Combined Frequency Domain and Time Domain Airborne Data for Environmental and Engineering Challenges

Karshakov, E. V.; Podmogov, Yury G.; Kertsman, Vladimir M.; Moilanen, J.

doi:10.2113/jeeg22.1.1

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…In this respect, FDEM presents several advantages as it does not require any direct coupling with the surface. In fact, several FDEM systems are specifically designed to be operated while mounted on helicopters (Karshakov et al., 2017; Yin & Hodges, 2007) and, probably, soon, on drones (Mitsuhata et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Petrophysical Reconstruction of Danta's Alpine Peatland via Electromagnetic Induction Data

Zaru,

Silvestri,

Assiri

et al. 2024

Earth and Space Science

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Peatlands are fundamental deposits of organic carbon. Thus, their protection is of crucial importance to avoid emissions from their degradation. Peat is a mixture of organic soil that originates from the accumulation of wetland plants under continuous or cyclical anaerobic conditions for long periods. Hence, a precise quantification of peat deposits is extremely important; for that, remote‐ and proximal‐sensing techniques are excellent candidates. Unfortunately, remote‐sensing can provide information only on the few shallowest centimeters, whereas peatlands often extend to several meters in depth. In addition, peatlands are usually characterized by difficult (flooded) terrains. So, frequency‐domain electromagnetic instruments, as they are compact and contactless, seem to be the ideal solution for the quantitative assessment of the extension and geometry of peatlands. Generally, electromagnetic methods are used to infer the electrical resistivity of the subsurface. In turn, the resistivity distribution can, in principle, be interpreted to infer the morphology of the peatland. Here, to some extent, we show how to shortcut the process and include the expectation and uncertainty regarding the peat resistivity directly into a probabilistic inversion workflow. The present approach allows for retrieving what really matters: the spatial distribution of the probability of peat occurrence, rather than the mere electrical resistivity. To evaluate the efficiency and effectiveness of the proposed probabilistic approach, we compare the outcomes against the more traditional deterministic fully nonlinear (Occam's) inversion and against some boreholes available in the investigated area.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Petrophysical Reconstruction of Danta's Alpine Peatland via Electromagnetic Induction Data

Zaru,

Silvestri,

Assiri

et al. 2024

Earth and Space Science

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show abstract

“…In this respect, FDEM presents several advantages as it does not require any direct coupling with the surface. In fact, several FDEM systems are specifically designed to be operated while mounted on helicopters (Yin & Hodges, 2007;Karshakov et al, 2017) and, probably, soon, on drones (Mitsuhata et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic petrophysical reconstruction of Danta's Alpine peatland via electromagnetic induction data

Vignoli,

Zaru,

Silvestri

et al. 2024

Preprint

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Peatlands are fundamental deposits of organic carbon. Thus, their protection is of crucial importance to avoid emissions from their degradation. Peat is a mixture of organic soil that originates from the accumulation of wetland plants under continuous or cyclical anaerobic conditions for long periods. Hence, a precise quantification of peat deposits is extremely important; for that, remote-and proximal-sensing techniques are excellent candidates. Unfortunately, remote-sensing can provide information only on the few shallowest centimeters, whereas peatlands often extend to several meters in depth. In addition, peatlands are usually characterized by difficult (flooded) terrains. So, frequency-domain electromagnetic instruments, as they are compact and contactless, seem to be the ideal solution for the quantitative assessment of the extension and geometry of peatlands. Generally, electromagnetic methods are used to infer the electrical resistivity of the subsurface. In turn, the resistivity distribution can, in principle, be interpreted to infer the morphology of the peatland. Here, to some extent, we show how to shortcut the process and include the expectation and uncertainty regarding the peat resistivity directly into a probabilistic inversion workflow. The present approach allows for retrieving what really matters: the spatial distribution of the probability of peat occurrence, rather than the mere electrical resistivity. To evaluate the efficiency and effectiveness of the proposed probabilistic approach, we compare the outcomes against the more traditional deterministic fully nonlinear (Occam's) inversion and against some boreholes available in the investigated area.

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“…On the other hand, standard EMI equipment is routinely used to assess (quasi-)3D resistivity distribution of extremely vast areas [14][15][16]. In fact, many EMI systems are specifically designed to be operated while mounted on helicopters [17,18].…”

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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Combined Frequency Domain and Time Domain Airborne Data for Environmental and Engineering Challenges

Cited by 9 publications

References 8 publications

Probabilistic Petrophysical Reconstruction of Danta's Alpine Peatland via Electromagnetic Induction Data

Probabilistic Petrophysical Reconstruction of Danta's Alpine Peatland via Electromagnetic Induction Data

Probabilistic petrophysical reconstruction of Danta's Alpine peatland via electromagnetic induction data

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

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