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Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure

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  • Marina V Karsanina
  • Kirill M Gerke
  • Elena B Skvortsova
  • Dirk Mallants

Abstract

Structural features of porous materials such as soil define the majority of its physical properties, including water infiltration and redistribution, multi-phase flow (e.g. simultaneous water/air flow, or gas exchange between biologically active soil root zone and atmosphere) and solute transport. To characterize soil microstructure, conventional soil science uses such metrics as pore size and pore-size distributions and thin section-derived morphological indicators. However, these descriptors provide only limited amount of information about the complex arrangement of soil structure and have limited capability to reconstruct structural features or predict physical properties. We introduce three different spatial correlation functions as a comprehensive tool to characterize soil microstructure: 1) two-point probability functions, 2) linear functions, and 3) two-point cluster functions. This novel approach was tested on thin-sections (2.21×2.21 cm2) representing eight soils with different pore space configurations. The two-point probability and linear correlation functions were subsequently used as a part of simulated annealing optimization procedures to reconstruct soil structure. Comparison of original and reconstructed images was based on morphological characteristics, cluster correlation functions, total number of pores and pore-size distribution. Results showed excellent agreement for soils with isolated pores, but relatively poor correspondence for soils exhibiting dual-porosity features (i.e. superposition of pores and micro-cracks). Insufficient information content in the correlation function sets used for reconstruction may have contributed to the observed discrepancies. Improved reconstructions may be obtained by adding cluster and other correlation functions into reconstruction sets. Correlation functions and the associated stochastic reconstruction algorithms introduced here are universally applicable in soil science, such as for soil classification, pore-scale modelling of soil properties, soil degradation monitoring, and description of spatial dynamics of soil microbial activity.

Suggested Citation

  • Marina V Karsanina & Kirill M Gerke & Elena B Skvortsova & Dirk Mallants, 2015. "Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-26, May.
    • Handle: RePEc:plo:pone00:0126515
    DOI: 10.1371/journal.pone.0126515
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    References listed on IDEAS

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    1. Saucier, Antoine & Richer, Jacques & Muller, Jiri, 2002. "Assessing the scope of the multifractal approach to textural characterization with statistical reconstructions of images," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(1), pages 231-259.
    2. Kandelous, Maziar M. & Kamai, Tamir & Vrugt, Jasper A. & Šimůnek, Jiří & Hanson, Blaine & Hopmans, Jan W., 2012. "Evaluation of subsurface drip irrigation design and management parameters for alfalfa," Agricultural Water Management, Elsevier, vol. 109(C), pages 81-93.
    3. Biswal, B. & Manwart, C. & Hilfer, R. & Bakke, S. & Øren, P.E., 1999. "Quantitative analysis of experimental and synthetic microstructures for sedimentary rock," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 273(3), pages 452-475.
    4. Biswal, B. & Manwart, C. & Hilfer, R., 1998. "Three-dimensional local porosity analysis of porous media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 255(3), pages 221-241.
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    1. repec:caa:jnlswr:v:preprint:id:64-2023-swr is not listed on IDEAS
    2. Postnicov, Vasily & Karsanina, Marina V. & Khlyupin, Aleksey & Gerke, Kirill M., 2023. "The 2- and 3-point surface correlation functions calculations: From novel exact continuous approach to improving methodology for discrete images," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    3. Nannan Wang & Tibin Zhang, 2024. "Soil pore structure and its research methods: A review," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 19(1), pages 1-24.
    4. Cherkasov, Aleksei & Gerke, Kirill M. & Khlyupin, Aleksey, 2024. "Towards effective information content assessment: Analytical derivation of information loss in the reconstruction of random fields with model uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    5. Karsanina, Marina V. & Gerke, Kirill M., 2023. "Stochastic (re)constructions of non-stationary material structures: Using ensemble averaged correlation functions and non-uniform phase distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

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