Thermal conductivity of some rock-forming minerals: a tabulation

Diment, W.H.; Pratt, Howard R.

doi:10.3133/ofr88690

Cited by 33 publications

(20 citation statements)

References 12 publications

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“…Salts, such as those discovered by the Thermal Emission Imaging System (THEMIS) and Thermal Emission Spectrometer (TES) [ Osterloo et al , 2008] may have conductivities as high as 10 J s −1 m −1 K −1 [ Diment and Pratt , 1988]. The conductivity of sulfates and oxides ranges from 1 to 12 J s −1 m −1 K −1 depending on the minerals [ Diment and Pratt , 1988]. In this study, the cement phase thermal conductivity values range from 0.5 to 12 J s −1 m −1 K −1 .…”

Section: Methodsmentioning

confidence: 99%

A model of thermal conductivity for planetary soils: 2. Theory for cemented soils

2009

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A numerical model of heat conduction through particulate media made of spherical grains cemented by various bonding agents is presented. The pore‐filling gas conductivity, volume fraction, and thermal conductivity of the cementing phase are tunable parameters. Cement fractions <0.001–0.01% in volume have small effects on the soil bulk thermal conductivity. A significant conductivity increase (factor 3–8) is observed for bond fractions of 0.01 to 1% in volume. In the 1 to 15% bond fraction domain, the conductivity increases continuously but less intensely (25–100% conductivity increase compared to a 1% bond system). Beyond 15% of cements, the conductivity increases vigorously and the bulk conductivity rapidly approaches that of bedrock. The composition of the cements (i.e. conductivity) has little influence on the bulk thermal inertia of the soil, especially if the volume of bond <10%. These results indicate that temperature measurements are sufficient to detect cemented soils and quantify the amount of cementing phase, but the mineralogical nature of the bonds and the typical grain size are unlikely to be determined from orbit. On Mars, a widespread surface unit characterized by a medium albedo (0.19–0.26) and medium/high thermal inertia (200–600 J s−0.5 m−2 K−1) has long been hypothesized to be associated with a duricrust. The fraction of cement required to fit the thermal data is less than ∼1–5% by volume. This small amount of material is consistent with orbital observations, confirming that soil cementation is an important factor controlling the thermal inertia of the Martian surface.

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Section: Methodsmentioning

confidence: 99%

A model of thermal conductivity for planetary soils: 2. Theory for cemented soils

2009

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“…In particular, k t of the main silicate minerals can vary from ∼1.6 to >7 W m −1 K −1 at room P‐T conditions, although ∼90% of typical rocks exhibit k t values of 2–4 W m −1 K −1 [cf. Diment and Pratt , ; Clauser and Huenges , ; Jokinen and Kukkonen , ; Jaupart and Mareschal , ]. Likewise, although RHP of typical lithospheric rocks can vary by as much as ∼0 to >10 μW m −3 between individual samples, regional averages (more suitable for lithospheric studies) are typically limited to RHP values <3.6 μW m −3 [ Vilá et al , ; Jaupart and Mareschal , ].…”

Section: Mineral Assemblages and Inexact Physical Theoriesmentioning

confidence: 99%

3‐D multiobservable probabilistic inversion for the compositional and thermal structure of the lithosphere and upper mantle. I:a prioripetrological information and geophysical observables

