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SUMMARY:Advances in nonlinear geoscientific experimental and survey design
  - Curtis\, A (University of Edinburgh)
DTSTART:20110722T080000Z
DTEND:20110722T090000Z
UID:TALK32125@talks.cam.ac.uk
CONTACT:Mustapha Amrani
DESCRIPTION:Geoscience is replete with inverse problems that must be solve
 d  routinely. Many such problems such as using satellite remote-sensing da
 ta to estimate properties of the Earth's surface\, or solving Geophysical 
 imaging and monitoring problems for potentially dynamic  properties of the
  Earth's subsurface\, involve large datasets that cost  millions of dollar
 s to collect. Optimising the information content of such data is therefore
  crucial. While linearised experimental design methods have been deployed 
 within the Geosciences\, most Geophysical problems are significantly nonli
 near. This renders linearised design criteria invalid as they can signific
 antly over- or under-estimate the information content of any  dataset. Ove
 r the past few years we have therefore focussed on  developing new nonline
 ar design methods that can be applied to practical  data types and geometr
 ies for surveys of increasing size. We will summarise three advances in pr
 actical nonlinear design\, one using  a new design criterion applied in th
 e data space\, one using a new 'bi-focal' model space criterion\, and one 
 using a fast Monte Carlo  refinement procedure that significantly speeds u
 p nonlinear design calculations. Applications of the first two techniques 
 are to design subsurface (micro-)seismic energy-source location problems\,
  application  of the third is to design so-called industrial seismic  ampl
 itude-versus-offset data sets to derive (an)elastic properties of  subsurf
 ace geological strata. Using the first of these we managed to design an in
 dustrially practical Geophysical survey design using fully non-linearised 
 methods.\n
LOCATION:Seminar Room 1\, Newton Institute
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