Ensemble Kalman inversion of induced polarization data

Tso, Chak-Hau Michael ORCID: https://orcid.org/0000-0002-2415-0826; Iglesias, Marco; Binley, Andrew. 2024 Ensemble Kalman inversion of induced polarization data. Geophysical Journal International, 236 (3). 1877-1900. https://doi.org/10.1093/gji/ggae012

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Abstract/Summary

This paper explores the applicability of Ensemble Kalman Inversion (EKI) with level-set parameterization for solving geophysical inverse problems. In particular, we focus on its extension to induced polarization (IP) data with uncertainty quantification. IP data may provide rich information on characteristics of geological materials due to its sensitivity to characteristics of the pore-grain interface. In many IP studies, different geological units are juxtaposed and the goal is to delineate these units and obtain estimates of unit properties with uncertainty bounds. Conventional inversion of IP data does not resolve well sharp interfaces and tends to reduce and smooth resistivity variations, while not readily providing uncertainty estimates. Recently, it has been shown for DC resistivity that EKI is an efficient solver for inverse problems which provides uncertainty quantification, and its combination with level set parameterization can delineate arbitrary interfaces well. In this contribution, we demonstrate the extension of EKI to IP data using a sequential approach, where the mean field obtained from DC resistivity inversion is used as input for a separate phase angle inversion. We illustrate our workflow using a series of synthetic and field examples. Variations with uncertainty bounds in both DC resistivity and phase angles are recovered by EKI, which provides useful information for hydrogeological site characterization. While phase angles are less well-resolved than DC resistivity, partly due to their smaller range and higher percentage data errors, it complements DC resistivity for site characterization. Overall, EKI with level set parameterization provides a practical approach forward for efficient hydrogeophysical imaging under uncertainty.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1093/gji/ggae012
UKCEH and CEH Sections/Science Areas:	Pollution (Science Area 2017-)
ISSN:	0956-540X
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	Ensemble Kalman methods, induced polarization, inversion–data assimilation, uncertainty quantification, level sets
NORA Subject Terms:	Data and Information
Related URLs:	Dataset
Date made live:	12 Jan 2024 14:06 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536650

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