Improved Geoarchaeological Mapping with Electromagnetic Induction Instruments from Dedicated Processing and Inversion
"> Figure 1
<p>(<b>a</b>) Location of survey area and collected borehole and geophysical data. The map spans (552,960 m–553,420 m) in UTMX and (6,211,130 m–6,211,677 m) in UTMY (wgs84, zone 32°N). All following maps are exactly the same (<b>b</b>) Schematics of the DualEM-421s system employed in the survey (photo from another site).</p> "> Figure 2
<p>(<b>a</b>) The combined red and blue dots show the full datasets prior to processing, i.e., where it was physically possible to access the study site with the DualEM-421S system. The red dots show the datasets that were removed during the processing mainly due to coupling with man-made structures. The light blue dots between A and A’ indicates the track shown in (<b>b</b>). The map spans (552,960 m–553,420 m) in UTMX and (6,211,130 m–6,211,677 m) in UTMY (wgs84, zone 32°N). (<b>b</b>) Example of a coupling in the dataset and the effect of the averaging filters. The raw data show actual measured apparent resistivities whereas the averaged data have been shifted upwards by a factor of 10. The data affected by coupling to human-made structures are marked with grey.</p> "> Figure 3
<p>Map showing the spatially-constrained inversion concept with inline and cross-line constraints at Alken Enge. Sounding positions are marked with red dots and the interconnecting constraints are indicated with the black lines. The map spans (552,960 m–553,420 m) in UTMX and (6,211,130 m–6,211,677 m) in UTMY (wgs84, zone 32°N).</p> "> Figure 4
<p>Panel showing an inverted resistivity section with data residuals as a tool for quality assessment of the inversion results. In the top panels the data are shown with red error bars and the forward response from the inverted model with the solid black line. Below the inverted model is shown with the data misfit in red. The arrows indicate the positions of the data plots on the full section.</p> "> Figure 5
<p>Survey depth of the DualEM-421s for two-layer models. The circles indicate <span class="html-italic">the depth of exploration (</span>DOE) stated by DualEM for the 4 m HCP configuration and assuming a low induction numbers (LIN) approximation. Crosses are DOI estimates obtained with a full solution of all six channels of a DualEM-421S instrument. Values are tabulated in <a href="#remotesensing-08-01022-t001" class="html-table">Table 1</a>.</p> "> Figure 6
<p>(<b>a</b>) Geophysical section with borehole information at Alken Enge. The model is from a full non-linear inversion of the entire data set. The location of the profile can be seen in <a href="#remotesensing-08-01022-f001" class="html-fig">Figure 1</a>a; (<b>b</b>) Mean-resistivity map in a depth of 0–1 m; (<b>c</b>) Mean-resistivity map in a depth of 1–2 m. The map spans (552,960 m–553,420 m) in UTMX and (6,211,130 m–6,211,677 m) in UTMY (wgs84, zone 32°N).</p> "> Figure 7
<p>Comparison between the channel values of the HCP2-channel which is reported to have a focus depth of 1.5 m, and the full solution for the Alken Enge field case. (<b>a</b>) Raw channel data values for the 2 m horizontal co-planar receiver; (<b>b</b>) Mean-resistivity map in a depth of 1.5 m based on the full solution. The map spans (552,960 m–553,420 m) in UTMX and (6,211,130 m–6,211,677 m) in UTMY (wgs84, zone 32°N).</p> "> Figure 8
<p>Depth of investigation, DOI, for the individual resistivity models at Alken Enge. The map spans (552,960 m–553,420 m) in UTMX and (6,211,130 m–6,211,677 m) in UTMY (wgs84, zone 32°N).</p> "> Figure 9
<p>The effect of not doing any processing on raw data and in the resulting models. The display is identical to that of <a href="#remotesensing-08-01022-f007" class="html-fig">Figure 7</a>, but here without processing. (<b>a</b>) Raw channel data values for the 2 m horizontal co-planar receiver; (<b>b</b>) Mean-resistivity map in a depth of 1.5 m based on the full solution. Boxes 1–4 indicate areas with examples of coupling effects that are dealt with in the manual processing. The map spans (552,960 m–553,420 m) in UTMX and (6,211,130 m–6,211,677 m) in UTMY (wgs84, zone 32°N).</p> ">
Abstract
:1. Introduction
- The EM fields from an EMI instrument are diffusive and will average over a certain volume depending on the instrument design. Hence, a given data point never reflects a certain place in space. The deeper the target depth, the larger the averaging volume.
