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dc.contributor.authorMatsuno, Tetsuo  Concept link
dc.contributor.authorChave, Alan D.  Concept link
dc.contributor.authorJones, Alan G.  Concept link
dc.contributor.authorMuller, Mark R.  Concept link
dc.contributor.authorEvans, Rob L.  Concept link
dc.date.accessioned2014-04-02T16:06:23Z
dc.date.available2014-04-02T16:06:23Z
dc.date.issued2014-01-02
dc.identifier.citationGeophysical Journal International 196 (2014): 1365-1374en_US
dc.identifier.urihttps://hdl.handle.net/1912/6534
dc.descriptionAuthor Posting. © The Author(s), 2014. This article is posted here by permission of The Royal Astronomical Society for personal use, not for redistribution. The definitive version was published in Geophysical Journal International 196 (2014): 1365-1374, doi:10.1093/gji/ggt484.en_US
dc.description.abstractA robust magnetotelluric (MT) inversion algorithm has been developed on the basis of quantile-quantile (q-q) plotting with confidence band and statistical modelling of inversion residuals for the MT response function (apparent resistivity and phase). Once outliers in the inversion residuals are detected in the q-q plot with the confidence band and the statistical modelling with the Akaike information criterion, they are excluded from the inversion data set and a subsequent inversion is implemented with the culled data set. The exclusion of outliers and the subsequent inversion is repeated until the q-q plot is substantially linear within the confidence band, outliers predicted by the statistical modelling are unchanged from the prior inversion, and the misfit statistic is unchanged at a target level. The robust inversion algorithm was applied to synthetic data generated from a simple 2-D model and observational data from a 2-D transect in southern Africa. Outliers in the synthetic data, which come from extreme values added to the synthetic responses, produced spurious features in inversion models, but were detected by the robust algorithm and excluded to retrieve the true model. An application of the robust inversion algorithm to the field data demonstrates that the method is useful for data clean-up of outliers, which could include model as well as data inconsistency (for example, inability to fit a 2-D model to a 3-D data set), during inversion and for objectively obtaining a robust and optimal model. The present statistical method is available irrespective of the dimensionality of target structures (hence 2-D and 3-D structures) and of isotropy or anisotropy, and can operate as an external process to any inversion algorithm without modifications to the inversion program.en_US
dc.description.sponsorshipTM was supported by the scientific program of TAIGA (trans-crustal advection and in-situ reaction of global sub-seafloor aquifer) sponsored by the MEXT of Japan, and is supported by the NIPR project KP-7. ADC is supported by US National Science Foundation (NSF) grant EAR1015185.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.publisherOxford University Press on behalf of The Royal Astronomical Societyen_US
dc.relation.urihttps://doi.org/10.1093/gji/ggt484
dc.subjectInverse theoryen_US
dc.subjectProbability distributionsen_US
dc.subjectMagnetotelluricsen_US
dc.titleRobust magnetotelluric inversionen_US
dc.typeArticleen_US
dc.identifier.doi10.1093/gji/ggt484


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