Multi-well clustering and inverse modeling-based approaches for exploring geometry, petrophysical, and hydrogeological parameters of the Quaternary aquifer system around Debrecen area, Hungary.

This research aims to explore the application of an unsupervised machine learning and inverse modeling-based methods to map the aquifers geometry and investigate the petrophysical and hydrogeological parameters of the Quaternary aquifer system around the Debrecen area, Eastern Hungary. The study utilized a limited well-logs, including spontaneous potential, natural gamma ray, and normal resistivity logs. The k-means clustering technique is applied to identify the distribution of lithological facies within the formerly identified basin-scale hydrostratigraphical units of coarsening upward, alluvial, valley incision, and Late Miocene deposits. Based on the mathematical and geological considerations, the analysis revealed three main clusters (C1, C2, and C3), representing different lithofacies of shale, shaly sand, and sand and gravel. The result of the cluster analysis is further validated with a surface geophysical survey using the vertical electrical sounding (VES) technique. Furthermore, an inverse-modeling-based approach using Csókás method is employed to detect the vertical and horizontal distribution of the hydraulic conductivity along these units. Csókás model is empirically modified from the Kozeny-Carman equation that suites the unconsolidated freshwater-bearing units. The results indicated that the petrophysical and hydrogeological parameters widely ranged with the hydraulic conductivity between almost zero in the shaly layers to more than 21.5 m/d in the sandy and gravely layers. However, the valley incision unit aquifers showed a more uniform distribution of hydraulic conductivity. Based on these results, the Quaternary aquifers in the Debrecen area are classified as moderate to highly productive and ideal for groundwater development. The applied methodology contributed to understanding the complexity of the hydrogeological conditions, providing a robust approach to characterize the heterogeneous groundwater systems.