Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic Data
Often, multiple geophysical measurements are sensitive to the same subsurface parameters. In this case, joint inversions are mostly preferred over two (or more) separate inversions of the geophysical data sets due to the expected reduction of the non-uniqueness in the joint inverse solution. This re...
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MDPI AG
2020-06-01
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Online Access: | https://www.mdpi.com/1999-4893/13/6/144 |
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author | Christin Bobe Daan Hanssens Thomas Hermans Ellen Van De Vijver |
author_facet | Christin Bobe Daan Hanssens Thomas Hermans Ellen Van De Vijver |
author_sort | Christin Bobe |
collection | DOAJ |
description | Often, multiple geophysical measurements are sensitive to the same subsurface parameters. In this case, joint inversions are mostly preferred over two (or more) separate inversions of the geophysical data sets due to the expected reduction of the non-uniqueness in the joint inverse solution. This reduction can be quantified using Bayesian inversions. However, standard Markov chain Monte Carlo (MCMC) approaches are computationally expensive for most geophysical inverse problems. We present the Kalman ensemble generator (KEG) method as an efficient alternative to the standard MCMC inversion approaches. As proof of concept, we provide two synthetic studies of joint inversion of frequency domain electromagnetic (FDEM) and direct current (DC) resistivity data for a parameter model with vertical variation in electrical conductivity. For both studies, joint results show a considerable improvement for the joint framework over the separate inversions. This improvement consists of (1) an uncertainty reduction in the posterior probability density function and (2) an ensemble mean that is closer to the synthetic true electrical conductivities. Finally, we apply the KEG joint inversion to FDEM and DC resistivity field data. Joint field data inversions improve in the same way seen for the synthetic studies. |
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language | English |
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spelling | doaj.art-a3d4007ef5724905b2e1c2180cd5a8332023-11-20T04:11:42ZengMDPI AGAlgorithms1999-48932020-06-0113614410.3390/a13060144Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic DataChristin Bobe0Daan Hanssens1Thomas Hermans2Ellen Van De Vijver3Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, BelgiumDepartment of Environment, Ghent University, Coupure Links 653, 9000 Gent, BelgiumDepartment of Geology, Ghent University, 9000 Gent, BelgiumDepartment of Environment, Ghent University, Coupure Links 653, 9000 Gent, BelgiumOften, multiple geophysical measurements are sensitive to the same subsurface parameters. In this case, joint inversions are mostly preferred over two (or more) separate inversions of the geophysical data sets due to the expected reduction of the non-uniqueness in the joint inverse solution. This reduction can be quantified using Bayesian inversions. However, standard Markov chain Monte Carlo (MCMC) approaches are computationally expensive for most geophysical inverse problems. We present the Kalman ensemble generator (KEG) method as an efficient alternative to the standard MCMC inversion approaches. As proof of concept, we provide two synthetic studies of joint inversion of frequency domain electromagnetic (FDEM) and direct current (DC) resistivity data for a parameter model with vertical variation in electrical conductivity. For both studies, joint results show a considerable improvement for the joint framework over the separate inversions. This improvement consists of (1) an uncertainty reduction in the posterior probability density function and (2) an ensemble mean that is closer to the synthetic true electrical conductivities. Finally, we apply the KEG joint inversion to FDEM and DC resistivity field data. Joint field data inversions improve in the same way seen for the synthetic studies.https://www.mdpi.com/1999-4893/13/6/144joint inversionKalman ensemble generatorgeophysicsresistivityelectromagneticsMonte Carlo |
spellingShingle | Christin Bobe Daan Hanssens Thomas Hermans Ellen Van De Vijver Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic Data Algorithms joint inversion Kalman ensemble generator geophysics resistivity electromagnetics Monte Carlo |
title | Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic Data |
title_full | Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic Data |
title_fullStr | Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic Data |
title_full_unstemmed | Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic Data |
title_short | Efficient Probabilistic Joint Inversion of Direct Current Resistivity and Small-Loop Electromagnetic Data |
title_sort | efficient probabilistic joint inversion of direct current resistivity and small loop electromagnetic data |
topic | joint inversion Kalman ensemble generator geophysics resistivity electromagnetics Monte Carlo |
url | https://www.mdpi.com/1999-4893/13/6/144 |
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