Flexible Coupling in Joint Inversions: A Bayesian Structure Decoupling Algorithm

When different geophysical observables are sensitive to the same volume, it is possible to invert them simultaneously to jointly constrain different physical properties. The question addressed in this study is to determine which structures (e.g., interfaces) are common to different properties and wh...

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Bibliographic Details
Published in:Journal of geophysical research. Solid earth Vol. 123; no. 10; pp. 8798 - 8826
Main Authors: Piana Agostinetti, Nicola, Bodin, Thomas
Format: Journal Article
Language:English
Published: United States Blackwell Publishing Ltd 01.10.2018
American Geophysical Union
John Wiley and Sons Inc
Subjects:
Algorithms
Bayesian analysis
Bayesian inferences
Bayesian theory
Computer simulation
Coupling
Data
Data processing
Decoupling
Density stratification
Earth
Evolution
Geophysics
Hydrology
Informatics
Interfaces
Inverse problems
Inverse Theory
Inversions
joint inversions
Local Crustal Structure
Markov analysis
Markov chain
Markov chains
Mathematical Geophysics
Mathematical models
Monte Carlo simulation
Natural Hazards
Nonlinear Geophysics
Oceanography: General
Parameterization
Physical properties
Probability theory
Profiles
Sampling
Scaling: Spatial and Temporal
Sciences of the Universe
Seismology
Solutions
Statistical Analysis
Statistical methods
Statistical methods: Inferential
Stratification
Structural Geology
Structures
Temporal Analysis and Representation
Time Series Analysis
Time Series Experiments
trans‐dimensional algorithms
Uncertainty
Uncertainty Assessment
Uncertainty Quantification
trans‐dimensional algorithms
joint inversions
inverse problems
Bayesian inferences
trans-dimensional algorithms
ISSN:2169-9313, 2169-9356
Online Access:Get full text
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Summary:When different geophysical observables are sensitive to the same volume, it is possible to invert them simultaneously to jointly constrain different physical properties. The question addressed in this study is to determine which structures (e.g., interfaces) are common to different properties and which ones are separated. We present an algorithm for resolving the level of spatial coupling between physical properties and to enable both common and separate structures in the same model. The new approach, called structure decoupling (SD) algorithm, is based on a Bayesian trans‐dimensional adaptive parameterization, where models can display the full spectra of spatial coupling between physical properties, from fully coupled models, that is, where identical model geometries are imposed across all inverted properties, to completely decoupled models, where an independent parameterization is used for each property. We apply the algorithm to three 1‐D geophysical inverse problems, using both synthetic and field data. For the synthetic cases, we compare the SD algorithm to standard Markov chain Monte Carlo and reversible‐jump Markov chain Monte Carlo approaches that use either fully coupled or fully decoupled parameterizations. In case of coupled structures, the SD algorithm does not behave differently from methods that assume common interfaces. In case of decoupled structures, the SD approach is demonstrated to correctly retrieve the portion of profiles where the physical properties do not share the same structure. The application of the new algorithm to field data demonstrates its ability to decouple structures where a common stratification is not supported by the data. Plain Language Summary One of the present‐day challenges for geodata analysis consists in the joint inversion of an incredible number of data, where both number of observations and the number of observables are exponentially increasing. This trend in geophysics research is definitely positive and could lead to a considerable increase in our knowledge of the Earth's interior, if the correct tools are applied for making inferences out of the data. Unfortunately, our old tools for geodata analysis show all their weakness when faced to this new challenge, especially where subjective choices force models associated to different physical properties to be similar and to show the same spatial structures. Here we present the next evolution of Monte Carlo sampling applied to joint inversion of two or more geo‐observables. Monte Carlo sampling of solutions to joint inverse problem is used to infer spatial correlation between different physical properties, where supported by data, or, at the contrary, to point out Earth's volumes where such properties are varying following different structures. Key Points The SD algorithm outperforms classical algorithms in recognizing volumes where two different properties are not sharing the same structure In case of fully coupled structures, our algorithm produces comparable solutions to those offered by standard approaches The algorithm has been tested on both a standard changepoints inverse problem and a more complex geophysical joint inversion
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ISSN:2169-9313
2169-9356
DOI:10.1029/2018JB016079