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...

Full description

Bibliographic Details
Main Authors: Christin Bobe, Daan Hanssens, Thomas Hermans, Ellen Van De Vijver
Format: Article
Language:English
Published: MDPI AG 2020-06-01
Series:Algorithms
Subjects:
Online Access:https://www.mdpi.com/1999-4893/13/6/144
_version_ 1827714780547252224
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.
first_indexed 2024-03-10T19:05:17Z
format Article
id doaj.art-a3d4007ef5724905b2e1c2180cd5a833
institution Directory Open Access Journal
issn 1999-4893
language English
last_indexed 2024-03-10T19:05:17Z
publishDate 2020-06-01
publisher MDPI AG
record_format Article
series Algorithms
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
work_keys_str_mv AT christinbobe efficientprobabilisticjointinversionofdirectcurrentresistivityandsmallloopelectromagneticdata
AT daanhanssens efficientprobabilisticjointinversionofdirectcurrentresistivityandsmallloopelectromagneticdata
AT thomashermans efficientprobabilisticjointinversionofdirectcurrentresistivityandsmallloopelectromagneticdata
AT ellenvandevijver efficientprobabilisticjointinversionofdirectcurrentresistivityandsmallloopelectromagneticdata