Calibration and Filtering Strategies for Frequency Domain Electromagnetic Data

Minsley, Burke J.; Smith, Bruce D.; Hammack, Richard; Sams, James I.; Veloski, Garret

doi:10.4133/1.3445431

Cited by 20 publications

(30 citation statements)

References 7 publications

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“…Considerable post‐processing is required of EMI data, but the ease of data collection exceeded that for GPR as vegetation did not need to be cleared, and the data were improved (instead of hindered) by the wet conditions. EMI also has potential value for integration with large‐scale airborne electromagnetic data, capable of mapping permafrost distribution at landscape scales (Minsley et al ., ; Pastick et al ., ).…”

Section: Discussionmentioning

confidence: 97%

“…Prior to inversion, we filter and calibrate the data following the approach suggested by Minsley et al . (). With this approach, the EMI data are adjusted against a conductivity model derived from ERI surveys.…”

Section: Methodsmentioning

confidence: 97%

“…This new code is an extension of an EMI code previously described by Schultz and Ruppel () with additional features and capabilities, including one‐dimensional (1D) laterally constrained electromagnetic inversion, and regularisation and inversion in 2D or three dimensions (3D; user defined), although the forward models are in 1D, in order to minimise problems commonly associated with other unconstrained 1D approaches (e.g. Auken et al ., ; Minsley et al ., ). A parallel inversion capability greatly speeds the processing of large data‐sets such as that collected at Twelvemile Lake.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Surface Geophysical Methods for Characterising Frozen Ground in Transitional Permafrost Landscapes

Briggs

Campbell

Nolan

et al. 2016

Permafrost and Periglac. Process.

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The distribution of shallow frozen ground is paramount to research in cold regions, and is subject to temporal and spatial changes influenced by climate, landscape disturbance and ecosystem succession. Remote sensing from airborne and satellite platforms is increasing our understanding of landscape-scale permafrost distribution, but typically lacks the resolution to characterise finer-scale processes and phenomena, which are better captured by integrated surface geophysical methods. Here, we demonstrate the use of electrical resistivity imaging (ERI), electromagnetic induction (EMI), ground penetrating radar (GPR) and infrared imaging over multiple summer field seasons around the highly dynamic Twelvemile Lake, Yukon Flats, central Alaska, USA. Twelvemile Lake has generally receded in the past 30 yr, allowing permafrost aggradation in the receded margins, resulting in a mosaic of transient frozen ground adjacent to thick, older permafrost outside the original lakebed. ERI and EMI best evaluated the thickness of shallow, thin permafrost aggradation, which was not clear from frost probing or GPR surveys. GPR most precisely estimated the depth of the active layer, which forward electrical resistivity modelling indicated to be a difficult target for electrical methods, but could be more tractable in time-lapse mode. Infrared imaging of freshly dug soil pit walls captured active-layer thermal gradients at unprecedented resolution, which may be useful in calibrating emerging numerical models. GPR and EMI were able to cover landscape scales (several kilometres) efficiently, and new analysis software showcased here yields calibrated EMI data that reveal the complicated distribution of shallow permafrost in a transitional landscape.

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Section: Discussionmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Surface Geophysical Methods for Characterising Frozen Ground in Transitional Permafrost Landscapes

Briggs

Campbell

Nolan

et al. 2016

Permafrost and Periglac. Process.

