Seven new peer-reviewed articles from MapField (1st half of 2021)

Published 28-09-2021

MapField generates extensive knowledge ready to be used in more targeted nitrogen regulation of Danish agriculture. Seven new peer-reviewed articles were published since the beginning of 2021. Here we give an overview, for more details, please follow the links.

Assessment of complex subsurface redox structures for sustainable development of agriculture and the environment

By Hansen et al., published in Environmental Research Letters on 29 January 2021

Targeted nitrogen (N) regulation of agriculture requires detailed knowledge of the subsurface redox structures to determine the locations of nitrate reduction in the subsurface. This analysis is highly needed in many parts of Denmark due to complex redox structures in the glacial landscapes.

Complete list of authors: Hansen, B., Voutchkova, D.D., Sandersen, P.B.E., Kallesøe, A., Thorling, L., Møller, I., Madsen, R.B., Jakobsen, R., Aamand, J., Maurya, P., Kim, H.,

Link to the paper here.

Machine learning based fast forward modelling of ground-based time-domain electromagnetic data

By Bording et al., published in Journal of Applied Geophysics on 23 February 2021

We propose a machine learning based forward modelling approach as a computationally feasible alternative to approximate numerical forward modelling. The accuracy of this alternative approach is within typical data uncertainties and can help in speeding up TEM inversions that requires calculation of computationally expensive forward responses.

Complete list of authors: Bording, T.S., Asif, M.R., Barfod, A.S., Larsen, J.J, Zhang, B., Grombacher, D.J., Christiansen, A.V., Engebretsen, K.W., Pedersen, J.B., Maurya P.K., Auken, E.

Link to the paper here.

Effect of Data Pre-Processing on the Performance of Neural Networks for 1-D Transient Electromagnetic Forward Modeling

By Asif et al., published in IEEE Access on 24 February 2021.

In this paper, we show how the data pre-processing affects the performance of neural networks for forward modelling of TEM data. We provide insights into how various pre-processing methods affect the performance of these networks and recommend optimal pre-processing strategies to achieve superior performance.

Complete list of authors: Asif M.R., Bording, T.S., Barfod, A.S., Grombacher, D., Maurya, P.K., Christiansen, A.V., Auken, E., Larsen, J.J.

Link to the paper (open access) here.

A 3D hydrogeochemistry model of nitrate transport and fate in a glacial sediment catchment: A first step toward a numerical model

By Kim et al., published in Science of the Total Environment on 25 February 2021.

How can we upscale point scale observations of the hydrogeochemical dynamics to catchment scale? In this study, we attempted to project our understanding of nitrate transport and fate based on field observations to the entire catchment in 3D by integrating multi-disciplinary information.

Complete list of authors: Kim, H., Sandersen, P.B.E., Jakobsen, R., Kallesøe, A.J., Claes, N., Blicher-Mathiesen, G., Foged, N., Aamand, J., Hansen, B.

Link to the paper here.

Utilizing the towed Transient ElectroMagnetic method (tTEM) for achieving unprecedented near-surface detail in geological mapping

By Sandersen et al., published in Engineering Geology on 3 April 2021 (online).

The topic of the paper is the tTEM method and its capability of providing highly detailed geological information about the uppermost 50-70 m of the subsurface. The paper presents examples and discuss the method and its applicability in four study areas where data from tTEM surveys has been combined with geological data and knowledge to map near-surface geological features that could not be resolved in 3D using other geophysical methods focusing on the deeper subsurface or methods with a wider data spacing.

Complete list of authors: Sandersen, P.B.E., Kallesøe A.J., Møller, I., Høyer, A.S., Jørgensen, F., Pedersen, J.B., Christiansen, A.V.

Link to the paper (open access) here.

A Neural Network-Based Hybrid Framework for Least-Squares Inversion of Transient Electromagnetic Data

By Asif et al., published in IEEE Transactions on Geoscience and Remote Sensing on 11 May 2021

The calculation of partial derivatives for the Jacobian matrix is by far the most computationally intensive task during a TEM inversion. We accelerate the inversion process by predicting partial derivatives using an artificial neural network. We present four inversion schemes by switching between the numerically computed forwards and derivative responses with neural network-based computations and show that the proposed schemes provide a tunable balance between computational time and inversion accuracy.

Complete list of authors: Asif, M.R., Bording, T.S., Maurya, P.K., Zhang, B., Fiandaca, G., Grombacher, D., Christiansen, A.V., Auken, E., Larsen, J.J.

Link to the paper here

3D multiple-point geostatistical simulation of joint subsurface redox and geological architectures

By Madsen et al., published in Hydrology and Earth System Sciences (HESS) on 25 May 2021

The protection of subsurface aquifers from contamination is an ongoing environmental challenge. Some areas of the underground have a natural capacity for reducing contaminants. In this research these areas are mapped in 3D along with information about, e.g., sand and clay, which indicates whether contaminated water from the surface will travel through these areas. This mapping technique will be fundamental for more reliable risk assessment in water quality protection.

Complete list of authors: Madsen, R.B., Kim, H., Kallesøe, A.J., Sandersen, P.B.E., Vilhelmsen, T.N., Hansen, T.M., Christiansen, A.V., Møller, I., and Hansen, B.

Link to the paper (open access) here
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