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Addressing the issue of model error in stochastic geophysical inversion
James Irving
, Corinna Köpke
,
Ahmed Elsheikh
Institute for GeoEnergy Engineering
School of Energy, Geoscience, Infrastructure and Society
Research output
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Chapter in Book/Report/Conference proceeding
›
Conference contribution
Overview
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Engineering
Forward Solver
100%
Subsurface
66%
Model Parameter
66%
Forward Model
66%
Statistical Distribution
33%
Measured Data
33%
Physical Parameter
33%
Stochastic Model
33%
Ground Penetrating Radar Systems
33%
Global Error
33%
Orthogonal Projection
33%
Local Error
33%
Local Model
33%
Realization
33%
Measurement Error
33%
Mathematics
Stochastics
100%
Residuals
66%
Subsurface
66%
Bayesian
33%
Parametric
33%
Test Set
33%
Synthetic Data
33%
Traveltime
33%
Approximates
33%
Markov Chain Monte Carlo
33%
Stochastic Model
33%
Measurement Error
33%
Orthogonal Projection
33%
Statistical Distribution
33%
INIS
stochastic processes
100%
errors
100%
data
27%
approximations
18%
solutions
18%
dictionaries
18%
statistics
9%
distribution
9%
monte carlo method
9%
markov process
9%
chains
9%
layers
9%
yields
9%
equations
9%
tomography
9%
sampling
9%
interpolation
9%
physics
9%
hydrology
9%
geophysics
9%
eikonal approximation
9%
Earth and Planetary Sciences
Inverse Problem
100%
Interpolation
25%
Ground Penetrating Radar
25%
Tomography
25%
Data Integration
25%
Eikonal Equation
25%
Geophysics
25%
Markov Chain Monte Carlo
25%
Statistical Distribution
25%