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

Ikard, Scott J.; Carroll, Kenneth C.; Brooks, Scott C.; Rucker, Dale F.; Smith‐Vega, Gladisol; Elwes, Aubrey

doi:10.1029/2024WR037549

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

Journal Article · 26 October 2024 · Water Resources Research

DOI: https://doi.org/10.1029/2024WR037549 · OSTI ID:2475249

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^[4]; Smith‐Vega, Gladisol ^[2]; Elwes, Aubrey ^[2]

U.S. Geological Survey Oklahoma‐Texas Water Science Center Austin TX USA
New Mexico State University College of Agricultural, Consumer, and Environmental Sciences Las Cruces NM USA
Oak Ridge National Laboratory Oak Ridge TN USA
hydroGEOPHYSICS, Inc. Tucson AZ USA

Abstract 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.

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Research Organization:: New Mexico State University, Las Cruces, NM (United States)

Sponsoring Organization:: U.S. Department of Energy Office of Science Research Development and Partnership Pilot (RDPP); USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC05-00OR22725; SC0023132

OSTI ID:: 2475249

Journal Information:: Water Resources Research, Journal Name: Water Resources Research Journal Issue: 10 Vol. 60; ISSN 0043-1397

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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