- Calculating the full forward response is computationally efficient, making CPU time negligible.
- Dedicated tools for continuous datasets, such as Aarhus Workbench [34], are ready off-the-shelf.
- The full solution provides a more robust interpretation of the archaeological and geological features than derived via approximations or raw data analysis.
2. Materials and Methods
2.1. Study Site
2.2. Instrumental Setup and Field Work
2.3. Data Processing and Modelling
- Negative data are removed.
- Manual inspection of the raw data series with the primary target to identify couplings to human-made structures as buried cables, pipelines, metal fences, etc. Coupling effects are often easily identified by inspecting the GIS map with wire-installations shown together with the raw data. Figure 2a shows the full dataset with red dots whereas the superimposed blue dots show the resulting dataset after manual removal of couplings and noisy data. Figure 2b shows an example of a coupling in the dataset, which arises from a powered pump station. The effect of the manual processing for the entire survey is shown in the results section.
- The data are then averaged to improve the S/N-ratio. This is done by a running mean with a specified filter length, followed by a polynomial fit. The running mean filter length is typically in the order of 2–10 m, depending on the signal to noise ratio and the geological variability at hand. As the filter is a function of distance and not time, the number of measurements included in the filter will vary with the acquisition speed. In this process, great care is needed to make sure that geological structures are not smeared out by the averaging. Figure 2b shows a stretch of survey line with raw data as well as averaged data. Here, the averaging filter was set at 5 m.
- The averaged data are assigned noise according to (1) the absolute signal level with respect to absolute noise thresholds for the individual channels, and (2) the variance of the data entering the median filter. Here, the absolute noise level was set uniformly on all six channels to 0.6 mS/m based on on-site repetitive measurements. A sound estimate of noise-levels on the data are crucial for making a meaningful inversion later.
- Soundings for inversion are taken out at user-specified intervals, typically every 1–5 m. The trade-off is between computation speed and redundant information. There is no risk in choosing too low a sounding distance, but the soundings will then contain a lot of redundant information and the computation time for the subsequent inversion will go up. Here, a sounding distance of 1 m was chosen as some of the paleo-structures were thought to be quite small. The total number of raw measurements were 90,949. After processing and with one sounding every 1 m the total number of datasets (each with six data points) ready for inversion ends at 13,043.
