Towards large-scale low-temperature thermochronological data inversion: an assessment using a dataset from Madagascar

Over the past 30 years, the number of low-temperature thermochronological data has grown, driven by advances in analytical techniques and the proliferation of studies. While generating and sharing these data with the scientific community presents the initial challenge, a second appears during their interpretation through inverse modelling. While performing joint data inversion on few samples is common, scaling this process to larger datasets (>50 samples) remains rare.

In recent years, several research teams have addressed the data sharing challenge by standardizing data-sharing formats (Flowers et al., 2023b, 2023a) and developing dedicated platforms for low-temperature thermochronological data (lithodat.com). Here, we test a "large data-set inversion" approach using a large dataset from Madagascar.

Madagascar has been the focus of over 10 studies since the 1990s, producing a dataset of ~250 samples analysed using two methods ((U-Th)/He and fission track) across various minerals, including apatite and zircon. In this study, we exploited available data (201 AFT and 87 AHe) in a large-scale inversion using a preliminary spatial clustering version of the Bayesian thermal history modelling software, QTQt. The clustering approach follows that presented in Stephenson et al. (2006) but allows for trans-dimensional thermal history models. The approach tries to determine both the number of clusters (i.e. sample groupings) and the thermal histories in each cluster that can reproduce the observed data.

We present (very) preliminary results of this approach applied to the Madagascar dataset, that divide the data in 3 clusters. Although run for 6 weeks, we managed to do just a small number of iterations (<100), and the algorithm was not converged. The inferred 3 clusters are compared to Madagascar’s known tectono-morphological blocks, and the inferred time-temperature paths can then be tentatively assigned to these blocks, potentially offering new insights into the associated vertical dynamics of the island.

 

Flowers, R.M., Ketcham, R.A., Enkelmann, E., Gautheron, C., Reiners, P.W., Metcalf, J.R., Danišík, M., Stockli, D.F., Brown, R.W., 2023a. (U-Th)/He chronology: Part 2. Considerations for evaluating, integrating, and interpreting conventional individual aliquot data. GSA Bulletin 135, 137–161. https://doi.org/10.1130/B36268.1

Flowers, R.M., Zeitler, P.K., Danišík, M., Reiners, P.W., Gautheron, C., Ketcham, R.A., Metcalf, J.R., Stockli, D.F., Enkelmann, E., Brown, R.W., 2023b. (U-Th)/He chronology: Part 1. Data, uncertainty, and reporting. GSA Bulletin 135, 104–136. https://doi.org/10.1130/B36266.1

Stephenson, J., Gallagher, K., & Holmes, C. (2006). A Bayesian approach to calibrating apatite fission track annealing models for laboratory and geological timescales. Geochimica et Cosmochimica Acta, 70(20), 5183-5200.