An autonomous petrological database for geodynamic simulations of magmatic systems. Issue 3 (1st September 2020)

• Record Type: Journal Article
• Authors: Rummel, Lisa; Baumann, Tobias S; Kaus, Boris J P
• Is Part Of: Geophysical journal international. Volume 223:Issue 3(2020)
• Page Start: 1820
• Page End: 1836
• Publication Date: 2020-09-01
• Subjects: Composition and structure of the continental crust -- Numerical modelling -- Persistence, memory, correlations, clustering -- Probability distributions -- Magma chamber processes; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggaa413
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: Self-consistent modelling of magmatic systems is challenging as the melt continuously changes its chemical composition upon crystallization, which may affect the mechanical behaviour of the system. Melt extraction and subsequent crystallization create new rocks while depleting the source region. As the chemistry of the source rocks changes locally due to melt extraction, new calculations of the stable phase assemblages are required to track the rock evolution and the accompanied change in density. As a consequence, a large number of isochemical sections of stable phase assemblages are required to study the evolution of magmatic systems in detail. As the state-of-the-art melting diagrams may depend on nine oxides as well as pressure and temperature, this is a 10-D computational problem. Since computing a single isochemical section (as a function of pressure and temperature) may take several hours, computing new sections of stable phase assemblages during an ongoing geodynamic simulation is currently computationally intractable. One strategy to avoid this problem is to pre-compute these stable phase assemblages and to create a comprehensive database as a hyperdimensional phase diagram, which contains all bulk compositions that may emerge during petro-thermomechanical simulations. Establishing such a database would require repeating geodynamic simulations many times while collecting all requested compositions that may occur during a typical simulation and continuously updating the database until no additional compositions are required. Here, we describe an alternative method that is better suited for implementation on large-scale parallel computers. Our method uses the entries of an existing preliminary database to estimate future required chemical compositions. Bulk compositions are determined within boundaries that are defined manually or through principal component analysis in a parameter space consisting of clustered database entries. We have implemented both methods within a massively parallel computational framework while utilizing the Gibbs free energy minimization program Perple_X. Results show that our autonomous approach increases the resolution of the thermodynamic database in compositional regions that are most likely required for geodynamic models of magmatic systems.