Resolving spectral information from time domain induced polarization data through 2-D inversion

Fiandaca, Gianluca; Ramm, James; Binley, Andrew; Gazoty, A.; Christiansen, Anders Vest; Auken, Esben

doi:10.1093/gji/ggs060

Cited by 104 publications

(82 citation statements)

References 38 publications

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“…In particular, AarhusInv supports airborne TEM data and FEM data (Siemon et al, 2009) and several ground based methods, i.e. EM (Christiansen et al, 2007), DC (Auken et al, 2005;Auken and Christiansen, 2004), Induced Polarization (Fiandaca et al, 2012;Fiandaca et al, 2013), Nuclear Magnetic Resonance (Behroozmand et al, 2012b;Behroozmand et al, 2012a), Surface waves (Wisén and Christiansen, 2005) and magnetotellurics (Christiansen and Auken, 2009). …”

Section: Discussionmentioning

confidence: 90%

Quasi-3D inversion of full size AEM datasets

Auken

Fiandaca

Kirkegaard

et al. 2015

ASEG Extended Abstracts

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Section: Discussionmentioning

confidence: 90%

Quasi-3D inversion of full size AEM datasets

Auken

Fiandaca

Kirkegaard

et al. 2015

ASEG Extended Abstracts

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“…The data were fist processed to reduce noise, then inverted with AarhusInv inversion code (previously known as em1dinv), modified as per Fiandaca et al (2012). It is able to solve for Complex Impedance, using the model of Cole-Cole (1941), providing combined estimation of ρ, c, m and τ Care must be exerted with data preparation, regularization and choice of starting models.…”

Section: Methods and Resultsmentioning

confidence: 99%

Improving modelling of AEM data affected by IP, two case studies

Viezzoli¹,

Kaminski²,

Cooper

et al. 2015

ASEG Extended Abstracts

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“…However, as previously noted, this site is characterized by sediments that show very small differences in frequency-related properties between the units, suggesting that SIP studies will show the same limitations as IP methods at ∅-dominated sites with low heterogeneity in hydraulic characteristics of interest. We also note recent efforts to analyze time series of time-domain IP data in an attempt to remove superposition of charging cycles (Fiandaca et al, 2013). Such approaches, although currently limited to 2D analysis and specific polarization models, may offer greater ability to resolve more subtle contrasts in complex conductivity, especially in which there is evidence for additional (i.e., not K-C) petrophysics that is not dominated by ∅ (Morin, 2006;Barrash and Cardiff, 2013).…”

Section: Frequency-related Ip Propertiesmentioning

confidence: 99%

“…However, subtle variations in the polarization relaxation time distributions between units could not be correlated with unit physical properties. Therefore, we restrict our analysis to inversion of the complex conductivity only and do not consider more elaborate inversion methods recently developed to analyze the spectral characteristics of the polarization response timedomain data (Fiandaca et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

The hydrogeologic information in cross-borehole complex conductivity data from an unconsolidated conglomeratic sedimentary aquifer

et al. 2016

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Accurate estimation of the hydrological properties of nearsurface aquifers is important because these properties strongly influence groundwater flow and solute transport. Laboratorybased investigations have indicated that induced polarization (IP) properties of porous media may be linked, through either semiempirical or fully mechanistic models, to hydrological properties including hydraulic conductivity. Therefore, there is a need for field assessments of the value of IP measurements in providing insights into the hydrological properties of aquifers. A cross-borehole IP survey was carried out at the Boise Hydrogeophysical Research Site (BHRS), an unconsolidated fluvial aquifer that has previously been well-studied with a variety of geophysical and hydrogeologic techniques. High-quality IP measurements were inverted, with careful consideration of the data error structure, to provide a 3D distribution of complex electrical conductivity values. The inverted distribution was further simplified using k-means cluster analysis to divide the inverted volume into discrete zones with horizontal layering. Identified layers based on complex electrical conductivity inversions are in broad agreement with stratigraphic units identified in previous studies at the site. Although mostly subtle variations in the phase angle are recovered through inversion of field data, greater contrasts in the IP data are evident at some unit boundaries. However, in coarse-grained aquifers, such as the BHRS, the discrimination of mildly contrasting lithologic units and associated changes in hydraulic conductivity of one or two orders of magnitude are unlikely to be achieved through field IP surveys. Despite the difficulty of differentiating subtle differences between all units, overall estimates of hydraulic conductivity purely from our field IP data are typically within an order of magnitude of independently measured values.

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Resolving spectral information from time domain induced polarization data through 2-D inversion

Cited by 104 publications

References 38 publications

Quasi-3D inversion of full size AEM datasets

Quasi-3D inversion of full size AEM datasets

Improving modelling of AEM data affected by IP, two case studies

The hydrogeologic information in cross-borehole complex conductivity data from an unconsolidated conglomeratic sedimentary aquifer

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