2.4. Comparing Approximate Modelling and Full Non-Linear Solution
3. Results and Discussion
3.1. Geophysical Results and Borehole Comparison
3.2. Comparing Data Values with Full Solution on the Field Case
3.3. Evaluating the Benefit of Processing the Data before Inversion
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Gaffney, C. Detecting trends in the prediction of the buried past: A review of geophysical techniques in archaeology. Archaeometry 2008, 50, 313–336. [Google Scholar] [CrossRef]
- Saey, T.; Van Meirvenne, M.; De Smedt, P.; Stichelbaut, B.; Delefortrie, S.; Baldwin, E.; Gaffney, V. Combining EMI and GPR for non-invasive soil sensing at the Stonehenge World Heritage Site: The reconstruction of a WW1 practice trench. Eur. J. Soil Sci. 2015, 66, 166–178. [Google Scholar] [CrossRef]
- Linford, N.; Linford, P.; Martin, L.; Payne, A. Recent results from the English Heritage caesium magnetometer system in comparison with recent fluxgate gradiometers. Archaeol. Prospect. 2007, 14, 151–166. [Google Scholar] [CrossRef]
- Bonsall, J.; Fry, R.; Gaffney, C.; Armit, I.; Beck, A.; Gaffney, V. Assessment of the CMD mini-explorer, a new low-frequency multi-coil electromagnetic device, for archaeological investigations. Archaeol. Prospect. 2013, 20, 219–231. [Google Scholar] [CrossRef]
- Bates, M.R.; Bates, C.R.; Whittaker, J.E. Mixed method approaches to the investigation and mapping of buried Quaternary deposits: Examples from southern England. Archaeol. Prospect. 2007, 14, 104–129. [Google Scholar] [CrossRef]
- De Smedt, P.; Saey, T.; Lehouck, A.; Stichelbaut, B.; Meerschman, E.; Islam, M.M.; De Vijver, E.V.; Van Meirvenne, M. Exploring the potential of multi-receiver EMI survey for geoarchaeological prospection: A 90ha dataset. Geoderma 2013, 199, 30–36. [Google Scholar] [CrossRef]
- De Smedt, P.; Van Meirvenne, M.; Saey, T.; Baldwin, E.; Gaffney, C.; Gaffney, V. Unveiling the prehistoric landscape at Stonehenge through multi-receiver EMI. J. Archaeol. Sci. 2014, 50, 16–23. [Google Scholar] [CrossRef]
- Jiang, P.P.; He, Z.Q.; Kitchen, N.R.; Sudduth, K.A. Bayesian analysis of within-field variability of corn yield using a spatial hierarchical model. Precis. Agric. 2009, 10, 111–127. [Google Scholar] [CrossRef]
- Lopez-Lozano, R.; Casterad, M.A.; Herrero, J. Site-specific management units in a commercial maize plot delineated using very high resolution remote sensing and soil properties mapping. Comput. Electron. Agric. 2010, 73, 219–229. [Google Scholar] [CrossRef] [Green Version]
- Eigenberg, R.A.; Woodbury, B.L.; Nienaber, J.A.; Spiehs, M.J.; Parker, D.B.; Varel, V.H. Soil conductivity and multiple linear regression for precision monitoring of beef feedlot manure and runoff. J. Environ. Eng. Geophys. 2010, 15, 175–184. [Google Scholar] [CrossRef]
- Serrano, J.M.; Shahidian, S.; da Silva, J.R.M. Apparent electrical conductivity in dry versus wet soil conditions in a shallow soil. Precis. Agric. 2013, 14, 99–114. [Google Scholar] [CrossRef]
- Everett, M.E. Theoretical developments in electromagnetic induction geophysics with selected applications in the near surface. Surv. Geophys. 2012, 33, 29–63. [Google Scholar] [CrossRef]
- Doolittle, J.A.; Brevik, E.C. The use of electromagnetic induction techniques in soils studies. Geoderma 2014, 223, 33–45. [Google Scholar] [CrossRef]
- Calamita, G.; Perrone, A.; Brocca, L.; Onorati, B.; Manfreda, S. Field test of a multi-frequency electromagnetic induction sensor for soil moisture monitoring in southern Italy test sites. J Hydrol. 2015, 529, 316–329. [Google Scholar] [CrossRef]
- McNeill, J.D. Why Doesn’t Geonics Limited Build a Multi-Frequency EM31 or EM38? Technical note TN-30; Geonics Limited: Mississauaga, ON, Canada, 1996. [Google Scholar]
- McNeill, J. Electromagnetic Terrain Conductivity Measurement at Low Induction Numbers; Technical Report TN-6; Geonics Limited: Mississauga, ON, Canada, 1980. [Google Scholar]
- Callegary, J.B.; Ferre, T.P.A.; Groom, R.W. Vertical spatial sensitivity and exploration depth of low-induction-number electromagnetic-induction instruments. Vadose Zone J. 2007, 6, 158–167. [Google Scholar] [CrossRef]