et al. 2013

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[1] Traditional inversion techniques applied to the problem of characterizing the thermal and compositional structure of the upper mantle are not well suited to deal with the nonlinearity of the problem, the trade-off between temperature and compositional effects on wave velocities, the nonuniqueness of the compositional space, and the dissimilar sensitivities of physical parameters to temperature and composition. Probabilistic inversions, on the other hand, offer a powerful formalism to cope with all these difficulties, while allowing for an adequate treatment of the intrinsic uncertainties associated with both data and physical theories. This paper presents a detailed analysis of the two most important elements controlling the outputs of probabilistic (Bayesian) inversions for temperature and composition of the Earth's mantle, namely the a priori information on model parameters, (m), and the likelihood function, L(m). The former is mainly controlled by our current understanding of lithosphere and mantle composition, while the latter conveys information on the observed data, their uncertainties, and the physical theories used to relate model parameters to observed data.[2] The benefits of combining specific geophysical datasets (Rayleigh and Love dispersion curves, body wave tomography, magnetotelluric, geothermal, petrological, gravity, elevation, and geoid), and their effects on L(m), are demonstrated by analyzing their individual and combined sensitivities to composition and temperature as well as their observational uncertainties. The dependence of bulk density, electrical conductivity, and seismic velocities to major-element composition is systematically explored using Monte Carlo simulations. We show that the dominant source of uncertainty in the identification of compositional anomalies within the lithosphere is the intrinsic nonuniqueness in compositional space. A general strategy for defining (m) is proposed based on statistical analyses of a large database of natural mantle samples collected from different tectonic settings (xenoliths, abyssal peridotites, ophiolite samples, etc.). This strategy relaxes more typical and restrictive assumptions such as the use of local/limited xenolith data or compositional regionalizations based on age-composition relations. We demonstrate that the combination of our (m) with a L(m) that exploits the differential sensitivities of specific geophysical observables provides a general and robust inference platform to address the thermochemical structure of the lithosphere and sublithospheric upper mantle. An accompanying paper deals with the integration of these two functions into a general 3-D multiobservable Bayesian inversion method and its computational implementation.

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“…Anisotropies in highly porous clays are near 1.0, whereas porosity anisotropies calculated at zero porosity for the clays and claystones in this study fall in the range 1.9 to 2.4. Even these highly compacted clays still display a significant degree of disorder, however, since the anisotropy of individual sheet silicates can be greater than 5 (Goldsmid & Bowley 1960;Cermák & Rybach 1982;Diment & Pratt 1988;Deming, 1994).…”

Section: Discussionmentioning

confidence: 98%

Changes in matrix thermal conductivity of clays and claystones as a function of compaction

Waples¹,

Tirsgaard

2002

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Vertical matrix thermal conductivities of clays and claystones in onshore Denmark decrease with decreasing porosity, probably because of increasing orientation of highly anisotropic clay platelets during compaction. The relationship between vertical matrix conductivity vm and porosity for this dataset can be expressed as either vm =2.544exp~0.943 ! or vm =2.749exp~0.637 !, depending upon the statistical methods used to analyse the data. Using the first equation the vertical matrix conductivity of the Danish clays and claystones is found to be about 4.9 W m 1 K 1 in highly porous sediments, decreasing to 2.54 W m 1 K 1 when porosity reaches zero. Using the second equation the vertical matrix conductivity of the Danish clays and claystones is about 4.3 W m 1 K 1 in highly porous sediments, decreasing to about 2.75 W m 1 K 1 at zero porosity. Anisotropy varies from an assumed value of 1.02 in highly porous clays to 2.44 at zero porosity using the first equation, and 1.87 at zero porosity using the second equation. These values agree well with measured data.This phenomenon is probably common or universal in fine-grained clay-rich sediments, and this method of data analysis could probably be applied to most or all claystone datasets. However, the equations derived in this study will be most appropriate for shales and claystones with comparatively high quartz contents similar to those of the samples analysed. Other specific relationships will probably exist for clay-rich shales and more-pure claystones.

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Thermal conductivity of some rock-forming minerals: a tabulation

Cited by 33 publications

References 12 publications

A model of thermal conductivity for planetary soils: 2. Theory for cemented soils

A model of thermal conductivity for planetary soils: 2. Theory for cemented soils

3‐D multiobservable probabilistic inversion for the compositional and thermal structure of the lithosphere and upper mantle. I:a prioripetrological information and geophysical observables

Changes in matrix thermal conductivity of clays and claystones as a function of compaction

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