4-D inversion of DC resistivity monitoring data acquired over a dynamically changing earth model

Kim, Jung Ho; Yi, Myeong‐Jong; Park, Samgyu; Kim, Jae Gon

doi:10.1016/j.jappgeo.2009.03.002

Cited by 164 publications

(147 citation statements)

References 11 publications

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“…The different inversion scheme takes advantage of the repetition of measurements over time to eliminate the systematic error component of the data (LaBrecque and Yang, 2001;Kemna et al, 2002). The 4D inversion scheme (Kim et al, 2009;Karaoulis et al, 2011) inverts multiple data sets simultaneously including the time dimension in the inversion process. However, deterministic time-lapse inversion schemes still remain dependent on the spatial or spatiotemporal regularization procedure required to overcome the ill-posedness of the ERT inverse problem.…”

Section: Introductionmentioning

confidence: 99%

Covariance-constrained difference inversion of time-lapse electrical resistivity tomography data

2016

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Hydrogeophysics has become a major field of research in the past two decades, and time-lapse electrical resistivity tomography (ERT) is one of the most popular techniques to monitor passive and active processes in shallow subsurface reservoirs. Time-lapse inversion schemes have been developed to refine inversion results, but they mostly still rely on a spatial regularization procedure based on the standard smoothness constraint. We have applied a covariance-based regularization operator to the time-lapse ERT inverse problem. We first evaluated the method for surface and crosshole ERT with two synthetic cases and compared the results with the smoothness-constrained inversion (SCI). These tests showed that the covariance-constrained inversion (CCI) better images the target in terms of shape and amplitude. Although more important in low-sensitivity zones, we have observed improvements everywhere in the tomograms. Those synthetic examples also show that an error made in the range or in the type of the variogram model had a limited impact on the resulting image, which still remained better than SCI. We then applied the method to cross-borehole ERT field data from a heat-tracing experiment, in which the comparison with direct measurements showed a strong improvement of the breakthrough curves retrieved from ERT. This method could be extended to the time dimension, which would allow the use of CCI in 4D inversion schemes.

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Section: Introductionmentioning

confidence: 99%

Covariance-constrained difference inversion of time-lapse electrical resistivity tomography data

2016

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“…To simulate a 4D inversion that accommodates both time and space domains, we take the approach demonstrated by Kim et al [2009]. The time domain is incorporated directly into the regularization procedure by modifying Equation I above:…”

Section: Numerical Modelingmentioning

confidence: 99%

Environmental monitoring of leaks using time-lapsed long electrode electrical resistivity

Rucker

Fink

Loke³

2011

Journal of Applied Geophysics

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“…R d and R m are weighting matrices introduced so that different elements of the data misfit and model roughness vectors are given equal weights if the L1-norm inversion method is used (Loke et al, 2003). The inversion methodology by Kim et al (2009) directly incorporates the time domain with the space domain using the following equation.…”

Section: Theorymentioning

confidence: 99%

“…In some areas, repeated 3-D surveys are carried out to detect temporal changes of the subsurface (Rosqvist et al, 2010). In this paper, we adapt the time-lapse inversion methodology by Kim et al (2009) for 3-D surveys.…”

Section: Introductionmentioning

confidence: 99%

Constrained Time-lapse Inversion of 3-D Resistivity Surveys Data

Loke¹,

Dahlin²,

Leroux³

2011

Proceedings

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SUMMARYThree-dimensional surveys and inversion models are required to accurately resolve structures in areas with very complex geology where 2-D models might suffer from artefacts. 3-D data sets collected at different times are inverted simultaneously using a least-squares methodology that uses roughness filters in both the space and time domains. The spatial roughness filter constrains the model resistivity to vary smoothly in the x, y and z directions. A temporal roughness filter is also applied that minimizes changes in the resistivity between successive temporal inversion models. This method can accurately resolve temporal changes in the resistivity even in the presence of noise. The use of the L1 and L2 norm constraints for the temporal roughness filter are examined using a synthetic model. The L1 norm temporal constraint produces significantly more accurate results when the resistivity changes abruptly with time. A test with field data from a landfill site with methane gas accumulation shows near surface resistivity changes that are probably due to surface temperature variations. The temperature variations cause changes in the gas volume and moisture content in the near surface landfill materials.

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4-D inversion of DC resistivity monitoring data acquired over a dynamically changing earth model

Cited by 164 publications

References 11 publications

Covariance-constrained difference inversion of time-lapse electrical resistivity tomography data

Covariance-constrained difference inversion of time-lapse electrical resistivity tomography data

Environmental monitoring of leaks using time-lapsed long electrode electrical resistivity

Constrained Time-lapse Inversion of 3-D Resistivity Surveys Data

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