- Callegary, J.B.; Ferré, T.P.A.; Groom, R.W. Three-dimensional sensitivity distribution and sample volume of low-induction-number electromagnetic-induction instruments. Soil Sci. Soc. Am. J. 2012, 76, 85–91. [Google Scholar] [CrossRef]
- Wait, J.R. A note on the electromagnetic response of a stratified earth. Geophysics 1962, 27, 382–385. [Google Scholar] [CrossRef]
- Santos, F.A.M. 1-D laterally constrained inversion of EM34 profiling data. J. Appl. Geophys. 2004, 56, 123–134. [Google Scholar] [CrossRef]
- De Smedt, P.; Van Meirvenne, M.; Herremans, D.; De Reu, J.; Saey, T.; Meerschman, E.; Crombé, P.; De Clercq, W. The 3-D reconstruction of medieval wetland reclamation through electromagnetic induction survey. Sci. Rep. 2013, 3. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Saey, T.; De Smedt, P.; Meerschman, E.; Islam, M.M.; Meeuws, F.; Van De Vijver, E.; Lehouck, A.; Van Meirvenne, M. Electrical conductivity depth modelling with a multireceiver EMI sensor for prospecting archaeological features. Archaeol. Prospect. 2012, 19, 21–30. [Google Scholar] [CrossRef]
- Zare, E.; Huang, J.; Santos, F.A.M.; Triantafilis, J. Mapping salinity in three dimensions using a DualEM-421 and electromagnetic inversion software. Soil Sci. Soc. Am. J. 2015, 79, 1729–1740. [Google Scholar] [CrossRef]
- Mester, A.; Van Der Kruk, J.; Zimmermann, E.; Vereecken, H. Quantitative two-layer conductivity inversion of multi-configuration electromagnetic induction measurements. Vadose Zone J. 2011, 10, 1319–1330. [Google Scholar] [CrossRef]
- Dafflon, B.; Hubbard, S.S.; Ulrich, C.; Peterson, J.E. Electrical conductivity imaging of active layer and permafrost in an Arctic ecosystem, through advanced inversion of electromagnetic induction data. Vadose Zone J. 2013, 12. [Google Scholar] [CrossRef]
- Jadoon, K.Z.; Moghadas, D.; Jadoon, A.; Missimer, T.M.; Al-Mashharawi, S.K.; McCabe, M.F. Estimation of soil salinity in a drip irrigation system by using joint inversion of multicoil electromagnetic induction measurements. Water Resour. Res. 2015, 51, 3490–3504. [Google Scholar] [CrossRef]
- Lee, B.D.; Jenkinson, B.J.; Doolittle, J.A.; Taylor, R.S.; Tuttle, J.W. Electrical conductivity of a failed septic system soil absorption field. Vadose Zone J. 2006, 5, 757–763. [Google Scholar] [CrossRef]
- Saey, T.; De Smedt, P.; De Clercq, W.; Meerschman, E.; Monirul Islam, M.; Van Meirvenne, M. Identifying soil patterns at different spatial scales with a multi-receiver emi sensor. Soil Sci. Soc. Am. J. 2013, 77, 382–390. [Google Scholar] [CrossRef]
- Benech, C.; Dabas, M.; Simon, F.-X.; Tabbagh, A.; Thiesson, J. Interpretation of shallow electromagnetic instruments resistivity and magnetic susceptibility measurements using rapid 1D/3D inversion. Geophysics 2016, 81, E103–E112. [Google Scholar] [CrossRef] [Green Version]
- Dabas, M.; Anest, A.; Thiesson, J.; Tabbagh, A. Slingram EMI devices for characterizing resistive features using apparent conductivity measurements: Check of the DualEM-421S instrument and field tests. Archaeol. Prospect. 2016, 23, 165–180. [Google Scholar] [CrossRef]
- Triantafilis, J.; Wong, V.; Santos, F.A.M.; Page, D.; Wege, R. Modeling the electrical conductivity of hydrogeological strata using joint-inversion of loop-loop electromagnetic data. Geophysics 2012, 77, WB99–WB107. [Google Scholar] [CrossRef]
- Siemon, B.; Christiansen, A.V.; Auken, E. A review of helicopter-borne electromagnetic methods for groundwater exploration. Near Surf. Geophys. 2009, 7, 629–646. [Google Scholar] [CrossRef]
- Sengpiel, K.P.; Siemon, B. Advanced inversion methods for airborne electromagnetic exploration. Geophysics 2000, 65, 1983–1992. [Google Scholar] [CrossRef]
- Auken, E.; Viezzoli, A.; Christiansen, A.V. A single software for processing, inversion, and presentation of AEM data of different systems: The Aarhus Workbench. In Proceedings of the International Geophysical Conference and Exhibition, Adelaide, SA, Australia, 22–25 February 2009; pp. 1–5.
