Assessment of soil slurry saturation and slurry infiltration in pig slurry storage ponds using electrical resistivity tomography

Abstract Effective management of pig slurry in semi‐arid settings is critical to protect soils and aquifers. This study integrates multi‐profile electrical resistivity tomography (ERT) compiled into stitched 3D volumes with a calibration based on modified Archie's law derived from laboratory resistivity–moisture tests to obtain semi‐quantitative soil slurry saturation ( defined here as the fraction of connected pore space filled by pig slurry), beneath three active storage ponds in southeastern Spain, developed on silty clay (pond 1), sandy loam (pond 2), and silt loam (pond 3). Twenty dipole–dipole ERT profiles were inverted in 2D using a smoothness‐constrained (L2) inversion, and the resulting 2D models were stitched into 3D volumes for visualization and interpretation. Nine near‐pond soil cores were analyzed to determine texture‐specific porosity and to build calibration curves by incrementally wetting samples with pig slurry and normalizing resistivity to 25°C. In the modified Archie workflow, the cementation exponent ( m ) was selected per pond by minimizing cross‐validated misfit within literature bounds, while the saturation exponent ( n ) and the reference resistivity at full saturation ( were obtained from log–log regressions; pore‐fluid resistivity () was measured on the pig slurry. Calibrated models indicate a textural control on infiltration: silty clay shows shallow influence with declining below ∼3 m; sandy loam exhibits deeper and laterally extensive percolation to ∼4.5 m; silt loam is intermediate, maintaining elevated to ∼4–5 m. Across textures, resistivity increases sharply once falls below the laboratory breakpoint thresholds ≈ 0.37, 0.48, and 0.28 for ponds 1–3, respectively (see Section 3.2), indicating a loss of continuous conductive pathways. These texture‐specific thresholds were determined from laboratory resistivity–moisture curves as knee points marking the onset of an abrupt resistivity increase at low moisture and were converted from gravimetric moisture content to using Equation (5). Because bulk resistivity depends on saturation and pore‐fluid properties and m was fixed at its pond‐level calibrated value, maps are reported as semi‐quantitative. The Archie‐based approach offers a practical, non‐invasive tool to locate likely leakage pathways and to prioritize targeted verification for risk management of slurry ponds.