Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River.

A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange. Plain Language Summary: A self‐potential (SP)‐data inversion method was developed and tested on synthetic models of electrical‐potential data and electrical‐potential field data acquired by monitoring voltages through time at multiple locations on the flood plain of a river flowing over a bedrock aquifer. The SP‐data inversion method (a) calculates horizontal electrical‐potential profile data over buried electrical sources with user‐defined properties, (b) finds the locations of unknown electrical sources of electrical‐potential data by particle swarm optimization (PSO), (c) performs electrical‐source imaging using the locations of the sources determined by PSO as a guide, and (d) enables real‐time monitoring of water exchange between a river and a bedrock aquifer. The results obtained by applying the inversion method to SP‐monitoring data depict transient electrical changes in a bedrock aquifer that are affected by changes in streamflow and attributed to water exchange between the river and aquifer along a fault. Key Points: Hyporheic exchange flows in East Fork Poplar Creek occur along fault lines and fractures in the limestone bedrock riverbedSelf‐potential tomography is preconditioned by particle swarm optimization and applied to synthetic models and self‐potential monitoring dataInverse models image electrical sources attributed to groundwater flow along a known fault in the flood plain of East Fork Poplar Creek [ABSTRACT FROM AUTHOR]