hydrological loading are by far the most important cause of seasonal
displacements. Recognizing and filtering out non-tectonic signals allows us
to improve the accuracy and precision of the vertical velocities.
Barend Cornelis Root, Josef Sebera, Wolfgang Szwillus, Cedric Thieulot, Zdeněk Martinec, and Javier Fullea
Solid Earth, 13, 849–873, https://doi.org/10.5194/se-13-849-2022, https://doi.org/10.5194/se-13-849-2022, 2022
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Several alternative gravity modelling techniques and associated numerical codes with their own advantages and limitations are available for the solid Earth community. With upcoming state-of-the-art lithosphere density models and accurate global gravity field data sets, it is vital to understand the differences of the various approaches. In this paper, we discuss the four widely used techniques: spherical harmonics, tesseroid integration, triangle integration, and hexahedral integration.
Cedric Twardzik, Mathilde Vergnolle, Anthony Sladen, and Louisa L. H. Tsang
Solid Earth, 12, 2523–2537, https://doi.org/10.5194/se-12-2523-2021, https://doi.org/10.5194/se-12-2523-2021, 2021
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After an earthquake, the fault continues to slip for days to months. Yet, little is know about the very early part of this phase (i.e., minutes to hours). We have looked at what happens just after an earthquake in Chile from 2015. We find that the fault responds in two ways: south of the rupture zone it slips seismically in the form of aftershocks, while north of the rupture zone it slips slowly. Early inference of such bimodal behavior could prove to be useful for forecasting aftershocks.
Séverine Liora Furst, Samuel Doucet, Philippe Vernant, Cédric Champollion, and Jean-Louis Carme
Solid Earth, 12, 15–34, https://doi.org/10.5194/se-12-15-2021, https://doi.org/10.5194/se-12-15-2021, 2021
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We develop a two-step methodology combining multiple surface deformation measurements above a salt extraction site (Vauvert, France) in order to overcome the difference in resolution and accuracy. Using this 3-D velocity field, we develop a model to determine the kinematics of the salt layer. The model shows a collapse of the salt layer beneath the exploitation. It also identifies a salt flow from the deepest and most external part of the salt layer towards the center of the exploitation.
Bogdan Matviichuk, Matt King, and Christopher Watson
Solid Earth, 11, 1849–1863, https://doi.org/10.5194/se-11-1849-2020, https://doi.org/10.5194/se-11-1849-2020, 2020
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The Earth deforms as the weight of ocean mass changes with the tides. GPS has been used to estimate displacements of the Earth at tidal periods and then used to understand the properties of the Earth or to test models of ocean tides. However, there are important inaccuracies in these GPS measurements at major tidal periods. We find that combining GPS and GLONASS gives more accurate results for constituents other than K2 and K1; for these, GLONASS or ambiguity resolved GPS are preferred.
Letizia Anderlini, Enrico Serpelloni, Cristiano Tolomei, Paolo Marco De Martini, Giuseppe Pezzo, Adriano Gualandi, and Giorgio Spada
Solid Earth, 11, 1681–1698, https://doi.org/10.5194/se-11-1681-2020, https://doi.org/10.5194/se-11-1681-2020, 2020
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The Venetian Southern Alps (Italy) are located in a slowly deforming plate-boundary region where strong earthquakes occurred in the past even if seismological and geomorphological evidence is not conclusive about the specific thrust faults involved. In this study, we integrate and model different geodetic datasets of ground velocity to constrain the seismogenic potential of the studied faults, giving an example of the importance of using vertical geodetic data for seismic hazard estimates.
Wolfgang Szwillus, Jörg Ebbing, and Bernhard Steinberger
Solid Earth, 11, 1551–1569, https://doi.org/10.5194/se-11-1551-2020, https://doi.org/10.5194/se-11-1551-2020, 2020
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At the bottom of the mantle (2850 km depth) two large volumes of reduced seismic velocity exist underneath Africa and the Pacific. Their reduced velocity can be explained by an increased temperature or a different chemical composition. We use the gravity field to determine the density distribution inside the Earth's mantle and find that it favors a distinct chemical composition over a purely thermal cause.
Yu Tian and Yong Wang
Solid Earth, 11, 1121–1144, https://doi.org/10.5194/se-11-1121-2020, https://doi.org/10.5194/se-11-1121-2020, 2020
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Given the inconsistency of the plane height and also the effects of the initial density model on the inversion results, the sequential inversion of on-orbit GOCE satellite gravity gradient and terrestrial gravity are divided into two integrated processes. Some new findings are discovered through the reliable and effective inversion results in the North China Craton.
Jérémie Giraud, Mark Lindsay, Mark Jessell, and Vitaliy Ogarko
Solid Earth, 11, 419–436, https://doi.org/10.5194/se-11-419-2020, https://doi.org/10.5194/se-11-419-2020, 2020
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We propose a methodology for the identification of rock types using geophysical and geological information. It relies on an algorithm used in machine learning called
self-organizing maps, to which we add plausibility filters to ensure that the results respect base geological rules and geophysical measurements. Application in the Yerrida Basin (Western Australia) reveals that the thinning of prospective greenstone belts at depth could be due to deep structures not seen from surface.
