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.
Highlights
The basic principle behind any geophysical inversion is to explore the subsurface properties of geological structure
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
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
Summary
The basic principle behind any geophysical inversion is to explore the subsurface properties of geological structure. This involves measuring the geophysical properties at earth’s surface and subsequently inverting the data in order to reveal the subsurface properties. Several geophysical techniques are used in this process, among which the magnetotelluric method has been applied extensively in many fields. In quantifying the geophysical inverse problem, an initial model is assumed by means of forward modeling process. The assumed initial model result is compared to the real observed measured data of the geological surface whereby the best fit model is chosen in the end. All other inversion techniques in the field of science and engineering suffer from a common problem where multiple models or descriptions can be obtained that fit or interpret very well the observed data (Sen & Stoffa, 1996)
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