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Anne Mangeney
Researcher at Institut de Physique du Globe de Paris
Publications - 159
Citations - 6599
Anne Mangeney is an academic researcher from Institut de Physique du Globe de Paris. The author has contributed to research in topics: Landslide & Free surface. The author has an hindex of 43, co-authored 150 publications receiving 5570 citations. Previous affiliations of Anne Mangeney include French Institute for Research in Computer Science and Automation & Paris Diderot University.
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Ocean wave sources of seismic noise
TL;DR: In this article, the authors presented the first comprehensive numerical model of microseismic generation by random ocean waves, including ocean wave reflections, and showed that the modeled seismic noise critically depends on the damping of seismic waves.
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Frictional velocity-weakening in landslides on Earth and on other planetary bodies
TL;DR: This work proposes an empirical velocity-weakening friction law under a unifying phenomenological framework applicable to small and large landslides observed on Earth and beyond and shows that friction decreases with increasing volume or, more fundamentally, with increasing sliding velocity.
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Erosion and mobility in granular collapse over sloping beds
TL;DR: In this paper, the authors describe laboratory experiments of granular material flowing over an inclined plane covered by an erodible bed, designed to mimic erosion processes of natural flows travelling over deposits built up by earlier events.
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Solar Wind Turbulent Spectrum at Plasma Kinetic Scales
Olga Alexandrova,Catherine Lacombe,Anne Mangeney,Roland Grappin,Roland Grappin,Milan Maksimovic +5 more
TL;DR: In this paper, a statistical study of 100 spectra measured by the STAFF instrument on the Cluster mission was performed to resolve turbulent fluctuations from ion scales down to a fraction of electron scales.
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An Automatic Kurtosis‐Based P ‐ and S ‐Phase Picker Designed for Local Seismic Networks
TL;DR: An automatic P ‐ and S ‐wave onset‐picking algorithm, using kurtosis‐derived characteristic functions (CF) and eigenvalue decompositions on three‐component seismic data, which resulted in more locations than manual picking, picking as many P onsets and twice as many S onsets as with manual picking or the STA/LTA algorithm.