Abstract
Thermochronometry is widely used to track exhumation, the motion of rock towards Earth’s surface, and to gain fresh insights into geodynamic and geomorphic processes. Applications require models to reconstruct a rock’s cooling history as it is exhumed from higher temperatures at depth within the crust to cooler shallower levels and eventually Earth’s surface. Thermochronometric models are dependent on the predictable accumulation and the temperature-dependent loss of radiogenic daughter products measured in the laboratory. However, there are many geologically reasonable scenarios that will yield very similar thermochronometric ages. This similarity hinders finding the actual scenario, so instead an approximate model is sought. Finding suitable model parameters is a potentially ill-posed inverse problem that requires making decisions about how best to extract information from the data and how to combine data to leverage redundant information and reduce the impact of data noise. Often these decisions lead to differences in conclusions of studies and such discrepancies have led to heated debates. Here, we discuss debates centred on the use of a variety of modelling approaches and potential sources of biases that lead to differences in the predicted exhumation rate. We also provide some suggestions about future research paths that will help resolve these debates.
Highlights
Challenges associated with the design, implementation, interpretation and appraisal of these types of inverse models have resulted in long-standing debates within the thermochronology community regarding various geologic problems
It is unclear whether mountain belt erosion rates increased over the course of the Neogene or whether this is an artefact of different inverse modelling methodologies [3,4,5,6,7]
We have attempted to provide an overview of some of the debates that are currently unresolved within thermochronometry
Summary
Whereas a forward model, or simulation, has a single solution for a combination of model parameters, there may be many other combinations of model parameters that provide the same, or a very similar, solution, even for perfect, noise-free data These sorts of inverse problems are used extensively across the Earth Sciences from large-scale seismic tomographic inversions with thousands of model parameters describing the velocity structure of the Earth [1] to two-parameter linear regressions through geomorphic datasets with just a slope and intercept [2]. Challenges associated with the design, implementation, interpretation and appraisal of these types of inverse models have resulted in long-standing debates within the thermochronology community regarding various geologic problems It is unclear whether mountain belt erosion rates increased over the course of the Neogene or whether this is an artefact of different inverse modelling methodologies [3,4,5,6,7]. Our goal is to simplify these discussions and highlight solutions and practical ways forward
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