Machine Learning‐Based Hydrofacies Classification: Effects of Noise and Regularization

Abstract

AbstractThis study utilizes an unsupervised ML approach, the expectation‐maximization (EM) algorithm using Gaussian Mixture Models (GMM), to integrate near‐surface geophysics measurements for hydrofacies classification. We examined the impact of noise and noise estimation on classification across two synthetic models with varying lateral heterogeneity, simulating and inverting resistivity and seismic data with noise levels ranging from minimal to very high. The algorithm proved robust in accurately reconstructing hydrofacies when noise was correctly estimated or not significantly underestimated, showing minimal misclassification in shallow hydrofacies. However, severe underestimation of noise during inversion led to increased misclassifications and artifact‐laden hydrofacies models, especially in shallow regions. Higher lateral heterogeneity lessened the negative impact of noise, slightly improving algorithm performance when noise was correctly estimated. We also explored the influence of geophysical measurement uncertainties on classification uncertainty through hydrofacies probability maps, noting the greatest impact when noise was underestimated. Additionally, we investigated the effect of the regularization trade‐off parameter on the hydrofacies classification and show how the performance of the algorithm can be evaluated in the absence of ground truth data using the average silhouette score of the classification with data obtained from a basement complex field site. We found that moderate regularization (λ = 200) yielded the best hydrofacies model, as indicated by the highest average silhouette score. Our findings underscore the effectiveness of unsupervised ML for facies classification and emphasizes the critical role of accurate noise characterization in geophysical data processing for enhancing the integration of subsurface heterogeneity information into hydrological models.