@article{fdi:010077370,
  title = {{C}ombining {I}n{SAR} and {GNSS} to track magma transport at basaltic volcanoes},
  author = {{S}mittarello, {D}. and {C}ayol, {V}. and {P}inel, {V}irginie and {F}roger, {J}. {L}. and {P}eltier, {A}. and {D}umont, {Q}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he added value of combining {I}n{SAR} and {GNSS} data, characterized by good spatial coverage and high temporal resolution, respectively, is evaluated based on a specific event: the propagation of the magma intrusion leading to the 26 {M}ay 2016 eruption at {P}iton de la {F}ournaise volcano ({R}eunion {I}sland, {F}rance). {S}urface displacement is a non linear function of the geometry and location of the pressurized source of unrest, so inversions use a random search, based on a neighborhood algorithm, combined with a boundary element modeling method. {W}e first invert {I}n{SAR} and {GNSS} data spanning the whole event (propagation phase and eruption) to determine the final geometry of the intrusion. {R}andom search conducted in the inversion results in two best-fit model families with similar data fits. {A}dding the same time-period {GNSS} dataset to the inversions does not significantly modify the results. {E}ven when weighting data to provide even contributions, the fit is systematically better for descending than ascending interferograms, which might indicate an eastward flank motion. {T}hen, we invert the {GNSS} time series in order to derive information on the propagation dynamics, validating our approach using a {SAR} image acquired during the propagation phase. {W}e show that the {GNSS} time series can only be used to correctly track the magma propagation when the final intrusion geometry derived from {I}n{SAR} and {GNSS} measurements is used as an a priori. {A} new method to extract part of a mesh, based on the representation of meshes as graphs, better explains the data and better accounts for the opening of the eruptive fissure than a method based on the projection of a circular pressure sources. {F}inally, we demonstrate that the temporal inversion of {GNSS} data strongly favors one family of models over an other for the final intrusion, removing the ambiguity inherent in the inversion of {I}n{SAR} data.},
  keywords = {{I}n{SAR} ; {GNSS} ; {P}iton de la {F}ournaise ; dike propagation ; inversion ; joint ; inversion ; {REUNION} ; {FOURNAISE} {PITON}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {11},
  numero = {19},
  pages = {[26 ]},
  year = {2019},
  DOI = {10.3390/rs11192236},
  URL = {https://www.documentation.ird.fr/hor/fdi:010077370},
}