Marc Rovira-Navarro, Wouter van der Wal, Valentina R. Barletta, Bart C. Root, and Louise Sandberg Sørensen
Solid Earth, 11, 379–395, https://doi.org/10.5194/se-11-379-2020, https://doi.org/10.5194/se-11-379-2020, 2020
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The Barents Sea and Fennoscandia were home to large ice sheets around 20 000 years ago. After the melting of these ice sheets, the land slowly rebounded. The rebound speed is determined by the viscosity of the deep Earth. The rebound is ongoing and causes small changes in the Earth’s gravity field, which can be measured by the GRACE satellite mission. We use these measurements to obtain the viscosity of the upper mantle and find that it is 2 times higher in Fennoscandia than in the Barents Sea.
Junjie Wang, Nigel T. Penna, Peter J. Clarke, and Machiel S. Bos
Solid Earth, 11, 185–197, https://doi.org/10.5194/se-11-185-2020, https://doi.org/10.5194/se-11-185-2020, 2020
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Changes in the Earth's elastic strength at increasing timescales of deformation affect predictions of its response to the shifting weight of the oceans caused by tides. We show that these changes are detectable using GPS and must be accounted for but that 3-D or locally-tuned models of the Earth's behaviour around the East China Sea provide only slightly better predictions than a simpler model which varies only with depth. Use of this model worldwide will improve precise positioning by GPS.
Thomas Frederikse, Felix W. Landerer, and Lambert Caron
Solid Earth, 10, 1971–1987, https://doi.org/10.5194/se-10-1971-2019, https://doi.org/10.5194/se-10-1971-2019, 2019
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Due to ice sheets and glaciers losing mass, and because continents get wetter and drier, a lot of water is redistributed over the Earth's surface. The Earth is not completely rigid but deforms under these changes in the load on top. This deformation affects sea-level observations. With the GRACE satellite mission, we can measure this redistribution of water, and we compute the resulting deformation. We use this computed deformation to improve the accuracy of sea-level observations.
Christine Masson, Stephane Mazzotti, Philippe Vernant, and Erik Doerflinger
Solid Earth, 10, 1905–1920, https://doi.org/10.5194/se-10-1905-2019, https://doi.org/10.5194/se-10-1905-2019, 2019
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In using dense geodetic networks and large GPS datasets, we are able to extract regionally coherent velocities and deformation rates in France and neighboring western European countries. This analysis is combined with statistical tests on synthetic data to quantify the deformation detection thresholds and significance levels.
Evren Pakyuz-Charrier, Mark Jessell, Jérémie Giraud, Mark Lindsay, and Vitaliy Ogarko
Solid Earth, 10, 1663–1684, https://doi.org/10.5194/se-10-1663-2019, https://doi.org/10.5194/se-10-1663-2019, 2019
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This paper improves the Monte Carlo simulation for uncertainty propagation (MCUP) method for 3-D geological modeling. Topological heterogeneity is observed in the model suite. The study demonstrates that such heterogeneity arises from piecewise nonlinearity inherent to 3-D geological models and contraindicates use of global uncertainty estimation methods. Topological-clustering-driven uncertainty estimation is proposed as a demonstrated alternative to address plausible model heterogeneity.
Maurizio Milano, Maurizio Fedi, and J. Derek Fairhead
Solid Earth, 10, 697–712, https://doi.org/10.5194/se-10-697-2019, https://doi.org/10.5194/se-10-697-2019, 2019
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In this work we aim to interpret the extended magnetic low visible at satellite altitudes above central Europe by performing a joint analysis of magnetic field and total gradient intensity maps at low and high altitudes. Here we demonstrate that such a magnetic anomaly is mainly a result of the contrast between two crustal platforms differing strongly in geological and magnetic properties. Synthetic model tests have been created to support our modeling.
Martin Kobe, Gerald Gabriel, Adelheid Weise, and Detlef Vogel
Solid Earth, 10, 599–619, https://doi.org/10.5194/se-10-599-2019, https://doi.org/10.5194/se-10-599-2019, 2019
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Subrosion, i.e. the underground leaching of soluble rocks, causes disastrous sinkhole events worldwide. We investigate the accompanying mass transfer using quarter-yearly time-lapse gravity campaigns over 4 years in the town of Bad Frankenhausen, Germany. After correcting for seasonal soil water content, we find evidence of underground mass loss and attempt to quantify its amount. This is the first study of its kind to prove the feasibility of this approach in an urban area.
Christine Masson, Stephane Mazzotti, and Philippe Vernant
Solid Earth, 10, 329–342, https://doi.org/10.5194/se-10-329-2019, https://doi.org/10.5194/se-10-329-2019, 2019
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We use statistical analyses of synthetic position time series to estimate the potential precision of GPS velocities. Regression tree analyses show that the main factors controlling the velocity precision are the duration of the series, the presence of offsets, and the noise. Our analysis allows us to propose guidelines which can be applied to actual GPS data that constrain the velocity accuracies.
