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Bayesian Approach to the Joint Inversion of Gravity and Magnetic Data, with Application to the Ismenius Area of MarsThis viewgraph presentation reviews a Bayesian approach to the inversion of gravity and magnetic data with specific application to the Ismenius Area of Mars. Many inverse problems encountered in geophysics and planetary science are well known to be non-unique (i.e. inversion of gravity the density structure of a body). In hopes of reducing the non-uniqueness of solutions, there has been interest in the joint analysis of data. An example is the joint inversion of gravity and magnetic data, with the assumption that the same physical anomalies generate both the observed magnetic and gravitational anomalies. In this talk, we formulate the joint analysis of different types of data in a Bayesian framework and apply the formalism to the inference of the density and remanent magnetization structure for a local region in the Ismenius area of Mars. The Bayesian approach allows prior information or constraints in the solutions to be incorporated in the inversion, with the "best" solutions those whose forward predictions most closely match the data while remaining consistent with assumed constraints. The application of this framework to the inversion of gravity and magnetic data on Mars reveals two typical challenges - the forward predictions of the data have a linear dependence on some of the quantities of interest, and non-linear dependence on others (termed the "linear" and "non-linear" variables, respectively). For observations with Gaussian noise, a Bayesian approach to inversion for "linear" variables reduces to a linear filtering problem, with an explicitly computable "error" matrix. However, for models whose forward predictions have non-linear dependencies, inference is no longer given by such a simple linear problem, and moreover, the uncertainty in the solution is no longer completely specified by a computable "error matrix". It is therefore important to develop methods for sampling from the full Bayesian posterior to provide a complete and statistically consistent picture of model uncertainty, and what has been learned from observations. We will discuss advanced numerical techniques, including Monte Carlo Markov
Document ID
20090007931
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Authors
Jewell, Jeffrey B.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Raymond, C.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Smrekar, S.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Millbury, C.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Date Acquired
August 24, 2013
Publication Date
December 15, 2004
Subject Category
Lunar And Planetary Science And Exploration
Meeting Information
Meeting: American Geophysical Union Fall Meeting
Location: San Francisco, CA
Country: United States
Start Date: December 14, 2004
Distribution Limits
Public
Copyright
Other
Keywords
data analysis
gravity
magnetic inversion
inverse problems

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