Stochastic methods for model assessment of airborne frequency-domain electromagnetic data

Details

Serval ID
serval:BIB_74204B04D1CF
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Stochastic methods for model assessment of airborne frequency-domain electromagnetic data
Author(s)
Minsley B., Irving J., Abraham J., Smith B.
Publisher
ASEG Extended Abstract
Organization
22nd Annual ASEG Meeting, Brisbane, Australia
Publication state
Published
Issued date
2012
Language
english
Notes
Minsley2012
Abstract
Bayesian Markov chain Monte Carlo (MCMC) algorithms are introduced
for the analysis of one- and two-dimensional airborne frequency-domain
electromagnetic datasets. Substantial information about parameter
uncertainty, non-uniqueness, correlation, and depth of investigation
are revealed from the MCMC analysis that cannot be obtained using
traditional least-squares methods. In the one-dimensional analysis,
a trans-dimensional algorithm allows the number of layers to be unknown,
implicitly favouring models with fewer layers. Assessment of data
errors and systematic instrumentation errors can also be incorporated.
An example from western Nebraska shows that the MCMC analysis reveals
important details about the subsurface that are not identified using
a single ?best-fit? model. A geostatistical facies-based parameterization
is introduced in order to reduce the number of underlying parameters
for the two-dimensional MCMC analysis. This parameterization naturally
incorporates lateral constraints in the proposed models, which is
important for efficiently sampling the model space. A transdimensional
component can be optionally incorporated in the two-dimensional algorithm
by allowing the number of facies to vary, but with models that contain
fewer facies implicitly favoured.
Open Access
Yes
Create date
25/11/2013 18:31
Last modification date
21/08/2019 6:12
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