Drone‐towed electromagnetic and magnetic systems for subsurface characterization and archaeological prospecting

Abstract Drone‐based geophysical surveying is an emerging measuring platform. High time‐cost efficiency and flexibility to survey over inaccessible areas make drones attractive or sometimes the only feasible option to carry geophysical measurements. This study presents a new drone‐towed electromagnetic induction and magnetic gradient sensor system used for near‐surface characterization and areal mapping. The system uses various datasets to enhance processing and interpretation. The system includes; an electromagnetic induction instrument; magnetic sensors; GNSS‐IMU system; photogrammetry; Lidar model data; and geoid model data. Robust data processing and stochastic inversion subsurface characterization for archaeological prospecting with drone‐towed electromagnetic induction and magnetic gradient sensor systems. Robust statistical methods were used to process the data. We conducted the fieldwork at one of the ancient Viking settlements in Denmark. The surveyed area was approximately 100200 m. We then implemented and applied a one‐dimensional laterally constrained non‐linear stochastic inversion to image the subsurface electrical conductivity. The inversion results show a consistent conductive layer at 5–8 m depths, likely associated with the groundwater level. This conductive layer is disrupted under a prominent anomaly within a 2–4 m wide area. Our analysis showed that this conductivity disruption could be a flint mine extending 7 m deep. This anomaly also has a strong signature in magnetic gradient data.

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Institutions

Publication Details

Journal
Near Surface Geophysics
Published
2024-10-02
DOI
https://doi.org/10.1002/nsg.12320
Citations
4
Primary Topic
Geophysical Methods and Applications
Type
article
Field-Weighted Citation Impact
1.34

Funders

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article

Drone‐towed electromagnetic and magnetic systems for subsurface characterization and archaeological prospecting

Alexander V. Grayver, Tobias Bjerg Vilhelmsen, Arne Døssing
4 citations
Near Surface Geophysics
Geophysical Methods and Applications
1.34
article

Drone‐towed electromagnetic and magnetic systems for subsurface characterization and archaeological prospecting

Alexander V. Grayver, Tobias Bjerg Vilhelmsen, Arne Døssing
article en
4 citations

Abstract

Abstract Drone‐based geophysical surveying is an emerging measuring platform. High time‐cost efficiency and flexibility to survey over inaccessible areas make drones attractive or sometimes the only feasible option to carry geophysical measurements. This study presents a new drone‐towed electromagnetic induction and magnetic gradient sensor system used for near‐surface characterization and areal mapping. The system uses various datasets to enhance processing and interpretation. The system includes; an electromagnetic induction instrument; magnetic sensors; GNSS‐IMU system; photogrammetry; Lidar model data; and geoid model data. Robust data processing and stochastic inversion subsurface characterization for archaeological prospecting with drone‐towed electromagnetic induction and magnetic gradient sensor systems. Robust statistical methods were used to process the data. We conducted the fieldwork at one of the ancient Viking settlements in Denmark. The surveyed area was approximately 100200 m. We then implemented and applied a one‐dimensional laterally constrained non‐linear stochastic inversion to image the subsurface electrical conductivity. The inversion results show a consistent conductive layer at 5–8 m depths, likely associated with the groundwater level. This conductive layer is disrupted under a prominent anomaly within a 2–4 m wide area. Our analysis showed that this conductivity disruption could be a flint mine extending 7 m deep. This anomaly also has a strong signature in magnetic gradient data.

Near Surface GeophysicsVol. 22(6)
University of Cologne (DE), Technical University of Denmark (DK)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 20%
Geophysical Methods and Applications
1.34
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