2 Magma, Heat, Fluids, and Critical Resources Across Tectonic Settings, Lithospheric Realms, Volcanic Systems, and Geologic Timescales I

Monday, 15 April 2024
8:00 AM - 12:00 PM
Windsor Ballroom Salon A

DEVELOPING A PYTHON-BASED WORKFLOW FOR ANALYZING ASPECT MODELS OF DEEP MELT GENERATION

Paper number: 2-4
Program: Theme Sessions
Time: 8:45 AM – 9:05 AM

Authors

Abstract

PyGMT is a Python wrapper for Generic Mapping Tools (GMT), which allows users to utilize GMT features within a Python-based interface, like a Jupyter Notebook. This approach can reduce the number of files needed to interpret results because post-processing and visualization can be done with the same file in a single interface. In this work, we develop a Python-based workflow to analyze the output of geodynamic models from the Advanced Solver for Problems in Earth’s Convection (ASPECT) community code. In this work, we are using ASPECT to test the influence of sublithospheric (deep) melt on rifting in part of the East African Rift System. The presence of deep melt in the sublithospheric mantle could provide insight into the formation of magma-rich and magma-poor rift systems in the Northern Western Branch of the East African Rift System because melt can weaken the lithosphere, enabling the splitting of tectonic plates. Currently, seismic velocity models indicate there are no major plumes in the upper mantle beneath the Northern Western Branch, therefore we test a model of lithospheric modulated convection and deep melt generation for this region. We use three different lithospheric thickness models, Afonso, Fishwick, and LITHO1.0 as inputs to ASPECT to simulate upper mantle convection and the generation of deep melt in the sublithospheric mantle for a range of initial temperature conditions. We then use our new Python-based workflow to generate visualizations and analyses of deep melt. We assess the distribution of total melt fraction generated in the region using PyGMT and found that in magma-poor rifting areas, melt was not generated for all scenarios at any depth. These results suggest another mechanism is required to facilitate rifting, such as pre-existing weaknesses. Leveraging PyGMT in a Jupyter Notebook allows for a more efficient way of interpreting experimental results.

Geological Society of America Abstracts with Programs. Vol. 56, No. 2

doi: 10.1130/abs/2024SE-398313

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