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“…Various approaches have recently been developed to estimate EC from EMI data, including a smooth one‐dimensional inversion approach (e.g., Farquharson, 2000; Hendrickx et al, 2002; Huang and Won, 2003; Minsley et al, 2012b), a similar approach that uses lateral constraints along a two‐dimensional profile (e.g., Monteiro Santos, 2004; Triantafilis and Monteiro Santos, 2009; Monteiro Santos et al, 2011), and smooth two‐dimensional inversion approaches (e.g., Mitsuhata et al, 2006). Recent developments have also trended toward exploring multiple solutions of EC from EMI data instead of only a single best fitting model.…”

mentioning

confidence: 99%

Electrical Conductivity Imaging of Active Layer and Permafrost in an Arctic Ecosystem, through Advanced Inversion of Electromagnetic Induction Data

Dafflon

Hubbard

Ulrich

et al. 2013

Vadose Zone Journal

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Characterizing the spatial variability of active layer and permafrost properties is critical for parameterizing process‐rich models that simulate feedbacks from Arctic ecosystem to a changing climate. Because of the sensitivity of electrical conductivity (EC) measurements to moisture content, salinity, and freeze state and the ease of collecting electromagnetic induction (EMI) data with portable tools (e.g., EM38, GEM2, or DUALEM) over large regions, EMI surveys hold great potential for Arctic ecosystem characterization. However, estimation of subsurface EC distribution from such data is challenging because of the insufficient amount of information such data provide towards finding a unique solution. The non‐uniqueness problem is often approached by fixing inversion constraints and initial models without a clear understanding of their possible effects on the obtained results. Here we developed a direct search method, which involves a grid‐based evaluation of one‐dimensional layered model parameters, to estimate EC distribution from EMI data and evaluate the influence of prior constraints, data information content, and solution non‐uniqueness. We applied the new method to EMI data acquired in Barrow, AK, as part of the Department of Energy Next‐Generation Ecosystem Experiments (DOE NGEE–Arctic). Results demonstrate the success of the developed approach for estimating models that reproduce recorded data within a specified range of uncertainty at each measurement location, as well as the value of different types of constraints. Importantly, the method can be used to quickly investigate the need for and effects of different priors at numerous measurement locations, since the time‐consuming simulation of the EMI signals from the multidimensional search grid is performed only once.

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“…This hinders reliable EMI data inversion and only allows qualitative interpretations (Binley et al, ). To obtain reliable subsurface images, quantitative EMI data are needed (Mester et al, ; Minsley et al, ; Whalley et al, ). Measured ECa values can be calibrated, for example, using electrical resistivity tomography (Lavoué et al, ) or vertical electrical sounding data (Thiesson et al, ).…”

Section: Introductionmentioning

confidence: 99%

Understanding Soil and Plant Interaction by Combining Ground‐Based Quantitative Electromagnetic Induction and Airborne Hyperspectral Data

Hebel

Matveeva

Verweij

et al. 2018

Geophysical Research Letters

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For the first time, we combine depth‐specific soil information obtained from the quantitative inversion of ground‐based multicoil electromagnetic induction data with the airborne hyperspectral vegetation mapping of 1 × 1‐m pixels including Sun‐induced fluorescence (F) to understand how subsurface structures drive above‐surface plant performance. Hyperspectral data were processed to quantitative F and selected biophysical canopy maps, which are proxies for actual photosynthetic rates. These maps showed within‐field spatial patterns, which were attributed to paleo‐river channels buried at around 1‐m depth. The soil structures at specific depths were identified by quantitative electromagnetic induction data inversions and confirmed by soil samples. Whereas the upper plowing layer showed minor correlation to the plant data, the deeper subsoil carrying vital plant resources correlated substantially. Linking depth‐specific soil information with plant performance data may greatly improve our understanding and the modeling of soil‐vegetation‐atmosphere exchange processes.

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Calibration and Filtering Strategies for Frequency Domain Electromagnetic Data

Cited by 20 publications

References 7 publications

Surface Geophysical Methods for Characterising Frozen Ground in Transitional Permafrost Landscapes

Surface Geophysical Methods for Characterising Frozen Ground in Transitional Permafrost Landscapes

Electrical Conductivity Imaging of Active Layer and Permafrost in an Arctic Ecosystem, through Advanced Inversion of Electromagnetic Induction Data

Understanding Soil and Plant Interaction by Combining Ground‐Based Quantitative Electromagnetic Induction and Airborne Hyperspectral Data

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