2013
DOI: 10.3997/1873-0604.2013043
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Faulting and groundwater in a desert environment: constraining hydrogeology using time‐domain electromagnetic data

Abstract: Within the south‐western Mojave Desert, the Joshua Basin Water District is considering applying imported water into infiltration ponds in the Joshua Tree groundwater sub‐basin in an attempt to artificially recharge the underlying aquifer. Scarce subsurface hydrogeological data are available near the proposed recharge site; therefore, time‐domain electromagnetic (TDEM) data were collected and analysed to characterize the subsurface. TDEM soundings were acquired to estimate the depth to water on either side of t… Show more

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Cited by 9 publications
(4 citation statements)
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“…Both inversion approaches employ one‐dimensional (1‐D) physics, in which resistivity is assumed to only vary with depth. Although lateral contrasts in resistivity most certainly occur, the 1‐D modeling assumption is generally a valid approximation except within 100–300 m of a sharp lateral resistivity contact (Bedrosian et al., 2013). We used a deterministic spatially constrained inversion (SCI) and laterally constrained inversion (LCI) (Auken et al., 2015) carried out with Aarhus Workbench software (Aarhus Geosoftware) as well as a Bayesian Markov‐chain Monte Carlo (MCMC) inversion implemented in the open‐source U.S. Geological Survey code Geophysical Bayesian Inference in Python (GeoBIPy; Minsley, 2011; Minsley et al., 2020; Table S2).…”
Section: Methodsmentioning
confidence: 99%
“…Both inversion approaches employ one‐dimensional (1‐D) physics, in which resistivity is assumed to only vary with depth. Although lateral contrasts in resistivity most certainly occur, the 1‐D modeling assumption is generally a valid approximation except within 100–300 m of a sharp lateral resistivity contact (Bedrosian et al., 2013). We used a deterministic spatially constrained inversion (SCI) and laterally constrained inversion (LCI) (Auken et al., 2015) carried out with Aarhus Workbench software (Aarhus Geosoftware) as well as a Bayesian Markov‐chain Monte Carlo (MCMC) inversion implemented in the open‐source U.S. Geological Survey code Geophysical Bayesian Inference in Python (GeoBIPy; Minsley, 2011; Minsley et al., 2020; Table S2).…”
Section: Methodsmentioning
confidence: 99%
“…TDEM method provides the measure the electrical resistivity distribution of the ground through the induction of eddy currents in the subsoil (Christiansen et al, ; Nabighian & Macnae, ). It is widely used to image the electrical properties of subsurface geological structures over different lithological settings with the capability to reach significant penetration depths over moderately resistive environments (few hundreds of meters; Bedrosian et al, ). We follow the approach by Civico et al () to map the top bedrock depths using 1‐D vertical resistivity models.…”
Section: Methodsmentioning
confidence: 99%
“…In fact, the ratio of penetration depth to loop size for TDEM can be much greater than 1, as opposed to electrical resistivity tomography, where great penetration depths require long electrode arrays [ Revil et al ., ; Pucci et al ., ]. Ground TDEM surveys were rarely used in the recent past to map fault zone [e.g., Bedrosian et al ., ] or for depth‐to‐bedrock investigations over a fault‐controlled area [e.g., Bedrosian et al ., ].…”
Section: Methods and Data Acquisitionmentioning
confidence: 99%