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Bayesian inversion using nested trans-dimensional Gaussian processes
To understand earth processes, geoscientists infer subsurface earth properties such as electromagnetic resistivity or seismic velocity from surface observations of electromagnetic or seismic data. These properties are used to populate an earth model vector, and the spatial variation of properties across this vector sheds light on the underlying earth structure or physical phenomenon of interest, from groundwater aquifers to plate tectonics. However, to infer these properties the spatial characteristics of these properties need to be known in advance. Typically, assumptions are made about the length scales of earth properties, which are encoded a priori in a Bayesian probabilistic setting. In an optimisation setting, appeals are made to promote model simplicity together with constraints which keep models close to a preferred model. All of these approaches are valid, though they can lead to unintended features in the resulting inferred geophysical models owing to inappropriate prior assumptions, constraints or even the nature of the solution basis functions. In this work it will be shown that in order to make accurate inferences about earth properties, inferences can first be made about the underlying length scales of these properties in a very general solution basis. From a mathematical point of view, these spatial characteristics of earth properties can be conveniently thought of as “properties” of the earth properties. Thus, the same machinery used to infer earth properties can be used to infer their length scales. This can be thought of as an “infer to infer” paradigm analogous to the “learning to learn” paradigm which is now commonplace in the machine learning literature. However, it must be noted that (geophysical) inference is not the same as (machine) learning, though there are many common elements which allow for cross-pollination of useful ideas from one field to the other, as is shown here. A non-stationary trans-dimensional Gaussian Process (TDGP) is used to parameterise earth properties, and a multi-channel stationary TDGP is used to parameterise the length scales associated with the earth property in question. Using non-stationary kernels, i.e., kernels with spatially variable length scales, models with sharp discontinuities can be represented within this framework. As GPs are multi-dimensional interpolators, the same theory and computer code can be used to solve geophysical problems in 1D, 2D and 3D. This is demonstrated through a combination of 1D and 2D non-linear regression examples and a controlled source electromagnetic (CSEM) field example. The key difference between this and previous work using TDGP is generalised nested inference and the marginalisation of prior length scales for better posterior subsurface property characterisation.
<b>Citation:</b> Anandaroop Ray, Bayesian inversion using nested trans-dimensional Gaussian processes, <i>Geophysical Journal International</i>, Volume 226, Issue 1, July 2021, Pages 302–326,
<a href=" https://doi.org/10.1093/gji/ggab114">https://doi.org/10.1093/gji/ggab114</a>
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- Date (Released)
- 2021-03-26
- Date (Publication)
- 2023-10-22T23:22:50
- Citation identifier
- Geoscience Australia Persistent Identifier/https://pid.geoscience.gov.au/dataset/ga/144380
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Digital Object Identifier
- Cited responsible party
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Role Organisation / Individual Name Details Author Ray, A.
MEG Internal Contact Publisher Oxford University Press
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Geophysical Journal International
- Issue identification
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Volume 226, Issue 1
- Page
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302-326
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Article written to submit to Geophysical Journal international
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- Completed
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Role Organisation / Individual Name Details Point of contact Commonwealth of Australia (Geoscience Australia)
Voice Point of contact Ray, A.
MEG Internal Contact Resource provider Minerals, Energy and Groundwater Division
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- Geoscientific information
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Product data repository: Various Formats
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Data Store directory containing the digital product files
Data Store directory containing one or more files, possibly in a variety of formats, accessible to Geoscience Australia staff only for internal purposes
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MATHEMATICAL SCIENCES
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EARTH SCIENCES
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- Keywords
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Inverse theory
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- Keywords
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Probability distributions
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Published_External
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Creative Commons Attribution 4.0 International Licence
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CC-BY
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4.0
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(c) The Author(s) 2021
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Australian Government Security ClassificationSystem
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- 2018-11-01T00:00:00
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- Unclassified
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- English
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Role Organisation / Individual Name Details Distributor Commonwealth of Australia (Geoscience Australia)
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Article written to submit to Geophysical Journal international
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Australian Government Security Classification System
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- 2018-11-01T00:00:00
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urn:uuid/0dda9301-e861-4887-b293-67c68d9c5ec4
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GeoNetwork UUID
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Role Organisation / Individual Name Details Point of contact Commonwealth of Australia (Geoscience Australia)
Voice Owner Ray, A.
MEG Internal Contact Point of contact Ray, A.
MEG Internal Contact
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- Document
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Journal Articles and Conference Papers
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Geoscience Australia - short identifier for metadata record with
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- eCatId/144380
- Date info (Creation)
- 2019-04-08T01:55:29
- Date info (Revision)
- 2019-04-08T01:55:29
Metadata standard
- Title
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AU/NZS ISO 19115-1:2014
Metadata standard
- Title
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ISO 19115-1:2014
Metadata standard
- Title
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ISO 19115-3
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Geoscience Australia Community Metadata Profile of ISO 19115-1:2014
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Version 2.0, September 2018
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- https://pid.geoscience.gov.au/dataset/ga/122551