Examples of Improved Inversion of Different Airborne Electromagnetic Datasets Via Sharp Regularization

Vignoli, Giulio; Sapia, Vincenzo; Menghini, A.; Viezzoli, Andrea

doi:10.2113/jeeg22.1.51

Cited by 36 publications

(28 citation statements)

References 42 publications

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“…An option would be to use these resistivity data in the MPS modelling as soft conditioning data, such that high electric resistivities indicate sand, while clay corresponds to low resistivity values (see, for instance, He et al, 2014He et al, , 2016. However, EM data have limited resolution capability towards thin layers (Ley-Cooper et al, 2014;Vignoli et al, 2017), especially in the deeper parts of the investigated sequences. The modelled unit is generally present at great depths (from a range between 30 and 80 m, in the east, to a range between 150 and 170 m, in the west), and the resolution of the EM data is consequently quite low in most of the area.…”

Section: Discussionmentioning

confidence: 99%

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Høyer

Vignoli

Hansen

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Most studies on the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2-D or quasi-3-D training images. In the present study, we demonstrate a novel strategy for 3-D MPS modelling characterized by (i) realistic 3-D training images and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km 2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3-D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed spatial trends. The training image is constructed as a relatively small 3-D voxel model covering an area of 90 km 2 . We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, this study shows how to include both the geological environment and the type and quality of input information in order to achieve optimal results from MPS modelling. We present a practical workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modelling.

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Section: Discussionmentioning

confidence: 99%

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Høyer

Vignoli

Hansen

et al. 2017

Hydrol. Earth Syst. Sci.

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“…Please refer to the supporting information for a detailed description of the applied procedure. Since the type of regularization chosen in the inversion plays an important role in the details of the output model, several types were tested: smooth and sharp, multilayered, and few layered (Vignoli et al, 2017) spatially constrained inversions (SCI, Viezzoli et al, 2008). forced us to remove from the dataset large portions of data.…”

Section: Airborne Electromagnetic Data Processingmentioning

confidence: 99%

Peatland Volume Mapping Over Resistive Substrates With Airborne Electromagnetic Technology

Silvestri

Christensen

Lysdahl

et al. 2019

Geophysical Research Letters

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Despite the importance of peatlands as carbon reservoirs, a reliable methodology for the detection of peat volumes at regional scale is still missing. In this study we explore for the first time the use of airborne electromagnetic (AEM) to detect and quantify peat thickness and extension of two bogs located in Norway, where peat lays over resistive bedrock. Our results show that when calibrated using a small amount of field measurements, AEM can successfully detect peat volume even in less ideal conditions, that is, relatively resistive peat over resistive substrata. We expect the performance of AEM to increase significantly in presence of a conductive substratum without need of calibration with field data. The organic carbon content retrieved from field surveys and laboratory analyses combined with the 3‐D model of the peat extracted from AEM allowed us to quantify the total organic carbon of the selected bogs, hence assessing the carbon pool.

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“…Besides the classical regularization matrices based on the discretization of the first and second derivatives, in all the cases characterized by sharp interfaces, we tested a nonlinear regularization stabilizer promoting the reconstruction of blocky features and thus to improve the spatial resolution of EMI inversion results. (Zhdanov et al, 2006;Ley-Cooper et al, 2015;Vignoli et al, 2015Vignoli et al, , 2017. The advantage of this relatively new regularization is that, when appropriate prior knowledge about the medium to reconstruct is available, it can mitigate the smearing and over-smoothing effects of the more standard inversion strategies.…”

Section: Multi-height Emi Readings Inversionmentioning

confidence: 99%

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

Dragonetti

Comegna

Ajeel

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. This paper deals with the issue of monitoring the spatial distribution of bulk electrical conductivity, σ b , in the soil root zone by using electromagnetic induction (EMI) sensors under different water and salinity conditions. To deduce the actual distribution of depth-specific σ b from EMI apparent electrical conductivity (EC a ) measurements, we inverted the data by using a regularized 1-D inversion procedure designed to manage nonlinear multiple EMI-depth responses. The inversion technique is based on the coupling of the damped Gauss-Newton method with truncated generalized singular value decomposition (TGSVD). The illposedness of the EMI data inversion is addressed by using a sharp stabilizer term in the objective function. This specific stabilizer promotes the reconstruction of blocky targets, thereby contributing to enhance the spatial resolution of the EMI results in the presence of sharp boundaries (otherwise smeared out after the application of more standard Occamlike regularization strategies searching for smooth solutions). Time-domain reflectometry (TDR) data are used as groundtruth data for calibration of the inversion results. An experimental field was divided into four transects 30 m long and 2.8 m wide, cultivated with green bean, and irrigated with water at two different salinity levels and using two different irrigation volumes. Clearly, this induces different salinity and water contents within the soil profiles. For each transect, 26 regularly spaced monitoring soundings (1 m apart) were selected for the collection of (i) Geonics EM-38 and (ii) Tektronix reflectometer data. Despite the original discrepancies in the EMI and TDR data, we found a significant correlation of the means and standard deviations of the two data series; in particular, after a low-pass spatial filtering of the TDR data. Based on these findings, this paper introduces a novel methodology to calibrate EMI-based electrical conductivities via TDR direct measurements. This calibration strategy consists of a linear mapping of the original inversion results into a new conductivity spatial distribution with the coefficients of the transformation uniquely based on the statistics of the two original measurement datasets (EMI and TDR conductivities).

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Examples of Improved Inversion of Different Airborne Electromagnetic Datasets Via Sharp Regularization

Cited by 36 publications

References 42 publications

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Multiple-point statistical simulation for hydrogeological models: 3-D training image development and conditioning strategies

Peatland Volume Mapping Over Resistive Substrates With Airborne Electromagnetic Technology

Calibrating electromagnetic induction conductivities with time-domain reflectometry measurements

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