Environmental and Geotechnical Mapping With a Fixed-Wing Frequency-Domain Electromagnetic System

Hodges, Greg; Daly, Eve; Lafrenière, L. B.; O’Connell, Yvonne

doi:10.4133/sageep.29-004

Cited by 1 publication

(1 citation statement)

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“…Frequency-domain airborne electromagnetic (AEM) methods are widely used in near-surface geophysical exploration [1][2][3][4]. AEM data interpretation mainly relies on inversion.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Inversion of Frequency-Domain Airborne EM Data With Spatial Correlation Prior Information

Zhou,

Husmeier,

Gao

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Bayesian inversion of electromagnetic data can obtain key information on the uncertainty of subsurface resistivity. However, due to its high computational cost, Bayesian inversion is largely limited to 1D resistivity models. In this study, a fast Bayesian inversion method is implemented by introducing the spatial correlation as prior information. The contributions of this paper mainly include: (1) Explicitly introduce the expression of spatial correlation prior information, and provide a method to determine the parameters in the expression through the variogram theory. The influence of parameters in the spatial correlation prior information on the inversion results is systematically analyzed with 1D synthetic model. (2) The information entropy theory of continuous function is introduced to quantify the degrees of freedom of the parameters of the spatial correlation prior model. The analysis shows that the degree of freedom of model parameters is significantly smaller than the number of model parameters when spatial correlation prior information is introduced, which is the main reason for the rapid Bayesian inversion. (3) Introducing the sengpiel fast imaging algorithm, combined with the variogram theory, realized the direct acquisition of spatial correlation prior information from the observation data, minimizing the dependence on other information. The inversion results of 1D and 2D synthetic models and field dataset show that considering the spatial correlation prior information, hundreds of thousands of MCMC searches are needed to realize the inversion of up to thousands of model parameters. This result provides a possible idea for future Bayesian inversion of complex 3D models. Index Terms-airborne electromagnetic (AEM), statistical methods, Bayesian inversion, Markov chain Monte Carlo.

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“…Frequency-domain airborne electromagnetic (AEM) methods are widely used in near-surface geophysical exploration [1][2][3][4]. AEM data interpretation mainly relies on inversion.…”

Section: Introductionmentioning

confidence: 99%