Windowed 4D inversion for near real-time geoelectrical monitoring applications

Wilkinson, P.B.; Chambers, J.E.; Meldrum, P.I.; Kuras, O.; Inauen, C.M.; Swift, R.T.; Curioni, G.; Uhlemann, S.; Graham, J.; Atherton, N.. 2022 Windowed 4D inversion for near real-time geoelectrical monitoring applications. Frontiers in Earth Science, 10, 983603. https://doi.org/10.3389/feart.2022.983603

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Official URL: http://dx.doi.org/10.3389/feart.2022.983603

Abstract/Summary

Many different approaches have been developed to regularise the time-lapse geoelectrical inverse problem. While their advantages and limitations have been demonstrated using synthetic models, there have been few direct comparisons of their performance using field data. We test four time-lapse inversion methods (independent inversion, temporal smoothness-constrained 4D inversion, spatial smoothness constrained inversion of temporal data differences, and sequential inversion with spatial smoothness constraints on the model and its temporal changes). We focus on the applicability of these methods to automated processing of geoelectrical monitoring data in near real-time. In particular, we examine windowed 4D inversion, the use of short sequences of time-lapse data, without which the 4D method would not be suitable in the near real-time context. We develop measures of internal consistency for the different methods so that the effects of the use of short time windows or the choice of baseline data set can be compared. The resulting inverse models are assessed against qualitative and quantitative ground truth information. Our findings are that 4D inversion of the full data set performed best, and that windowed 4D inversion retained the majority of its benefits while also being applicable to applications requiring near real-time inversion.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3389/feart.2022.983603
ISSN:	2296-6463
Date made live:	07 Sep 2022 13:28 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533157

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.3389/feart.2022.983603

ISSN:

2296-6463

Date made live:

07 Sep 2022 13:28 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/533157