TITLE:
A Bayesian Inference Approach to Reduce Uncertainty in Magnetotelluric Inversion: A Synthetic Case Study
AUTHORS:
Osborne Kachaje, Liangjun Yan, Zhou Zhang
KEYWORDS:
Bayesian Inversion, Magnetotellurics, MCMC, Metropolis-Hastings, Uncertainty
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.7 No.2,
February
18,
2019
ABSTRACT: The deterministic geophysical inversion methods are dominant when inverting
magnetotelluric data whereby its results largely depends on the assumed
initial model and only a single representative solution is obtained. A
common problem to this approach is that all inversion techniques suffer
from non-uniqueness since all model solutions are subjected to errors, under-determination and uncertainty. A statistical approach in nature is a
possible solution to this problem as it can provide extensive information
about unknown parameters. In this paper, we developed a 1D Bayesian inversion
code based Metropolis-Hastings algorithm whereby the uncertainty
of the earth model parameters were quantified by examining the posterior
model distribution. As a test, we applied the inversion algorithm to synthetic
model data obtained from available literature based on a three layer
model (K, H, A and Q). The frequency for the magnetotelluric impedance
data was generated from 0.01 to 100 Hz. A 5% Gaussian noise was added at
each frequency in order to simulate errors to the synthetic results. The developed
algorithm has been successfully applied to all types of models and
results obtained have demonstrated a good compatibility with the initial
synthetic model data.