A Bayesian Inference Approach to Reduce Uncertainty in Magnetotelluric Inversion: A Synthetic Case Study ()
Affiliation(s)
1School of Geophysics, Yangtze University, Wuhan, China.
2Key Laboratory of Exploration Technologies for Oil and Gas Resources, Yangtze University, Wuhan, China.
3Depertment of Physics and Biochemical Sciences, University of Malawi, Blantyre, Malawi.
4State Key Laboratory of Isotope Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, China.
5College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China.
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.
Share and Cite:
Kachaje, O. , Yan, L. and Zhang, Z. (2019) A Bayesian Inference Approach to Reduce Uncertainty in Magnetotelluric Inversion: A Synthetic Case Study.
Journal of Geoscience and Environment Protection,
7, 62-75. doi:
10.4236/gep.2019.72005.
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