『Abstract
Detrital low temperature thermochronometric data provides spatial
and temporal information on catchment erosion, which is relevant
to problems in climate, tectonics and geomorphology. However,
direct inference of erosion rates from such data is not trivial
and only the simplest inverse problems have been addressed previously.
In this paper, we present a new approach that relies on the Bayesian
interpretation of probability and uses a Markov chain Monte Carlo
algorithm for inversion, which affords flexibility in the choice
of specific model parametrization and transparent assessment of
model uncertainty. We demonstrate how a single detrital sample
sourced from a high relief catchment can constrain long-term (＞106
years) changes in erosion rate that are in good agreement with
published bedrock age-elevation profiles. Furthermore, we use
detrital data to jointly invert for long-term exhumation history
and spatial variability in short-term (＜103 years)
sediment supply, information relevant to many geomorphic studies.
Where cooling histories are simple, we show that even small sample
sizes (＜20 grams) reliably estimate long-term rates of exhumation.
We suggest that the presented approach to modeling detrital low-temperature
thermochronometric data is both a powerful and efficient tool
for solving tectonic and geomorphic problems.
Keywords: detrital thermochronometry; inverse modeling; Shillong
Plateau; Sierra Nevada; erosion exhumation
』
1. Introduction
2. Detrital thermochronometric age model
3. Determining erosion history from detrital data: Shillong Plateau
example
3.1. Discussion of the Shilllong Plateau modeling results
4. Quantifying spatially variable erosion: Sierra Nevada example
4.1. Simultaneous estimation of spatial and temporal patterns
of erosion
4.2. Discussion of the Sierra Nevada modeling results
5. How many grains are needed for an erosional study
6. Future directions
7. Conclusions
Acknowledgments
Appendix A. Notation
Appendix B. Bayesian methodology
Appendix C. Sample collection and analytical procedures
Appendix D. Supplementary data
References