Hostname: page-component-76fb5796d-zzh7m Total loading time: 0 Render date: 2024-04-25T08:23:18.276Z Has data issue: false hasContentIssue false
  • English
  • Français

Spatial relationship between clay content and geophysical data

Published online by Cambridge University Press:  09 July 2018

D. de Benedetto ,
A. Castrignanò ,
D. Sollitto  and
F. Modugno
D. de Benedetto*
Affiliation:
CRA-SCA, Research Unit for Cropping Systems in Dry Environments, Bari, 70125, Italy
A. Castrignanò
Affiliation:
CRA-SCA, Research Unit for Cropping Systems in Dry Environments, Bari, 70125, Italy
D. Sollitto
Affiliation:
CRA-SCA, Research Unit for Cropping Systems in Dry Environments, Bari, 70125, Italy
F. Modugno
Affiliation:
CRA-SCA, Research Unit for Cropping Systems in Dry Environments, Bari, 70125, Italy
*
*E-mail: daniela.debenedetto@entecra.it
Get access Rights & Permissions [Opens in a new window]

Abstract

An important attribute for soil use is clay content, because it affects the water-holding capacity and hydraulic properties of a soil. Soil surveys are time-consuming, labour-intensive and costly, while geophysical methods, such as Electromagnetic Induction (EMI) and Ground Penetrating Radar (GPR), offer a non-invasive and non-destructive approach to mapping soil features. The objective of this paper is to assess the spatial relationship between clay content and geophysical data. The EMI and GPR data and soil cores were collected and analysed in a field of about 3 ha size in Rutigliano, Bari, in SE Italy. The EMI data and clay contents were interpolated using ordinary cokriging. The GPR data were pre-processed and the envelope of the filtered GPR data was used to produce 3Dmaps of the kriged estimates. The correlation with clay content was large and positive for EMI, whereas it was negative for the GPR measurement. In this work, a combination of geostatistical and statistical analysis has shown a significant correlation between the EMI and GPR observations. Estimation of the mathematical function relating these two groups of variables requires a multivariate approach of non-stationary geostatistics.

Keywords

electromagnetic inductionground penetrating radarclay contentgeostatistics
Type
Research Article
Information
Clay Minerals , Volume 45 , Issue 2 , June 2010 , pp. 197 - 207
Copyright
Copyright © The Mineralogical Society of Great Britain and Ireland 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Annan, A.P. (1993) Practical processing of GPR data. Proceedings of the Second Government Workshop on Ground Penetrating Radar, Columbus, Ohio, USA.Google Scholar
Castrignano, A., Giugliarini, L., Risaliti, R. & Martinelli, N. (2000) Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics. Geoderma, 97, 39–60.Google Scholar
Chilès, J.P. & Delfmer, P. (1999) Geostatistics: Modeling Spatial Uncertainty, Wiley & Sons, New York, USA.CrossRefGoogle Scholar
Claerbout, J.F. (1985) Fundamentals of Geophysical Data Processing: with Applications to Petroleum Prospecting. Blackwell Scientific Publications, Palo Alto, California, USA.Google Scholar
Davis, J.L. & Annan, A.P. (1989) Ground penetrating radar for high resolution mapping of soil and rock stratigraphy. Geophysical Prospecting, 37, 531–551.Google Scholar
De Benedetto, D., Castrignanò, A., Sollitto, D., Campi, P. & Modugno, F. (2008) Non-intrusive mapping of subsoil properties in agricultural fields with GPR and EMI. Proceedings First Global Workshop on High Resolution Digital Soil Sensing and Mapping, Sydney, Australia.Google Scholar
Doolittle, J.A. & Collins, M.E. (1998) A comparison of EM induction and GPR methods in areas of karst. Geoderma, 85, 83–102.CrossRefGoogle Scholar
Doolittle, J.A., Sudduth, K.A., Kitchen, N.R. & Indorante, S.J. (1994) Estimating depth to claypans using electromagnetic induction methods. Journal of Soil and Water Conservation, 49, 572–579.Google Scholar
Dougherty, M.E., Micheals, P., Pelton, J.R. & Liberty, L.M. (1994) Enhancement of ground penetrating radar data through signal processing. Pp. 1021–1028 in: Proceedings of the Symposium on the Application of Geophysics to Engineering and Environmental Problems (SAGEEP ’94), Boston, USA.Google Scholar
Fisher, C.S., Stewart, R.R. & Jol, H.M. (1994) Processing ground penetrating radar. Pp. 661–675 in: Proceedings of 5th International Conference on Ground Penetrating Radar (GPR ‘94), Canada.Google Scholar
Géovariances (2008) Isatis Technical Ref, ver. 8.05 Geovariances & Ecole Des Mines De Paris: Avon Cedex, France.Google Scholar
Gerlitz, K., Knoll, M.D., Cross, G.M., Luzitano, R.D. & Knight, R. (1993) Processing ground penetrating radar data to improve resolution of near-surface targets. Pp. 561–575 in: Proceedings of Symposium on the Application of Geophysics to Engineering and Environmental Problems (SAGEEP ’93), San Diego, USA.Google Scholar
Goovaerts, P. (1997) Geostatistics for Natural Resources Evaluation. Oxford University Press, New York, USA.CrossRefGoogle Scholar
Knight, R., Tercier, P. & Jol, H. (1997) The role of ground-penetrating radar and geostatistics in reservoir description. The Leading Edge, 16, 1576–1582.CrossRefGoogle Scholar
Lesch, S.M., Rhoades, J.D., Lund, L.J. & Corwin, D.L. (1992) Mapping soil salinity using calibrated electromagnetic measurements. Soil Science Society of America Journal, 56, 540–548.Google Scholar
McNeill, J.D. (1980) Electromagnetic Terrain Conductivity Measurement at Low Induction Numbers. Geonics Limited, Technical Note TN 6. Geonics Ltd. Mississauga, Ontario, Canada.Google Scholar
Sandmeier Scientific Software (2008) Reflexw, v.4.5.5, Program for processing and interpretation of reflection and transmission data. Karlruhe, Germany.Google Scholar
Senechel, P., Perroud, H. & Garambois, S. (2000) Geometrical and physical parameters comparison between GPR data and other geophysical data. Extended abstract, 8th International Conference on Ground Penetrating Radar. Gold Coast/Australia, 2326/05/2000.CrossRefGoogle Scholar
Wackernagel, H. (2003) Multivariate Geostatistics: an Introduction with Applications. Springer Verlag, Berlin, 3rd ed., 388 pp.Google Scholar
Williams, B.G. & Hoey, D. (1987) The use of electro-magnetic induction to detect the spatial variability of salt and clay content of soils. Australian Journal of Soil Research, 25, 21–27.Google Scholar