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  • Controlled source electromagnetics (CSEM)  (1)
  • Physics of magma and magma bodies  (1)
  • Oxford University Press  (2)
  • 2015-2019  (2)
  • 1945-1949
  • 2018  (2)
  • 1947
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Publisher
  • Oxford University Press  (2)
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  • 2015-2019  (2)
  • 1945-1949
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  • 1
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    Oxford University Press
    Publication Date: 2022-05-25
    Description: Author Posting. © The Authors, 2018. This article is posted here by permission of The Royal Astronomical Society for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 215 (2018): 713–735, doi:10.1093/gji/ggy313.
    Description: Gas flux in volcanic conduits is often associated with long-period oscillations known as seismic tremor (Lesage et al.; Nadeau et al.). In this study, we revisit and extend the ‘magma wagging’and ‘whirling’models for seismic tremor, in order to explore the effects of gas flux on the motion of a magma column surrounded by a permeable vesicular annulus (Jellinek & Bercovici; Bercovici et al.; Liao et al.). We find that gas flux flowing through the annulus leads to a Bernoulli effect, which causes waves on the magma column to become unstable and grow. Specifically, the Bernoulli effects are associated with torques and forces acting on the magma column, increasing its angular momentum and energy. As the displacement of the magma column becomes large due to the Bernoulli effect, frictional drag on the conduit wall decelerates the motions of the column, restoring them to small amplitude. Together, the Bernoulli effect and the damping effect contribute to a self-sustained wagging-and-whirling mechanism that help explain the longevity of long-period seismic tremor.
    Description: This work was supported by National Science Foundation grants EAR-1344538 and EAR-1645057
    Keywords: Physics of magma and magma bodies ; Volcano seismology ; Volcanic gases
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2022-05-25
    Description: Author Posting. © The Authors, 2018. This article is posted here by permission of The Royal Astronomical Society for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 215 (2018): 460–473, doi:10.1093/gji/ggy152.
    Description: In this work, we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine-learning techniques. Non-negative matrix factorization is used to determine grain-size end-members from sediment surface samples. Four end-members were found, which well represent the variety of sediments in the study area. A radial basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.
    Description: This work was funded through DFG Research Center/Cluster of Excellence ‘The Ocean in the Earth System’ and was part of MARUM Research Area SD
    Keywords: Neural networks ; Fuzzy logic ; Statistical methods ; Electrical properties ; Magnetic properties ; Marine electromagnetics ; Controlled source electromagnetics (CSEM)
    Repository Name: Woods Hole Open Access Server
    Type: Article
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