Articles | Volume 3, issue 1
https://doi.org/10.5194/gchron-3-181-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gchron-3-181-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Apr 2021
Research article |
| 16 Apr 2021
| 16 Apr 2021
An evaluation of Deccan Traps eruption rates using geochronologic data
Department of Geosciences, Princeton University, Princeton, NJ 08544, USA
Michael P. Eddy
Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA
C. Brenhin Keller
Department of Earth Sciences, Dartmouth College, Hanover, NH 03755, USA
Kyle M. Samperton
Nuclear and Chemical Sciences Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
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- Comments on: “Machine learning-based re-classification of the geochemical stratigraphy of the Rajahmundry Traps, India” by Hoyer, et al.: J. Volc.Geother.Res. v. 428, no: 107594 V. Kale & K. Pande 10.1016/j.jvolgeores.2022.107737
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Discussed (final revised paper)
Latest update: 13 Dec 2024
Short summary
We compare two published U–Pb and 40Ar / 39Ar geochronologic datasets to produce eruption rate models for the Deccan Traps large igneous province. Applying the same approach to each dataset, the resulting models agree well, but the higher-precision U–Pb dataset results in a more detailed eruption model than the lower-precision 40Ar / 39Ar data. We explore sources of geologic uncertainty and reiterate the importance of systematic uncertainties in comparing U–Pb and 40Ar / 39Ar datasets.
We compare two published U–Pb and 40Ar / 39Ar geochronologic datasets to produce eruption rate...
