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SUMMARY:Radical uncertainty in subsurface science: How do we make decision
 s? - Andrew Curtis -- University of Edinburgh
DTSTART:20231122T160000Z
DTEND:20231122T170000Z
UID:TALK205381@talks.cam.ac.uk
CONTACT:Yihe Xu
DESCRIPTION:The impact of subsurface science can often be assessed by deci
 sions that it affects. Scientists decide whether to adopt newly proposed m
 odels\, scientific evaluation of the likelihood of hazards impacts on the 
 decision between mitigating actions\, and estimates of subsurface properti
 es and dynamics affect economically important decisions. In all cases an a
 ssessment of uncertainty in the scientific results allows decisions to be 
 better informed\, and more robust to possible variations.\nSeismic Tomogra
 phy is a method to image the Earth’s subsurface\, and is used to inform 
 all three types of decisions outlined above. In order to better interpret 
 the resulting images it is important to assess imaging uncertainties\, but
  this is hard to achieve. Monte Carlo random sampling methods are often ap
 plied for this purpose but the ‘curse of dimensionality’ makes them co
 mputationally intractable for high-dimensional parameter spaces. This talk
  extends uncertainty analysis to larger systems by introducing new variati
 onal inference methods to seismic tomography. In contrast to random sampli
 ng\, variational methods solve an optimization problem yet provide probabi
 listic results. Even the most demanding uncertainty assessments\, in three
 -dimensional full seismic waveform tomography\, are achievable to some ext
 ent.\nA remaining difficulty is that geoscientific models are always wrong
 \, and are usually biased in unknown ways. Real world uncertainties beyond
  the modelled domain are therefore unknown and potentially are unknowable.
  In a world of so-called radical uncertainty\, in which true probabilities
  are unable to be assessed\, a new approach to decision making will be req
 uired\, as will be discussed.\n
LOCATION:Wolfson Lecture Theatre
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