- Auken, E.; Christiansen, A.V.; Westergaard, J.A.; Kirkegaard, C.; Foged, N.; Viezzoli, A. An integrated processing scheme for high-resolution airborne electromagnetic surveys, the SkyTEM system. Explor. Geophys. 2009, 40, 184–192. [Google Scholar] [CrossRef]
- Pedersen, J.B.; Auken, E.; Vest, C.A.; Kristiansen, S.M. Mapping soil heterogeneity using spatially constrained inversion of electromagnetic induction data. In Proceedings of the First Conference on Proximal Sensing Supporting Precision Agriculture, Turin, Italy, 6 September 2015.
- Søe, N.E.; Odgaard, B.; Hertz, E.; Holst, M.K.; Kristiansen, S.M. Geological setting of a sacred landscape: Iron age post battle depositions at Alken Enge, Denmark. Geoarchaeology 2016. submitted. [Google Scholar]
- Lavoué, F.; Van Der Kruk, J.; Rings, J.; André, F.; Moghadas, D.; Huisman, J.A.; Lambot, S.; Weihermüller, L.; Vanderborght, J.; Vereecken, H. Electromagnetic induction calibration using apparent electrical conductivity modelling based on electrical resistivity tomography. Near Surf. Geophys. 2010, 8, 553–561. [Google Scholar] [CrossRef]
- Auken, E.; Christiansen, A.V.; Fiandaca, G.; Schamper, C.; Behroozmand, A.A.; Binley, A.; Nielsen, E.; Effersø, F.; Christensen, N.B.; Sørensen, K.I.; et al. An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data. Explor. Geophys. 2015, 46, 223–235. [Google Scholar] [CrossRef]
- Podgorski, J.E.; Green, A.G.; Kalscheuer, T.; Kinzelbach, W.; Horstmeyer, H.; Maurer, H.; Rabenstein, L.; Doetsch, J.; Auken, E.; Ngwisanyi, T.; et al. Integrated interpretation of helicopter and ground-based geophysical data recorded within the Okavango Delta, Botswana. J. Appl. Geophys. 2015, 114, 52–67. [Google Scholar] [CrossRef]
- Mikucki, J.A.; Auken, E.; Tulaczyk, S.; Virginia, R.A.; Schamper, C.; Sorensen, K.I.; Doran, P.T.; Dugan, H.; Foley, N. Deep groundwater and potential subsurface habitats beneath an Antarctic dry valley. Nat. Commun. 2015, 6. [Google Scholar] [CrossRef] [PubMed]
- Auken, E.; Violette, S.; d’Ozouville, N.; Deffontaines, B.; Sørensen, K.I.; Viezzoli, A.; de Marsily, G. An integrated study of the hydrogeology of volcanic islands using helicopter borne transient electromagnetic: Application in the Galápagos Archipelago. Comptes Rendus Geosci. 2009, 341, 899–907. [Google Scholar] [CrossRef]
- Viezzoli, A.; Christiansen, A.V.; Auken, E.; Sørensen, K.I. Quasi-3d modeling of airborne tem data by spatially constrained inversion. Geophysics 2008, 73, F105–F113. [Google Scholar] [CrossRef]
- Christiansen, A.V.; Auken, E. A global measure for depth of investigation. Geophysics 2012, 77, WB171–WB177. [Google Scholar] [CrossRef]
- Taylor, R. Apparent Conductivity as an Indicator of Thickness. Available online: http://www.dualem.com/acit.htm (accessed on 5 September 2016).
