A Bayesian Reappraisal of Australian Crustal Heat Flow and Temperature
type: CreativeWork
description: Geothermal data offers a unique perspective from which to image the Earth. However, geothermal data is difficult to collect and this has necessitated a reliance on industrial data collection during the exploration for mineral and petroleum resources. Resulting data quality issues have limited previous studies in their ability map the information contained in the data into robust model inferences. In this thesis I employ a Bayesian statistical framework to address these issues, predominantly through the Bayesian re-appraisal of Australian crustal heat flow and temperature data. This has been achieved by defining sensitivity kernels for surface heat flow determinations, examining the sources and magnitudes of noise in primary geothermal data sets, and developing methods to assign petrophysical rock property a priori distributions with spatial variability scaled appropriate to the length-scales of modelling. I combine these factors and use them to infer the surface heat flow field across Australia. This demonstrates the existence of a relationship between surface heat flow determinations and lithospheric thickness. Remote sensing and Argo sea float data have been used to derive a national land-surface temperature coverage, providing the basis for detailed crustal temperature modelling. This is supported by a new framework for assessing the uncertainties inherent in bottom-hole temperature measurements, enabling us to leverage vast industrial data sets to model crustal thermal fields. The significance of these outcomes is that they advances us on the pathway towards genuine joint-inversion involving geothermal data. The quantification of uncertainty distributions on geothermal data sets provides the means to calculate the degree with which a given earth model is consistent with available geothermal data. In doing so, this thesis establishes the basis for future data integration projects to build detailed models of Australian crustal structure and composition, and throug…
abstract: Geothermal data offers a unique perspective from which to image the Earth. However, geothermal data is difficult to collect and this has necessitated a reliance on industrial data collection during the exploration for mineral and petroleum resources. Resulting data quality issues have limited previous studies in their ability map the information contained in the data into robust model inferences. In this thesis I employ a Bayesian statistical framework to address these issues, predominantly through the Bayesian re-appraisal of Australian crustal heat flow and temperature data. This has been achieved by defining sensitivity kernels for surface heat flow determinations, examining the sources and magnitudes of noise in primary geothermal data sets, and developing methods to assign petrophysical rock property a priori distributions with spatial variability scaled appropriate to the length-scales of modelling. I combine these factors and use them to infer the surface heat flow field across Australia. This demonstrates the existence of a relationship between surface heat flow determinations and lithospheric thickness. Remote sensing and Argo sea float data have been used to derive a national land-surface temperature coverage, providing the basis for detailed crustal temperature modelling. This is supported by a new framework for assessing the uncertainties inherent in bottom-hole temperature measurements, enabling us to leverage vast industrial data sets to model crustal thermal fields. The significance of these outcomes is that they advances us on the pathway towards genuine joint-inversion involving geothermal data. The quantification of uncertainty distributions on geothermal data sets provides the means to calculate the degree with which a given earth model is consistent with available geothermal data. In doing so, this thesis establishes the basis for future data integration projects to build detailed models of Australian crustal structure and composition, and throug…
author: Haynes, Marcus
language: en_AU
date published: 2021
date modified: 2026-07-24T01:07:43Z
provider: Australian Catholic University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Australian Catholic University

PropertyValue
property ID: Handle
value: http://hdl.handle.net/1885/236902