Return to search

Uncertainty analysis and inversion of geothermal conductive models using random simulation methods

Abstract
Knowledge of the thermal conditions in the lithosphere is
based on theoretical models of heat transfer constrained by geological
and geophysical data. The present dissertation focuses on the uncertainties of
calculated temperature and heat flow density results and on how
they depend on the uncertainties of thermal properties of rocks,
as well as on the relevant boundary conditions. Due to the high
number of involved variables of typical models, the random simulation
technique was chosen as the applied tool in the analysis. Further,
the random simulation technique was applied in inverse Monte Carlo solutions
of geothermal models. In addition to modelling technique development,
new measurements on thermal conductivity and diffusivity of middle
and lower crustal rocks in elevated pressure and temperature were
carried out.



In the uncertainty analysis it was found that a temperature
uncertainty of 50 K at the Moho level, which is at a 50 km's
depth in the layered model, is produced by an uncertainty of only
0.5 W m-1 K-1 in
thermal conductivity values or 0.2 orders of magnitude uncertainty
in heat production rate (mW m-3). Similar
uncertainties are obtained in Moho temperature, given that the lower
boundary condition varies by ± 115 K in temperature (nominal
value 1373 K) or ± 1.7 mW m-2 in
mantle heat-flow density (nominal value 13.2 mW m-2).
Temperature and pressure dependencies of thermal conductivity are
minor in comparison to the previous effects.



The inversion results indicated that the Monte Carlo technique
is a powerful tool in geothermal modelling. When only surface heat-flow
density data are used as a fitting object, temperatures at the depth
of 200 km can be inverted with an uncertainty of 120 - 170 K. When
petrological temperature-depth (pressure) data on kimberlite-hosted
mantle xenoliths were used also as a fitting object, the uncertainty
was reduced to 60 - 130 K. The inversion does not remove the ambiguity
of the models completely, but it reduces significantly the uncertainty
of the temperature results.

Identiferoai:union.ndltd.org:oulo.fi/oai:oulu.fi:isbn951-42-5590-9
Date31 March 2000
CreatorsJokinen, J. (Jarkko)
PublisherUniversity of Oulu
Source SetsUniversity of Oulu
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/doctoralThesis, info:eu-repo/semantics/publishedVersion
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess, © University of Oulu, 2000
Relationinfo:eu-repo/semantics/altIdentifier/pissn/0355-3191, info:eu-repo/semantics/altIdentifier/eissn/1796-220X

Page generated in 0.0021 seconds