Calibration of near‐surface multi‐frequency electromagnetic induction data

Finco, Cécile; Réjiba, Fayçal; Schamper, Cyril; Fraga, L.H. Cavalcante; Wang, A.

doi:10.1111/1365-2478.13344

Cited by 2 publications

(1 citation statement)

References 38 publications

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“…On the other hand, ERT data do not require any complex procedure for calibration since a test on well-characterized (internal) resistances is sufficient to guarantee measurement consistency and is typically performed automatically using commercial equipment. Unfortunately, this is not true in the case of EMI as, to have absolute calibrated measurements, EMI instruments need to go through a sequence of adjustments [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Spreading of Localized Information across an Entire 3D Electrical Resistivity Volume via Constrained EMI Inversion Based on a Realistic Prior Distribution

Zaru,

Rossi,

Vacca

et al. 2023

Remote Sensing

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Frequency-domain electromagnetic induction (EMI) methods are commonly used to map vast areas quickly and with minimum logistical efforts. Unfortunately, they are often characterized by a very limited number of frequencies and severe ill-posedness. On the other hand, electrical resistivity tomography (ERT) approaches are usually considered more reliable; for example, they do not require specific calibration procedures and can be easily inverted in 2D/3D. However, ERT surveys are, by far, more demanding and time consuming, allowing for the deployment of a few acquisition lines per day. Ideally, the optimal would be to have the advantages of both approaches: ease of acquisition while keeping robustness and reliability. The present work raises from the necessity to cope with this issue and from the importance of enforcing realistic constraints to the data inversion without being limited to (over)simplistic spatial constraints (for example, characterizing the smooth and/or sharp regularization). Accordingly, the present research demonstrates, by means of synthetic and field data, how the EMI inversion—based on realistic prior models—can be further enhanced by incorporating additional pre-existing pieces of information. While the proposed scheme is quite general, in the specific examples discussed here, these additional pieces of information are, respectively, a reference model along a line across the survey area, and an ERT section. The field EMI results were verified against extensive ground penetrating radar (GPR) measurements and boreholes.

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