A model of transmissivity and hydraulic conductivity from electrical resistivity distribution derived from airborne electromagnetic surveys of the Mississippi River Valley Alluvial Aquifer, Midwest USA

Ikard, Scott J.; Minsley, Burke J.; Rigby, James R.; Kress, Wade H.

doi:10.1007/s10040-022-02590-6

Cited by 3 publications

(1 citation statement)

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“…Resistivity models from airborne electromagnetic (AEM) surveys can be used to estimate aquifer parameters and aquifer frameworks (N. K. Christensen, Ferre, et al., 2017; N. K. Christensen, Minsley, & Christensen, 2017; N. K. Christensen et al., 2016; Ikard et al., 2023; Kang et al., 2021; Knight et al., 2018; Korus et al., 2017; Marker et al., 2015; Minsley, Foks, & Bedrosian, 2021; Minsley, Rigby, et al., 2021). The relationship between resistivity and hydraulic conductivity can be influenced by factors such as porewater chemistry and the presence of clay minerals.…”

Section: Introductionmentioning

confidence: 99%

Three‐Dimensional Probabilistic Hydrofacies Modeling Using Machine Learning

Kawo,

Korus,

Kishawi

et al. 2024

Water Resources Research

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Characterizing the 3D distribution of hydraulic properties in glacial sediments is challenging due to fine‐scale heterogeneity and complexity. Borehole lithological data provide high vertical resolution but low horizontal resolution. Geophysical methods can fill gaps between boreholes, providing improved horizontal resolution but low vertical resolution. Machine learning can combine borehole and geophysical data to overcome these challenges. However, few studies have compared multiple machine learning methods for predicting hydrofacies in glacial aquifer systems. This study uses colocated airborne electromagnetic resistivity and borehole lithology data to train multiple machine learning models and predict the 3D distribution of hydrofacies in glacial deposits of eastern Nebraska, USA. Random Forest, Gradient Boosting Classifier, Extreme Gradient Boosting, Multilayer Perceptron, and Stacking Classifier were used to model 3D probabilistic distributions of hydrofacies (sand and clay) at a grid size of 200 m × 200 m × 3 m. Comparison of the predicted 3D hydrofacies models shows that the probability distributions and the contrasts between hydrofacies vary. The classification metrics show that the Stacking Classifier model performed better than other machine learning models in predicting hydrofacies. Multi‐Layer Perceptron and Stacking Classifier models show sharp vertical transitions between the low and high sand probability while other machine learning models show gradual transitions. K‐means clustering was used to translate the Stacking Classifier model into a 4‐class hydraulic conductivity model. This study shows that machine learning methods advance our understanding of glacial hydrogeology by improving the vertical and horizontal resolution of hydrofacies distribution and resolving aquifer‐aquifer and stream‐aquifer connections.

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