Abstract
Real magnetotelluric (MT) data errors are commonly correlated, but MT inversions routinely neglect such correlations without an investigation on the impact of this simplification. This paper applies a hierarchical trans-dimensional (trans-D) Bayesian inversion to examine the effect of correlated MT data errors on the inversion for subsurface geoelectrical structures, and the model parameterization (the number of conductivity interfaces) is treated as an unknown. In the inversion considering error correlations, the data errors are parameterized by the first-order autoregressive (AR(1)) process, which is included as an unknown in the inversion. The data information itself determines the AR(1) parameter. The trans-D inversion applies the reversible-jump Markov chain Monte Carlo algorithm to sample the trans-D posterior probability density (PPD) for the model parameters, model parameterization and AR(1) parameters, accounting for the uncertainties of the model dimension and data error correlation in the uncertainty estimates of the conductivity profile. In the inversion ignoring the correlation, we neglect the correlation effect by turning off the AR(1) parameter. Then the correlation effect on the MT inversion can be examined upon comparing the posterior marginal conductivity profiles from the two inversions. Further investigation is then carried out for a synthetic case and a real MT data example. The results indicate that for strong correlation cases, neglecting error correlations can significantly affect the inversion results.
Highlights
Magnetotelluric (MT) data inversions are widely carried out to understand the subsurface geoelectrical structure with applications such as geothermal investigations (Heise et al 2008; He et al 2016), exploration for ore deposits (Zhang and Chouteau 1992) and hydrocarbon reservoirs (He et al 2010), and tectonic imaging (Becken and Ritter 2012; Espurt et al 2014; Goto et al 2005)
MT inversions are commonly conducted based on the assumption that the data errors are uncorrelated either over frequencies or over space with the consideration of variances in the inversion (e.g., Yoshimura et al 2018; Grayver 2015; Guo et al 2011; Usui et al 2016; Wheelock 2012)
Frequency-correlated noise can arise from the data measurement process (Egbert 1997; Eisel and Egbert 2001) and the simplified error statistics used in the inversion
Summary
Magnetotelluric (MT) data inversions are widely carried out to understand the subsurface geoelectrical structure with applications such as geothermal investigations (Heise et al 2008; He et al 2016), exploration for ore deposits (Zhang and Chouteau 1992) and hydrocarbon reservoirs (He et al 2010), and tectonic imaging (Becken and Ritter 2012; Espurt et al 2014; Goto et al 2005). A quantitative examination of the effect of neglecting error correlations over a range of frequencies is performed based on a trans-D Bayesian inversion, in which the uncertainty of the model parameterization is accounted for in the posterior and is included in the uncertainties of the inversion results.
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