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Inference from noisy data with an unknown number of discontinuities: ideas from outside the box


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Sambridge, M, Bodin, T, Reading, AM and Gallagher, K 2010 , 'Inference from noisy data with an unknown number of discontinuities: ideas from outside the box' , ASEG Extended Abstracts, vol. 1 , pp. 1-5 , doi: 10.1071/ASEG2010ab044.

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The focus of this presentation is on new ways to extract
information from complex datasets in situations where
direct measurement is not possible. Such inverse
problems are ubiquitous across the physical and
mathematical sciences and are central to discovery of
resources within the Earth upon which Australian society
is dependent.
A recurring problem is how to choose the number of
unknowns with which to fit data. If too few are chosen
the data cannot be fit and if too many the inversion
results become unreliable and contain unwarranted detail.
Statistical methods are often used to find optimal
numbers of unknowns, but these are based on simplistic
assumptions and require multiple trial inversions to be
performed with different numbers of variables. A new
general approach recently applied to geophysical
problems is to ask the data itself `How many unknowns
should be used ?’ While this may seem counter-intuitive
at first sight it turns out to be entirely feasible. In effect
the number of unknowns itself becomes an unknown. An
extension of the basic approach also allows the level of
noise on the data to also be included as an unknown.
In this presentation we outline the central ideas, and
illustrated through an example where a geophysical
property varies only in 1-D (usually depth or time) and is
constrained from surface measurements. Applications of
the general approach are to airborne EM data, borehole
geophysics, seismic interpretation and also palaeoclimate

Item Type: Article
Authors/Creators:Sambridge, M and Bodin, T and Reading, AM and Gallagher, K
Keywords: Geophysical inversion methods, disocontinuous fields, seismic, wireline, AEM.
Journal or Publication Title: ASEG Extended Abstracts
DOI / ID Number: 10.1071/ASEG2010ab044
Additional Information:

© ASEG 2010 [earlier title: Data inference in the 21st Century: Some ideas from outside
the box]

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