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Determination of Aggregate Surface Morphology at the Interfacial Transition Zone ITZ
Published on: December 16, 2019
Quantitative characterisation of Quaternary glaciofluvial aquifer heterogeneity using cluster analysis
Felipe Gallardo Ceron1, Landis Jared West1, Ian T Burke2
1Institute of Applied Geosciences, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT UK.
None:
Hydrogeological modelling requires the characterisation of hydrofacies and the representation of their spatial distribution. In this work, a workflow was developed to characterise heterogeneous unconsolidated glaciofluvial sediments in Northwest Cumbria (UK), which involved: (1) field sampling; (2) lithofacies classification; (3) in situ porosity measurements using the sand-replacement method; (4) determination of the particle size distribution (PSD); (5) hydrofacies definition via K-means cluster analysis using PSD data; (6) evaluation of empirical equations for predicting porosity using field measurements and regression analysis; and (7) estimation of hydraulic conductivity (K) using the Kozeny-Carman equation. Nine lithofacies were identified, including glaciofluvial silts, sands and gravels, and local till deposits. Three clusters were defined on the basis of PSD: fine-dominated (cluster-1), sand-dominated (cluster-2) and gravel-dominated (cluster-3). Cluster-1 exhibited the highest porosities (average 44%); cluster-2 showed intermediate to high porosities, with an average porosity of 40%; and cluster-3 had the lowest porosities (average 27%). The logarithm of d 50 was the parameter with the highest correlation with measured porosities (R 2 of 0.789). K values estimated using the Kozeny-Carman equation ranged between 0.06 and 0.2 m/d for cluster-1, 0.2-11 m/d for cluster 2, and 0.1-62 m/d for cluster 3. Measured porosities were higher than previously reported, while estimated K values were consistent with those from hydraulic tests. Comparison between lithofacies and clustering classification suggests that, for hydrofacies classification, the unsupervised cluster analysis approach is able to generate a classification that captures the hydrogeologically important details without creating an excessive number of categories.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s10040-025-02933-z.
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