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Open AccessArticle

The Influence of Geostatistical Prior Modeling on the Solution of DCT-Based Bayesian Inversion: A Case Study from Chicken Creek Catchment

1
Research Center Landscape Development and Mining Landscapes, Brandenburg University of Technology, D-03046 Cottbus, Germany
2
Department of Civil and Environmental Engineering, University of California Irvine, 4130 Engineering Gateway, Irvine, CA 92697-2175, USA
3
Department of Earth System Science, University of California Irvine, 3200 Croul Hall, Irvine, CA 92697-2175, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1549; https://doi.org/10.3390/rs11131549
Received: 1 May 2019 / Revised: 1 June 2019 / Accepted: 24 June 2019 / Published: 29 June 2019
(This article belongs to the Special Issue Recent Advances in Subsurface Sensing Technologies)
Low frequency loop-loop electromagnetic induction (EMI) is a widely-used geophysical measurement method to rapidly measure in situ the apparent electrical conductivity (ECa) of variably-saturated soils. Here, we couple Bayesian inversion of a quasi-two-dimensional electromagnetic (EM) model with image compression via the discrete cosine transform (DCT) for subsurface electrical conductivity (EC) imaging. The subsurface EC distributions are obtained from multi-configuration EMI data measured with a CMD-Explorer sensor along two transects in the Chicken Creek catchment (Brandenburg, Germany). Dipole-dipole electrical resistivity tomography (ERT) data are used to benchmark the inferred EC fields of both transects. We are especially concerned with the impact of the DCT truncation method on the accuracy and reliability of the inversely-estimated EC images. We contrast the results of two different truncation approaches for model parametrization. The first scenario considers an arbitrary selection of the dominant DCT coefficients and their prior distributions (a commonly-used approach), while the second methodology benefits from geostatistical simulation of the EMI data pseudosection. This study demonstrates that DCT truncation based on geostatistical simulations facilitates a robust selection of the dominant DCT coefficients and their prior ranges, resulting in more accurate subsurface EC imaging from multi-configuration EMI data. Results based on geostatistical prior modeling present an excellent agreement between the EMI- and ERT-derived EC fields of the Chicken Creek catchment. View Full-Text
Keywords: near-surface geophysics; electromagnetic induction; Bayesian inversion; soil conductivity imaging; discrete cosine transform; multiple-point statistics near-surface geophysics; electromagnetic induction; Bayesian inversion; soil conductivity imaging; discrete cosine transform; multiple-point statistics
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MDPI and ACS Style

Moghadas, D.; Vrugt, J.A. The Influence of Geostatistical Prior Modeling on the Solution of DCT-Based Bayesian Inversion: A Case Study from Chicken Creek Catchment. Remote Sens. 2019, 11, 1549.

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