- Søe, N.E.; Odgaard, B.; Nielsen, A.B.; Olsen, J.; Kristiansen, S.M. The making of a sacred landscape: Late Holocene palaeoecology of Ilsø and the Illerup/Alken Enge valley, Denmark. Veg. Hist. Archaeobothany 2016. submitted. [Google Scholar]
- Tjelldén, A.K.E.; Matthiesen, H.; Petersen, L.M.M.; Søe, N.E.; Kristiansen, S.M. In-situ preservation solutions for deposited Iron age human bones in Alken Enge, Denmark. Conserv. Manag. Archaeol. Sites 2016, 18, 126–138. [Google Scholar] [CrossRef]
Thickness, Upper Layer | 1 m | 2 m | 3 m | 4 m | 5 m | 6 m | 7 m |
---|---|---|---|---|---|---|---|
10/100 ohm-m Model | |||||||
DOI, full, all (m) | 1.2 | 1.8 | 2.3 | 2.7 | 2.9 | 3.1 | 3.2 |
DOE, 4 m HCP (m) | 5.8 | 5.8 | 5.8 | 5.8 | 5.8 | 5.8 | 5.8 |
Abs error (m) | 4.6 | 4.0 | 3.5 | 3.1 | 2.9 | 2.7 | 2.6 |
(4 m HCP, LIN) (ohm-m) | 44.7 | 25.6 | 19.3 | 16.4 | 14.9 | 13.9 | 13.3 |
(4 m HCP, true) (ohm-m) | 47.2 | 26.7 | 20.1 | 17.2 | 15.7 | 14.8 | 14.3 |
100/10 ohm-m Model | |||||||
DOI, full, all (m) | 4.3 | 5.3 | 6.1 | 6.8 | 7.4 | 7.9 | 8.3 |
DOE, 4 m HCP (m) | 5.8 | 5.8 | 5.8 | 5.8 | 5.8 | 5.8 | 5.8 |
Abs error (m) | 1.5 | 0.5 | −0.3 | −1.0 | −1.6 | −2.1 | −2.5 |
(4 m HCP, LIN) (ohm-m) | 11.4 | 14.1 | 17.2 | 20.3 | 23.4 | 26.3 | 29.0 |
(4 m HCP, true) (ohm-m) | 15.6 | 20.4 | 26.6 | 33.5 | 40.8 | 48.0 | 55.1 |
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Christiansen, A.V.; Pedersen, J.B.; Auken, E.; Søe, N.E.; Holst, M.K.; Kristiansen, S.M. Improved Geoarchaeological Mapping with Electromagnetic Induction Instruments from Dedicated Processing and Inversion. Remote Sens. 2016, 8, 1022. https://doi.org/10.3390/rs8121022
Christiansen AV, Pedersen JB, Auken E, Søe NE, Holst MK, Kristiansen SM. Improved Geoarchaeological Mapping with Electromagnetic Induction Instruments from Dedicated Processing and Inversion. Remote Sensing. 2016; 8(12):1022. https://doi.org/10.3390/rs8121022
Chicago/Turabian StyleChristiansen, Anders Vest, Jesper Bjergsted Pedersen, Esben Auken, Niels Emil Søe, Mads Kähler Holst, and Søren Munch Kristiansen. 2016. "Improved Geoarchaeological Mapping with Electromagnetic Induction Instruments from Dedicated Processing and Inversion" Remote Sensing 8, no. 12: 1022. https://doi.org/10.3390/rs8121022