Random modelling of the lithospheric thermal regime: forward simulations applied in uncertainty analysis

Jokinen, Jarkko; Kukkonen, Ilmo

doi:10.1016/s0040-1951(99)00061-x

Cited by 35 publications

(12 citation statements)

References 14 publications

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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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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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“…The downward continuation of heat flow will cause a 100-130 K uncertainty of Moho temperature at 40-50 km depth, if thermal conductivities and heat productions of multi-layers are simultaneously varied in forward modeling, and the uncertainty decreases slightly to 80-90 K if random-varied basal temperature are used (Jokinen and Kukkonen, 1999). For a 40-km thick crust with lowto-moderate surface heat flow (35-60 mW m À2 ), geothermal modeling gives maximum of 50-70 K and 100-130 K uncertainties for 50 km and 100 km depth, respectively (Artemieva and Mooney, 2001).…”

Section: Uncertainty Analysismentioning

confidence: 99%

Lithospheric thermal structure and rheology of the eastern China

Wang

Cheng

2012

Journal of Asian Earth Sciences

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“…Thermal conductivity varies usually between values of 2 and 4 W m −1 K −1 while heat production can vary between several decades (Kukkonen, 1998) from the low values in the mantle to the high values in the upper crust. The combined effect when both parameters vary can result in a similar amount of change in the temperature at the depth of 50 km as caused by the change in the surface HFD by ±10 mW m −2 (Jokinen and Kukkonen, 1999).…”

Section: Uncertainty Analysismentioning

confidence: 99%

The present day rheology, stress field and deformation along the DSS profile FENNIA in the central Fennoscandian Shield

Moisio

Kaikkonen

2004

Journal of Geodynamics

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Random modelling of the lithospheric thermal regime: forward simulations applied in uncertainty analysis

Cited by 35 publications

References 14 publications

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

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

Lithospheric thermal structure and rheology of the eastern China

The present day rheology, stress field and deformation along the DSS profile FENNIA in the central Fennoscandian Shield

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