HR: 16:15h
AN: NG34A-02 INVITED [Abstracts]
TI: Bayesian Approach to the Joint Inversion of Gravity and Magnetic Data, with Application to the Ismenius
Area of Mars
AU: * Jewell, J
EM: Jeffrey.Jewell@jpl.nasa.gov
AF: JPL
Jeffrey Jewell, Carol Raymond, Sue Smrekar, MS 126-347
4800 Oak Grove Dr., Pasadena, CA 91109
United States
AU: Raymond, C
EM: Carol.Raymond@jpl.nasa.gov
AF: JPL
Jeffrey Jewell, Carol Raymond, Sue Smrekar, MS 126-347
4800 Oak Grove Dr., Pasadena, CA 91109
United States
AU: Smrekar, S
EM: Sue.Smrekar@jpl.nasa.gov
AF: JPL
Jeffrey Jewell, Carol Raymond, Sue Smrekar, MS 126-347
4800 Oak Grove Dr., Pasadena, CA 91109
United States
AU: Milbury, C
EM: cmilbury@ess.ucla.edu
AF: UCLA DEpt. of Earth and Space Sciences
Colleen Milbury, 595 Charles Young Drive East
Box 951567, Los Angeles, CA 90095
United States
AB:
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 the 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 incoporated 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 undertainty, and what has been learned
from observations. We will discuss advanced numerical techniques,
including Monte Carlo Markov Chain methods, for a Bayesian approach
to the joint inversion of gravity and magnetic data for
the Ismenious area on Mars, and for geophysical problems in
general.
DE: 5417 Gravitational fields (1227)
DE: 5440 Magnetic fields and magnetism
DE: 3260 Inverse theory
SC: Nonlinear Geophysics [NG]
MN: 2004 AGU Fall Meeting