Hydrogeophysical characterization in a volcanic context from local to regional scales combining airborne electromagnetism and magnetism

Dumont, Marc; Reninger, Pierre-Alexandre; Aunay, Bertrand; Pryet, Alexandre; Jougnot, Damien; Join, Jean-Lambert; Michon, Laurent; Martelet, Guillaume

doi:10.1002/essoar.10505311.1

Cited by 2 publications

(2 citation statements)

References 22 publications

(28 reference statements)

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“…For example, natural clayey rocks and soils usually contain more than one kind of clay mineral (e.g., kaolinite, illite, and smectite), different kinds of clay particles inherently hold different

{\sigma }_{\text{cp}}

. Patchett (1975) presented one method to average

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out of

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based on their mass fractions which works well on experiments (e.g., Woodruff and Revil (2011); Dumont et al., 2021). Additionally, inorganic precipitation on a clay surface decreases its conductivity

{\sigma }_{\text{cp}}

(e.g., Wu et al., 2010).…”

Section: Model Developmentmentioning

confidence: 99%

Electrical Conductivity of Clayey Rocks and Soils: A Non‐Linear Model

2022

Geophysical Research Letters

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Electrical conductivity of different rocks and soils lays the base for electrical logging and electrical and electromagnetic (E&EM) methods that have been extensively used for near-surface, environmental, and hydrogeological investigations (e.g., Glover, 2015). It is the pioneering work of Archie (1942) that makes the quantitative assessment of porosity available and paves the way for other petrophysical parameters. Several years later, Patnode and Wyllie (1950) found that Archie's law is only suitable to "clean" samples as clayey minerals in the "dirty" ones present themselves as "conductive solids". Winsauer and McCardell (1953) ascribed the clay conductivity to its electrical double layer (EDL) for the first time. Thus the additional component Patnode and Wyllie (1950) added to Archie's law shall be called the surface conductivity. Soon after, Wyllie and Southwick (1954) discovered that the surface conductivity is not constant but differs at low and intermediate salinities, and they presented a three-resistor (TR) model to explain this non-linearity. It shall be clarified here that "cleanness" and "dirtiness" are relative terminologies that are essentially a question of the ratio between surface and pore water conductivity (i.e., the Dukhin number. cf, Shilov et al., 2001). Indeed, one can find surface conductivity even in clean glass beads (e.g., Bolève et al., 2007).Although research on the electrical conductivity of clayey rocks and soils progressed significantly in the 1950s, the following years are not always the case. Despite the fact that many models have been presented to account for this non-linear characteristic, they are argued not to comply with electrochemical theories or not coincide with measurements, and most are too complex to use (Bussian, 1983). For example, the TR model (Wyllie & Southwick, 1954) can explain measurements well, yet it is exclusive of EDL, clay minerals being treated as classic conductors. The Waxman-Smits (WS) model (Waxman & Smits, 1968), probably the most popular model, postulates that the ionic mobility decreases with the diminishing ionic concertation which is opposite to the electrochemical theory (e.g., Bockris & Reddy, 1998). The Padé approximant (PA) model (Schwartz et al., 1989) first calculates the low-salinity conductivity with surface tortuosity and the high-salinity conductivity with bulk

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{\sigma }_{\text{cp}}

. Patchett (1975) presented one method to average

{\sigma }_{\mathrm{c}}

out of

{\sigma }_{\text{cp}}

{\sigma }_{\text{cp}}

(e.g., Wu et al., 2010).…”

Section: Model Developmentmentioning

confidence: 99%

Electrical Conductivity of Clayey Rocks and Soils: A Non‐Linear Model

2022

Geophysical Research Letters

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“…Within the last few years, AEM surveys are covering increasingly larger areas at regional basin scales, as large as 140,000 km 2 (32), and are moving from predominantly flat areas [e.g., (33,34)] to mountainous environments (35)(36)(37). There is an increased effort to use AEM data to construct hydrological models from local to regional scales (38)(39)(40).…”

Section: Introductionmentioning

confidence: 99%

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

et al. 2022

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Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

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Hydrogeophysical characterization in a volcanic context from local to regional scales combining airborne electromagnetism and magnetism

Cited by 2 publications

References 22 publications

Electrical Conductivity of Clayey Rocks and Soils: A Non‐Linear Model

Electrical Conductivity of Clayey Rocks and Soils: A Non‐Linear Model

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics

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