V042-08
3D thermokinematic modeling of a large thermochronologic dataset from the Pyrenees Mountains: Strategies and lessons of using Pecube in inversion mode
3D thermokinematic modeling of a large thermochronologic dataset from the Pyrenees Mountains: Strategies and lessons of using Pecube in inversion mode
Wednesday, 16 December 2020: 10:21
Virtual
Abstract:
We present our approach to 3D thermo-kinematic modeling using the code Pecube to investigate orogen-scale exhumation of the Pyrenees mountains. The Pyrenees comprise a small orogen that formed during Late-Cretaceous to Early-Miocene convergence between the European and Iberian plates. Using Pecube version 3 in inversion mode on a computing cluster, we ran large inversions to determine the spatio-temporal variation in uplift rate that best predicts a large thermochronology dataset. To construct the misfit function for the inversion, we compiled 264 low-temperature thermochronometer cooling ages spanning ~200 km of the Pyrenees mountains. These data, which include AHe, AFT, ZHe, and ZFT cooling ages, collectively record cooling from ~200°C to ~60°C. Our approach includes vertical uplift and independently established topographic changes providing surface boundary constraints. We implemented one change to the source code to mimic sediment draping and excavation in the region. The large spatial- and temporal-scale approach we implement here facilitates investigation into the rates, patterns, and drivers of orogen-wide exhumation within a regionally consistent framework. By providing large-scale independent constraints on spatio-temporal patterns of exhumation, our results supply important additions to landscape evolution models and can provide testable predictions for detrital-thermochronology studies. We will present major lessons learned and hurdles faced in the course of building and evaluating our models. We will also show examples integrating Pecube with the landscape evolution model Fastscape and comparisons between predicted and observed detrital thermochronology distributions.