Constraining Absolute Plate Motions Since the Triassic
Abstract
The absolute motion of tectonic plates since Pangea can be derived from observations of hotspot trails, paleomagnetism, or seismic tomography. However, fitting observations is typically carried out in isolation without consideration for the fit to unused data or whether the resulting plate motions are geodynamically plausible. Through the joint evaluation of global hotspot track observations (for times <80 Ma), first‐order estimates of net lithospheric rotation (NLR), and parameter estimation for paleo–trench migration (TM), we present a suite of geodynamically consistent, data‐optimized global absolute reference frames from 220 Ma to the present. Each absolute plate motion (APM) model was evaluated against six published APM models, together incorporating the full range of primary data constraints. Model performance for published and new models was quantified through a standard statistical analyses using three key diagnostic global metrics: root‐mean square plate velocities, NLR characteristics, and TM behavior. Additionally, models were assessed for consistency with published global paleomagnetic data and for ages <80 Ma for predicted relative hotspot motion, track geometry, and time dependence. Optimized APM models demonstrated significantly improved global fit with geological and geophysical observations while performing consistently with geodynamic constraints. Critically, APM models derived by limiting average rates of NLR to ~0.05°/Myr and absolute TM velocities to ~27‐mm/year fit geological observations including hotspot tracks. This suggests that this range of NLR and TM estimates may be appropriate for Earth over the last 220 Myr, providing a key step toward the practical integration of numerical geodynamics into plate tectonic reconstructions.
Additional Information
© 2019 American Geophysical Union. Received 29 JAN 2019; Accepted 31 MAY 2019; Accepted article online 7 JUN 2019; Published online 6 JUL 2019. This research was supported by the Science Industry Endowment Fund (RP 04‐174) Big Data Knowledge Discovery Project together with Australian Research Council Grants IH130200012 and DE160101020. M. G. T. received additional support from a CSIRO‐Data 61 Postgraduate Scholarship. M. G. was supported by the National Science Foundation (EAR‐1645775). The authors would like the thank the two anonymous reviewers for their careful and constructive comments that served to improve the paper, together with Sabin Zahirovic and John Cannon for their combined technical help and expertise to make this research possible. This is a contribution to IGCP project 648. Analyses were conducted using the following open source tools: GPlates and pyGPlates (www.gplates.org), Python (www.python.org), and Project Jupyter (www.jupyter.org). The digital files associated with this analysis and for the plate reconstruction using the GPlates software are archived online (doi:10.5281/zenodo.2638121).Attached Files
Published - Tetley_et_al-2019-Journal_of_Geophysical_Research__Solid_Earth.pdf
Supplemental Material - jgrb53537-sup-0001-2019jb017442-ts01.xlsx
Supplemental Material - jgrb53537-sup-0002-2019jb017442si-s01.pdf
Supplemental Material - jgrb53537-sup-0003-2019jb017442-ts02.xlsx
Supplemental Material - jgrb53537-sup-0004-2019jb017442-ts03.xlsx
Supplemental Material - jgrb53537-sup-0005-2019jb017442-ds01.zip
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Additional details
- Eprint ID
- 98429
- Resolver ID
- CaltechAUTHORS:20190905-101558184
- Science and Industry Endowment Fund
- RP 04‐174
- Australian Research Council
- IH130200012
- Australian Research Council
- DE160101020
- Commonwealth Scientific and Research Organization (CSIRO)
- NSF
- EAR‐1645775
- Created
-
2019-09-05Created from EPrint's datestamp field
- Updated
-
2021-11-16Created from EPrint's last_modified field
- Caltech groups
- Seismological Laboratory