Search Results - "Barfod, Adrian S."

Effect of Data Pre-Processing on the Performance of Neural Networks for 1-D Transient Electromagnetic Forward Modeling by Asif, Muhammad Rizwan, Bording, Thue S., Barfod, Adrian S., Grombacher, Denys J., Maurya, Pradip K., Christiansen, Anders V., Auken, Esben, Larsen, Jakob J.

“…Geophysical modelling and data inversion are important tools for interpreting the physical properties of Earth's subsurface. Solving the inverse problem…”
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Automatic processing of time domain induced polarization data using supervised artificial neural networks by Barfod, Adrian S, Lévy, Léa, Larsen, Jakob Juul

“…SUMMARY Processing of geophysical data is a time consuming task involving many different steps. One approach for accelerating and automating processing of…”
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Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods by Barfod, Adrian A. S, Møller, Ingelise, Christiansen, Anders V, Høyer, Anne-Sophie, Hoffimann, Júlio, Straubhaar, Julien, Caers, Jef

“…Creating increasingly realistic groundwater models involves the inclusion of additional geological and geophysical data in the hydrostratigraphic modeling…”
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Contributions to uncertainty related to hydrostratigraphic modeling using multiple-point statistics by Barfod, Adrian A. S, Vilhelmsen, Troels N, Jorgensen, Flemming, Christiansen, Anders V, Hoyer, Anne-Sophie, Straubhaar, Julien, Muller, Ingelise

“…Forecasting the flow of groundwater requires a hydrostratigraphic model, which describes the architecture of the subsurface. State-of-the-art multiple-point…”
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Successful Sampling Strategy Advances Laboratory Studies of NMR Logging in Unconsolidated Aquifers by Behroozmand, Ahmad A., Knight, Rosemary, Müller‐Petke, Mike, Auken, Esben, Barfod, Adrian A. S., Ferré, Ty P. A., Vilhelmsen, Troels N., Johnson, Carole D., Christiansen, Anders V.

“…The nuclear magnetic resonance (NMR) technique has become popular in groundwater studies because it responds directly to the presence and mobility of water in…”
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Machine learning based fast forward modelling of ground-based time-domain electromagnetic data by Bording, Thue Sylvester, Asif, Muhammad Rizwan, Barfod, Adrian S., Larsen, Jakob Juul, Zhang, Bo, Grombacher, Denys James, Christiansen, Anders Vest, Engebretsen, Kim Wann, Pedersen, Jesper Bjergsted, Maurya, Pradip Kumar, Auken, Esben

“…Inversion of large-scale time-domain electromagnetic surveys are computationally expensive and time consuming. Deterministic or probabilistic inversion schemes…”
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Compiling a national resistivity atlas of Denmark based on airborne and ground-based transient electromagnetic data by Barfod, Adrian A.S., Møller, Ingelise, Christiansen, Anders V.

“…We present a large-scale study of the petrophysical relationship of resistivities obtained from densely sampled ground-based and airborne transient…”
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