Probabilistic Inversion of Multiconfiguration Electromagnetic Induction Data Using Dimensionality Reduction Technique: A Numerical Study

• Published in: Vadose zone journal Vol. 18; no. 1; pp. 1 - 16
• Main Author: Moghadas, Davood
• Format: Journal Article
• Language: English
• Published: Madison The Soil Science Society of America, Inc 2019; John Wiley & Sons, Inc; Wiley
• Subjects: Bayesian analysis; Bayesian theory; Compression; Conductivity; Data; Discrete cosine transform; Distribution; Electrical conductivity; Electrical resistivity; Electromagnetic induction; Geology; Geophysical methods; geophysics; geostatistics; Inverse problems; Inversion; Mathematical models; Parameter estimation; Parameter uncertainty; Parameterization; Parameters; Probability theory; soil; Statistical inference; Training; vadose zone
• ISSN: 1539-1663; 1539-1663
• DOI: 10.2136/vzj2018.09.0183

Core Ideas DCT‐based inversion of EMI data successfully recovers subsurface conductivity images. Training image‐based parameterization ensures accurate subsurface characterization. Using ECa data pseudosection as a training image provides an adequate prior. Low‐frequency loop–loop electromagnetic induction (EMI) offers several key advantages over many other geophysical techniques for proximal soil sensing. Yet, because of problems with the inversion of measured apparent electrical conductivity (ECa) data, application of EMI for geophysical imaging and interpretation is limited. In this study, a Bayesian inference was used to obtain electromagnetic conductivity images (EMCIs) from multiconfiguration ECa data. This approach allows analysis of highly nonlinear problems and renders an ensemble of models obtained from the posterior distribution that can be used to explore parameter uncertainty. In this respect, generalized formal likelihood function was used to more accurately describe the sensitivity of the posterior distribution to residual assumptions. Discrete cosine transform (DCT) was employed as a model compression technique to reduce the number of unknown parameters in the inversion. The DCT parameterization was performed using training image (TI)‐based geostatistical simulations considering the ECa data pseudosection as a TI. The potential of the proposed approach was examined through different theoretical scenarios. The estimated subsurface EMCI shows excellent agreement with the original synthetic models subject to the appropriate choice of prior information. Moreover, DCT parameterization reduces the number of unknown parameters, increasing accuracy of the inversion with the Bayesian procedure. The proposed approach ensures accurate and high‐resolution characterization of subsurface conductivity layering from measured ECa values.