2009
DOI: 10.1111/j.1365-2478.2008.00774.x
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The use of reference models from a priori data to guide 2D inversion of electrical resistivity tomography data

Abstract: We report a modelling study to investigate the effects of constraining the inversion of Electrical Resistivity Tomography (ERT) data, from surface arrays, with reference models derived from supplementary resistivity data such as borehole resistivity logs, resistivity cone penetrometry (RCPT), and electromagnetic survey. A synthetic resistivity site model of a highly resistive (200 Ωm) sand and gravel lens in a low resistivity (30 Ωm) clay till was constructed to test the approach. Synthetic Wenner ERT field da… Show more

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Cited by 10 publications
(7 citation statements)
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“…F is the forward model operator, and L represents the model smoothing matrix. The reference model mref contains a priori information on the investigated medium and constrains the adjusted model (e.g., Catt et al ). The λ damping factor allows for adjusting the trade‐off between minimising the data misfit norm and the model smoothing norm (i.e., the regularisation term).…”
Section: The 3d‐ Proceduresmentioning
confidence: 99%
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“…F is the forward model operator, and L represents the model smoothing matrix. The reference model mref contains a priori information on the investigated medium and constrains the adjusted model (e.g., Catt et al ). The λ damping factor allows for adjusting the trade‐off between minimising the data misfit norm and the model smoothing norm (i.e., the regularisation term).…”
Section: The 3d‐ Proceduresmentioning
confidence: 99%
“…The addition of regularisation terms in inverse problem formulations is a powerful means of introducing prior information that has been proved to be essential for retrieving reliable solutions (Ellis and Oldenburg ). This has led to a variety of complementary techniques such as adding a smoothness constraint on the model to avoid unnecessary structure (e.g., Constable, Parker, and Constable ; deGroot‐Hedlin and Constable ), constraining the inverted model to a reference model (e.g., Oldenburg and Li ; Pidlisecky, Haber, and Knight ; Catt, West, and Clark ; Caterina et al ), using variable weighting factors depending on the reliability of prior information (Kim et al ), adding structural information such as known boundaries in the model (e.g., Kaipio et al ; Caterina et al ), decoupling the regularisation effects to preserve sharp boundaries where they are known to exist (Coscia et al ), or imposing bounds to the model resistivity values in selected regions by means of inequality constraints (Kim, Song, and Lee ; Cardarelli and Fischanger ). In the presented work, we implemented various modes of integrating a priori information, either by means of regularisation (as in all the previous references) or, alternatively, within the model and parameter mesh generation phase before the inversion process (Günther and Rücker ; Cardarelli and Fischanger ).…”
Section: Introductionmentioning
confidence: 99%
“…In the second method, the resistivity of blocks in the initial model are defined, following the methodology described in Catt et al (), where the size and resistivity of the blocks can be revised by the subsequent inversion. Here, the blocks were defined by estimating a layered model from the results of the standard processing and inserted into the resistivity data as shown in Table .…”
Section: Guided Resistivity Inversionmentioning
confidence: 99%
“…A third method, investigated in this paper, is to produce a synthetic model of one property based on the known values of another. (e.g., Catt et al )…”
Section: Introductionmentioning
confidence: 99%
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