Using bivariate multiple-point statistics and proximal soil sensor data to map fossil ice-wedge polygons

article i nfo Article history: Multiple-point statistics (MPS) is a collection of geostatistical simulation algorithms that uses a multiple-point training image (TI) as structural model instead of a two-point variogram. MPS allows to simulate more complex random fields, like phenomena characterized by spatial connectivity. A very recent development is multivariate MPS in which an ensemble of variables can be simulated simultaneously using a multivariate TI. We investigated if multivariate MPS can be used for the processing of proximal soil sensor data, i.e. interpolating the sensor data and predicting the target variable. We measured a field with fossil ice-wedge polygons in the subsoil with an electromagnetic induction sensor and used these measurements to predict the location of wedge material in the subsoil. We built a bivariate TI with a categorical image of a random polygonal network as primary variable and a continuous image of the corresponding sensor values as secondary variable. Then, we performed a bivar- iate reconstruction with the recently developed Direct Sampling software. The resulting E-types provided an in- terpolated sensor data map and a probability map predicting the location of wedge material in the subsoil. This procedure was compared to the more traditional approach of interpolating the sensor data with ordinary kriging and performing a fuzzy k-means classification. Comparing the resulting maps with an aerial photograph reveal- ing the location of the ice-wedges through polygonal crop marks, showed that MPS reconstructed the polygonal patterns much better. The local accuracy of the MPS maps was proven by an independent quantitative validation based on nine extra measurement lines and 94 bore hole samples. As a first application in soil science, our case study showed that multivariate MPS can be used for the processing of proximal soil sensor data. The flexibility of the technique opens perspectives for other new applications and therefore multivariate MPS can become a valu- able part of the pedometrical toolbox.

[1]  Philippe De Maeyer,et al.  Reconstructing palaeochannel morphology with a mobile multicoil electromagnetic induction sensor , 2011 .

[2]  Lieven Verbeke,et al.  Extracting topsoil information from EM38DD sensor data using a neural network approach , 2009 .

[3]  R. Olea.  Geostatistics for Natural Resources Evaluation By Pierre Goovaerts, Oxford University Press, Applied Geostatistics Series, 1997, 483 p., hardcover, $65 (U.S.), ISBN 0-19-511538-4 , 1999 .

[4]  Thomas F. Kent,et al.  Image analysis and fractal geometry to characterize soil desiccation cracks , 2009 .

[5]  L. Cockx,et al.  Prospecting frost‐wedge pseudomorphs and their polygonal network using the electromagnetic induction sensor EM38DD , 2006 .

[6]  L. Cockx,et al.  Upgrading a 1/20,000 soil map with an apparent electrical conductivity survey , 2008 .

[7]  P. De Smedt,et al.  Mapping complex soil patterns with multiple‐point geostatistics , 2013 .

[8]  A. Dassargues,et al.  Application of multiple-point geostatistics on modelling groundwater flow and transport in a cross-bedded aquifer (Belgium) , 2009 .

[9]  Hugh M. French,et al.  The Periglacial Environment , 1977 .

[10]  S. Strebelle,et al.  Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Principal Component Analysis and Multiple-Point Geostatistics , 2003 .

[11]  G. Mariéthoz,et al.  An Improved Parallel Multiple-point Algorithm Using a List Approach , 2011 .

[12]  P. Adler,et al.  Multiple-Point Statistics for Modeling Facies Heterogeneities in a Porous Medium: The Komadugu-Yobe Alluvium, Lake Chad Basin , 2011 .

[13]  B. Werner,et al.  Modelling of ice‐wedge networks , 2008 .

[14]  B. T. Werner,et al.  Nonlinear dynamics of ice-wedge networks and resulting sensitivity to severe cooling events , 2002, Nature.

[15]  A. McBratney,et al.  Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions , 1997 .

[16]  John Triantafilis,et al.  Five Geostatistical Models to Predict Soil Salinity from Electromagnetic Induction Data Across Irrigated Cotton , 2001 .

[17]  Mohammad M. Islam,et al.  Delineating water management zones in a paddy rice field using a Floating Soil Sensing System , 2011 .

[18]  B. De Vos,et al.  Using the EM38DD soil sensor to delineate clay lenses in a sandy forest soil , 2007 .

[19]  R. Gil Pontius,et al.  Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA , 2001 .

[20]  Dennis L. Corwin,et al.  Characterizing soil spatial variability with apparent soil electrical conductivity , 2005 .

[21]  Gregoire Mariethoz,et al.  The Direct Sampling method to perform multiple‐point geostatistical simulations , 2010 .

[22]  A. Journel,et al.  The Necessity of a Multiple-Point Prior Model , 2007 .

[23]  M. Meirvenne,et al.  Reconstructing the paleotopography beneath the loess cover with the aid of an electromagnetic induction sensor , 2008 .

[24]  J. Hummelb,et al.  On-the-go soil sensors for precision agriculture , 2004 .

[25]  Josse De Baerdemaeker,et al.  Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area , 2008 .

[26]  Alex B. McBratney,et al.  An overview of pedometric techniques for use in soil survey , 2000 .

[27]  Alex B. McBratney,et al.  Sampling for High-Resolution Soil Mapping , 2010 .

[28]  Alex B. McBratney,et al.  Application of fuzzy sets to climatic classification , 1985 .

[29]  S. Gorelick,et al.  Identifying discrete geologic structures that produce anomalous hydraulic response: An inverse modeling approach , 2008 .

[30]  E. Kolstrup.  Reappraisal of the upper weichselian periglacial environment from Danish frost wedge casts , 1986 .

[31]  Philippe De Smedt,et al.  Imaging a Polygonal Network of Ice‐Wedge Casts with an Electromagnetic Induction Sensor , 2011 .

[32]  Sebastien Strebelle,et al.  Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics , 2002 .

[33]  3D Porosity Modeling of a Carbonate Reservoir Using Continuous Multiple-Point Statistics Simulation , 2006 .

[34]  C. Ballantyne.  The periglacial environment. Third Edition. Hugh M. French. John Wiley and Sons Ltd, Chichester, UK, 2007. xviii + 458 pp. ISBN-13: 978-0-470-86588-0 (HB), 978-0-470-86589-7 (PB) , 2008 .

[35]  J. D. Mcneill.  Electromagnetic Terrain Conduc-tivity Measurement at Low Induction Numbers , 1980 .

[36]  P. Renard,et al.  Three-dimensional high resolution fluvio-glacial aquifer analog - Part 2: Geostatistical modeling , 2011 .

[37]  L. Cockx,et al.  Comparing the EM38DD and DUALEM‐21S Sensors for Depth‐to‐Clay Mapping , 2009 .

[38]  Philippe Renard,et al.  A practical guide to performing multiple-point statistical simulations with the Direct Sampling algorithm , 2013, Comput. Geosci..

[39]  R. M. Srivastava,et al.  Multivariate Geostatistics: Beyond Bivariate Moments , 1993 .