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

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 th