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BEGIN:VEVENT
UID:news222@dmi.unibas.ch
DTSTAMP;TZID=Europe/Zurich:20180716T180942
DTSTART;TZID=Europe/Zurich:20180525T113000
SUMMARY:Seminar in Numerical Analysis: Ludovic Métivier (Université Greno
 ble Alpes)
DESCRIPTION:Full waveform inversion (FWI) is a powerful high resolution sei
 smic  imaging method\, used in the academy for global and regional scale  
 imaging\, and in the oil & gas industry for exploration purposes. It  can 
 be understood as a PDE constrained optimization problem: the misfit  betwe
 en recorded seismic data and synthetic seismic data computed as the  solut
 ion of a wave propagation problem is reduced over a space of  parameters c
 ontrolling the wave propagation. One of the main limitation  of FWI is its
  dependency on the accuracy of the initial guess of the  solution. This li
 mitation is due to the non-convexity of the standard  least-squares misfit
  function used to measure the discrepancy between  recorded and synthetic 
 data\, and the use of local optimization  techniques to reduce this misfit
 . In recent studies\, we have studied the  interest for using a misfit fun
 ction based on an optimal transport  distance to mitigate this issue. The 
 convexity of this distance with  respect to shifted patterns is the main r
 eason why we are interested in  this distance\, as it can be seen as a pro
 xy for the convexity with  respect to the wave velocities we want to  reco
 nstruct. In this talk\, we  will give an overview of this work\, starting 
 by introducing basic  concepts on optimal transport\, before detailing the
  difficulties for  using the optimal transport distance in the framework o
 f FWI\, and  reviewing the solutions we have proposed.
X-ALT-DESC:Full waveform inversion (FWI) is a powerful high resolution seis
 mic  imaging method\, used in the academy for global and regional scale  i
 maging\, and in the oil &amp\; gas industry for exploration purposes. It  
 can be understood as a PDE constrained optimization problem: the misfit  b
 etween recorded seismic data and synthetic seismic data computed as the  s
 olution of a wave propagation problem is reduced over a space of  paramete
 rs controlling the wave propagation. One of the main limitation  of FWI is
  its dependency on the accuracy of the initial guess of the  solution. Thi
 s limitation is due to the non-convexity of the standard  least-squares mi
 sfit function used to measure the discrepancy between  recorded and synthe
 tic data\, and the use of local optimization  techniques to reduce this mi
 sfit. In recent studies\, we have studied the  interest for using a misfit
  function based on an optimal transport  distance to mitigate this issue. 
 The convexity of this distance with  respect to shifted patterns is the ma
 in reason why we are interested in  this distance\, as it can be seen as a
  proxy for the convexity with  respect to the wave velocities we want to  
 reconstruct. In this talk\, we  will give an overview of this work\, start
 ing by introducing basic  concepts on optimal transport\, before detailing
  the difficulties for  using the optimal transport distance in the framewo
 rk of FWI\, and  reviewing the solutions we have proposed. 
DTEND;TZID=Europe/Zurich:20180525T123000
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