Towards surface-based hydraulic conductivity mapping using seismoelectric signals and data-driven models

Estimating vertical hydraulic conductivity ( K v ) in semi-arid basins is challenging because lithological heterogeneity limits the representativeness of sparse hydraulic measurements. This study evaluates a surface-based seismoelectric workflow for inferring depth-resolved K v profiles without using drilling information during model development. Borehole data were reserved for external validation. The workflow integrates field acquisition, geospatial data synthesis, signal conditioning, physically guided feature engineering, supervised regression benchmarking, and hydrogeological expert appraisal. Its main contribution is a drilling-independent modeling strategy that translates surface seismoelectric attributes into vertical K v profiles. This provides pre-drilling information for well siting, screen-interval design, and planning of drilling and completion costs. A high-density survey was conducted in the Ñuble Region of central Chile using a dual-channel seismoelectric system with investigation depths of up to 200 m. More than 500 georeferenced stations were acquired. Seismoelectric attributes derived from the dual-channel response, together with topographic descriptors, were used to train and compare multiple regression models. Compared with a baseline configuration based only on raw voltage and depth, the augmented model showed stronger cross-validated performance. The coefficient of determination increased from R 2 = 0.711 to R 2 = 0.968 , RMSE decreased from 3.534 to 1.305 m/d, and MAE decreased from 1.368 to 0.546 m/d. The predicted K v profiles better preserved persistent high- and low-conductivity intervals and abrupt vertical transitions than the baseline model. They were also consistent with independent lithostratigraphic evidence and post-drilling yield verification at the validation site. Profile-level smoothing with Savitzky–Golay and wavelet filters improved visual continuity while preserving the dominant conductivity contrasts. These results support the use of surface seismoelectric data combined with supervised learning to reconstruct vertical K v structure in geologically complex and data-scarce settings.