@article{fdi:010071971,
  title = {{A}ssimilation of deformation data for eruption forecasting : potentiality assessment based on synthetic cases},
  author = {{B}ato, {M}. {G}. and {P}inel, {V}irginie and {Y}an, {Y}. {J}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n monitoring active volcanoes, the magma overpressure is one of the key parameters used in forecasting volcanic eruptions. {T}his parameter can be inferred from the ground displacements measured on the {E}arth's surface by applying inversion techniques. {H}owever, in most studies, the huge amount of information about the behavior of the volcano contained in the temporal evolution of the deformation signal is not fully exploited by inversion. {O}ur work focuses on developing a strategy in order to better forecast the magma overpressure using data assimilation. {W}e take advantage of the increasing amount of geodetic data [i.e., {I}nterferometric {S}ynthetic {A}perture {R}adar ({I}n{SAR}) and {G}lobal {N}avigation {S}atellite {S}ystem ({GNSS})] recorded on volcanoes nowadays together with the wide-range availability of dynamical models that can provide better understanding about the volcano plumbing system. {H}ere, we particularly built our strategy on the basis of the {E}nsemble {K}alman {F}ilter ({E}n{KF}). {W}e forecast the temporal behaviors of the magma overpressures and surface deformations by adopting a simple and generic two-magma chamber model and by using synthetic {GNSS} and/or {I}n{SAR} data. {W}e prove the ability of {E}n{KF} to both estimate the magma pressure evolution and constrain the characteristics of the deep volcanic system (i.e., reservoir size as well as basal magma inflow). {H}igh temporal frequency of observation is required to ensure the success of {E}n{KF} and the quality of assimilation is also improved by increasing the spatial density of observations in the near-field. {W}e thus show that better results are obtained by combining a few {GNSS} temporal series of high temporal resolution with {I}n{SAR} images characterized by a good spatial coverage. {W}e also show that {E}n{KF} provides similar results to sophisticated {B}ayesian-based inversion while using the same dynamical model with the advantage of {E}n{KF} to potentially account for the temporal evolution of the uncertain model parameters. {O}ur results show that {E}n{KF} works well with the synthetic cases and there is a great potential in using the method for real-time monitoring of volcanic unrest.},
  keywords = {data assimilation ; {E}nsemble {K}alman {F}ilter ; eruption forecasting ; {I}n{SAR} ; {GNSS} ; volcano deformation},
  booktitle = {},
  journal = {{F}rontiers in {E}arth {S}cience},
  volume = {5},
  numero = {},
  pages = {48 [23 ]},
  ISSN = {2296-6463},
  year = {2017},
  DOI = {10.3389/feart.2017.00048},
  URL = {https://www.documentation.ird.fr/hor/fdi:010071971},
}