Sergei Rudenko, Saskia Esselborn, Tilo Schöne, and Denise Dettmering
Solid Earth, 10, 293–305, https://doi.org/10.5194/se-10-293-2019, https://doi.org/10.5194/se-10-293-2019, 2019
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A terrestrial reference frame (TRF) realization is a basis for precise orbit determination of Earth-orbiting artificial satellites and sea level studies. We investigate the impact of a switch from an older TRF realization (ITRF2008) to a new one (ITRF2014) on the quality of orbits of three altimetry satellites (TOPEX/Poseidon, Jason-1, and Jason-2) for 1992–2015, but especially from 2009 onwards, and on altimetry products computed using the satellite orbits derived using ITRF2014.
Jeremie Giraud, Mark Lindsay, Vitaliy Ogarko, Mark Jessell, Roland Martin, and Evren Pakyuz-Charrier
Solid Earth, 10, 193–210, https://doi.org/10.5194/se-10-193-2019, https://doi.org/10.5194/se-10-193-2019, 2019
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We propose the quantitative integration of geology and geophysics in an algorithm integrating the probability of observation of rocks with gravity data to improve subsurface imaging. This allows geophysical modelling to adjust models preferentially in the least certain areas while honouring geological information and geophysical data. We validate our algorithm using an idealized case and apply it to the Yerrida Basin (Australia), where we can recover the geometry of buried greenstone belts.
Karen M. Simon, Riccardo E. M. Riva, Marcel Kleinherenbrink, and Thomas Frederikse
Solid Earth, 9, 777–795, https://doi.org/10.5194/se-9-777-2018, https://doi.org/10.5194/se-9-777-2018, 2018
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This study constrains the post-glacial rebound signal in Scandinavia and northern Europe via the combined inversion of prior forward model information with GPS-measured vertical land motion data and GRACE gravity data. The best-fit model for vertical motion rates has a χ2 value of ~ 1 and a maximum uncertainty of 0.3–0.4 mm yr−1. An advantage of inverse models relative to forward models is their ability to estimate formal uncertainties associated with the post-glacial rebound process.
Christina Lück, Jürgen Kusche, Roelof Rietbroek, and Anno Löcher
Solid Earth, 9, 323–339, https://doi.org/10.5194/se-9-323-2018, https://doi.org/10.5194/se-9-323-2018, 2018
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Since 2002, the GRACE mission provides estimates of the Earth's time-variable gravity field, from which one can derive ocean mass variability. Now that the GRACE mission has come to an end, it is especially important to find alternative ways for deriving ocean mass changes. For the first time, we use kinematic orbits of Swarm for computing ocean mass time series. We compute monthly solutions, but also show an alternative way of directly estimating time-variable spherical harmonic coefficients.
Hai Ninh Nguyen, Philippe Vernant, Stephane Mazzotti, Giorgi Khazaradze, and Eva Asensio
Solid Earth, 7, 1349–1363, https://doi.org/10.5194/se-7-1349-2016, https://doi.org/10.5194/se-7-1349-2016, 2016
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We present a new 3-D GPS velocity solution for 182 sites for the region encompassing the Western Alps, Pyrenees. The only significant horizontal deformation (0.2 mm/yr over a distance of 50 km) is a NNE–SSW extension in the western Pyrenees. In contrast, significant uplift rates up to 2 mm/yr occur in the Western Alps but not in the Pyrenees. A correlation between site elevations and fast uplift rates in the Western Alps suggests that part of this uplift is induced by postglacial rebound.
Gang Chen, Anmin Zeng, Feng Ming, and Yifan Jing
Solid Earth, 7, 817–825, https://doi.org/10.5194/se-7-817-2016, https://doi.org/10.5194/se-7-817-2016, 2016
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In this paper, we presented a new method on the basis of a collocation and multi-quadric equation interpolation. We introduce a multi-quadric kernel function to determine the covariance of local deformation and use the method to be a simple approximation of the covariance function. We established a horizontal velocity field model for the Chinese mainland by using a set of observed velocity data of GPS stations. The result is simple and reasonable and has a significant reference value.
M. Nordman, M. Poutanen, A. Kairus, and J. Virtanen
Solid Earth, 5, 673–681, https://doi.org/10.5194/se-5-673-2014, https://doi.org/10.5194/se-5-673-2014, 2014
S. Rudenko, N. Schön, M. Uhlemann, and G. Gendt
Solid Earth, 4, 23–41, https://doi.org/10.5194/se-4-23-2013, https://doi.org/10.5194/se-4-23-2013, 2013
T. Frontera, A. Concha, P. Blanco, A. Echeverria, X. Goula, R. Arbiol, G. Khazaradze, F. Pérez, and E. Suriñach
Solid Earth, 3, 111–119, https://doi.org/10.5194/se-3-111-2012, https://doi.org/10.5194/se-3-111-2012, 2012
J. Klokočník, J. Kostelecký, I. Pešek, P. Novák, C. A. Wagner, and J. Sebera
Solid Earth, 1, 71–83, https://doi.org/10.5194/se-1-71-2010, https://doi.org/10.5194/se-1-71-2010, 2010
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