The Henkel Petrophysical Plot: Mineralogy and Lithology From Physical Properties

Enkin, Randolph J.; Hamilton, T. S.; Morris, William A.

doi:10.1029/2019gc008818

Cited by 19 publications

(24 citation statements)

References 96 publications

(164 reference statements)

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“…Measurements at PPL were done on a 2.5 cm diameter core of unweathered material using the methodology and instrumentation as described in Enkin et al. (2020). Comparison of physical property results from EOAS‐UBC and GSC‐P show a near 1:1 linear relationship with an R 2 of 0.92 for density (Figure ) and a near‐linear relationship of log (magnetic susceptibility) with an R 2 of 0.97.…”

Section: Methodsmentioning

confidence: 99%

“…Two approaches for doing so for serpentinites include: (a) taking physical property measurements of hand-samples or drill-core and/or (b) using physical property models to inform geophysical survey inversions. We follow the methods outlined in Enkin et al (2020), in which the relationship between physical properties and mineral abundances were used as a forward model to calibrate the Henkel plot (Figure 10) and then to inverse model the mineralogy based on physical properties. In this approach, minerals with similar physical properties and that behave in a similar way are grouped together into mineral endmembers and mixing lines with volumetric proportions are calibrated.…”

Section: Modeling Ultramafic Rock Mineralogymentioning

confidence: 99%

“…For rocks that plot within the curves defined by the forward modeling, we can invert the model to provide estimated volumetric mineral abundances. Following the same framework as in Enkin et al (2020), for the serpentinite model we have three relationships with three unknowns:…”

Section: Inverse Modelingmentioning

confidence: 99%

“…Following the same framework as in Enkin et al. (2020), for the serpentinite model we have three relationships with three unknowns:

\begin{array}{l} UM badbreak+ SB badbreak+ normalM badbreak= 1 \\ (UM× d_{UM}) + (SB× d_{SB}) + (M \times d_{normalM}) = d \\ (UM× K_{UM}) + (SB× K_{SB}) + (M \times K_{normalM}) = K \end{array}

where d = density K = magnetic susceptibility, and UM, SB, and M, refer to the volumetric mineral abundances of ultramafic minerals, serpentine + brucite, and magnetite. These are combined as follows:

(\begin{array}{l} left & 1 & 1 & 1 \\ left & d_{UM} & d_{SB} & d_{M} \\ left & K_{UM} & K_{SB} & K \end{array}) (\begin{matrix} left \\ left UM \\ left SB \\ left M \end{matrix}) = (\begin{matrix} left \\ left 1 \\ left d \\ left K \end{matrix})

…”

Section: Identifying Ultramafic Carbon Sinks Using Physical Propertiesmentioning

confidence: 99%

See 3 more Smart Citations

Deducing Mineralogy of Serpentinized and Carbonated Ultramafic Rocks Using Physical Properties With Implications for Carbon Sequestration and Subduction Zone Dynamics

Cutts

Steinthorsdottir

Turvey

et al. 2021

Geochem Geophys Geosyst

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Ultramafic rocks comprise the largest volumetric portion of the oceanic lithosphere and their alteration is a fundamentally important geologic process on Earth. The high fluid flow associated with serpentinization alters the physio-chemical properties of the oceanic lithosphere, by weakening and allowing it to flex and subduct more easily, and so contribute to delivering volatiles into the mantle wedge and magmatic arcs (e.g.,

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Section: Methodsmentioning

confidence: 99%

Section: Modeling Ultramafic Rock Mineralogymentioning

confidence: 99%

Section: Inverse Modelingmentioning

confidence: 99%

“…Following the same framework as in Enkin et al. (2020), for the serpentinite model we have three relationships with three unknowns:

\begin{array}{l} UM badbreak+ SB badbreak+ normalM badbreak= 1 \\ (UM× d_{UM}) + (SB× d_{SB}) + (M \times d_{normalM}) = d \\ (UM× K_{UM}) + (SB× K_{SB}) + (M \times K_{normalM}) = K \end{array}

(\begin{array}{l} left & 1 & 1 & 1 \\ left & d_{UM} & d_{SB} & d_{M} \\ left & K_{UM} & K_{SB} & K \end{array}) (\begin{matrix} left \\ left UM \\ left SB \\ left M \end{matrix}) = (\begin{matrix} left \\ left 1 \\ left d \\ left K \end{matrix})

…”

Section: Identifying Ultramafic Carbon Sinks Using Physical Propertiesmentioning

confidence: 99%

See 2 more Smart Citations

Deducing Mineralogy of Serpentinized and Carbonated Ultramafic Rocks Using Physical Properties With Implications for Carbon Sequestration and Subduction Zone Dynamics

Cutts

Steinthorsdottir

Turvey

et al. 2021

Geochem Geophys Geosyst

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“…Therefore, an alternative would be to use actual rock susceptibility measurements and their statistical properties. Databases of such a measurement exist for Norway [30], Fennoscandia [31], and Canada [32] and can be used to provide an initial guess of a mean susceptibility and its standard deviation. A refined inversion could be done for those areas to explore the influence by a priori information with more detail.…”

Section: Use Of A-priori Modelmentioning

confidence: 99%

Global High-Resolution Magnetic Field Inversion Using Spherical Harmonic Representation of Tesseroids as Individual Sources

2020

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In this study, we present a novel approach combining the advantages of tesseroids in representing geophysical structures though their voxel-like discretization features with a spherical harmonic representation of the magnetic field. Modelling of the Earth lithospheric magnetic field is challenging since part of the spectra is hidden by the core field and the forward modeled field of a lithospheric magnetization is always biased by the spectral range used. In our approach, a spherical harmonic representation of the magnetic field of spherical prisms (tesseroids) is used for high-resolution magnetic inversion of lithospheric field models. The use of filtered spherical harmonic models of the magnetic field of each tesseroid ensures that the resulting field matches the spectral range of the input data. For the inversion, we use the projected gradient method. The projected gradient method easily allows us to assign an initial guess (i.e., a-priori assumption) for the inversion and avoids negative values of susceptibilities. The latter is providing more plausible models since induced magnetization is assumed to be dominant over the continents and, for the oceans, a remanence model can be subtracted. We show an application of the technique to a synthetic dataset and a satellite-derived lithospheric field model where the model geometry is based on seismic information. We also demonstrate a proof-of-concept for high-resolution tile-wise inversion for the Bangui anomaly in Africa.

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Antarctica's sedimentary basins and their influence on ice sheet dynamics

Aitken

Kulessa

et al. 2023

Preprint

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The Henkel Petrophysical Plot: Mineralogy and Lithology From Physical Properties

Cited by 19 publications

References 96 publications

Deducing Mineralogy of Serpentinized and Carbonated Ultramafic Rocks Using Physical Properties With Implications for Carbon Sequestration and Subduction Zone Dynamics

Deducing Mineralogy of Serpentinized and Carbonated Ultramafic Rocks Using Physical Properties With Implications for Carbon Sequestration and Subduction Zone Dynamics

Global High-Resolution Magnetic Field Inversion Using Spherical Harmonic Representation of Tesseroids as Individual Sources

Antarctica's sedimentary basins and their influence on ice sheet dynamics

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