Articles | Volume 17, issue 16
https://doi.org/10.5194/gmd-17-6105-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-6105-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
16 Aug 2024
Development and technical paper |
| 16 Aug 2024
| 16 Aug 2024
Modelling chemical advection during magma ascent
Institute of Geological Sciences, University of Bern, Baltzerstrasse 3, 3012 Bern, Switzerland
Nicolas Riel
Institute of Geosciences, Johannes Gutenberg University, 55099 Mainz, Germany
Pierre Lanari
Institute of Geological Sciences, University of Bern, Baltzerstrasse 3, 3012 Bern, Switzerland
Institute of Earth Sciences, University of Lausanne, Géopolis, 1015 Lausanne, Switzerland
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Nicolas Riel, Boris J. P. Kaus, Albert de Montserrat, Evangelos Moulas, Eleanor C. R. Green, and Hugo Dominguez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-197, https://doi.org/10.5194/gmd-2024-197, 2024
Revised manuscript accepted for GMD
Short summary
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Our research focuses on improving the way we predict mineral assemblage. Current methods, while accurate, are slowed by complex calculations. We developed a new approach that simplifies these calculations and speeds them up significantly using a technique called the BFGS algorithm. This breakthrough reduces computation time by more than five times, potentially unlocking new horizons in modeling reactive magmatic systems.
Nicolas Riel, Boris J. P. Kaus, Albert de Montserrat, Evangelos Moulas, Eleanor C. R. Green, and Hugo Dominguez
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-197, https://doi.org/10.5194/gmd-2024-197, 2024
Revised manuscript accepted for GMD
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Our research focuses on improving the way we predict mineral assemblage. Current methods, while accurate, are slowed by complex calculations. We developed a new approach that simplifies these calculations and speeds them up significantly using a technique called the BFGS algorithm. This breakthrough reduces computation time by more than five times, potentially unlocking new horizons in modeling reactive magmatic systems.
Kilian Lecacheur, Olivier Fabbri, Francesca Piccoli, Pierre Lanari, Philippe Goncalves, and Henri Leclère
Eur. J. Mineral., 36, 767–795, https://doi.org/10.5194/ejm-36-767-2024, https://doi.org/10.5194/ejm-36-767-2024, 2024
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In this study, we analyze a peculiar eclogite from the Western Alps, which not only recorded a classical subduction-to-exhumation path but revealed evidence of Ca-rich fluid–rock interaction. Chemical composition and modeling show that the rock experienced peak metamorphic conditions followed by Ca-rich pulsed fluid influx occurring consistently under high-pressure conditions. This research enhances our understanding of fluid–rock interactions in subduction settings.
Julien Reynes, Jörg Hermann, Pierre Lanari, and Thomas Bovay
Eur. J. Mineral., 35, 679–701, https://doi.org/10.5194/ejm-35-679-2023, https://doi.org/10.5194/ejm-35-679-2023, 2023
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Garnet is a high-pressure mineral that may incorporate very small amounts of water in its structure (tens to hundreds of micrograms per gram H2O). In this study, we show, based on analysis and modelling, that it can transport up to several hundred micrograms per gram of H2O at depths over 80 km in a subduction zone. The analysis of garnet from the various rock types present in a subducted slab allowed us to estimate the contribution of garnet in the deep cycling of water in the earth.
Veronica Peverelli, Alfons Berger, Martin Wille, Thomas Pettke, Pierre Lanari, Igor Maria Villa, and Marco Herwegh
Solid Earth, 13, 1803–1821, https://doi.org/10.5194/se-13-1803-2022, https://doi.org/10.5194/se-13-1803-2022, 2022
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This work studies the interplay of epidote dissolution–precipitation and quartz dynamic recrystallization during viscous granular flow in a deforming epidote–quartz vein. Pb and Sr isotope data indicate that epidote dissolution–precipitation is mediated by internal/recycled fluids with an additional external fluid component. Microstructures and geochemical data show that the epidote material is redistributed and chemically homogenized within the deforming vein via a dynamic granular fluid pump.
Veronica Peverelli, Tanya Ewing, Daniela Rubatto, Martin Wille, Alfons Berger, Igor Maria Villa, Pierre Lanari, Thomas Pettke, and Marco Herwegh
Geochronology, 3, 123–147, https://doi.org/10.5194/gchron-3-123-2021, https://doi.org/10.5194/gchron-3-123-2021, 2021
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This work presents LA-ICP-MS U–Pb geochronology of epidote in hydrothermal veins. The challenges of epidote dating are addressed, and a protocol is proposed allowing us to obtain epidote U–Pb ages with a precision as good as 5 % in addition to the initial Pb isotopic composition of the epidote-forming fluid. Epidote demonstrates its potential to be used as a U–Pb geochronometer and as a fluid tracer, allowing us to reconstruct the timing of hydrothermal activity and the origin of the fluid(s).
Felix Hentschel, Emilie Janots, Claudia A. Trepmann, Valerie Magnin, and Pierre Lanari
Eur. J. Mineral., 32, 521–544, https://doi.org/10.5194/ejm-32-521-2020, https://doi.org/10.5194/ejm-32-521-2020, 2020
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We analysed apatite–allanite/epidote coronae around monazite and xenotime in deformed Permian pegmatites from the Austroalpine basement. Microscopy, chemical analysis and EBSD showed that these coronae formed by dissolution–precipitation processes during deformation of the host rocks. Dating of monazite and xenotime confirmed the magmatic origin of the corona cores, while LA-ICP-MS dating of allanite established a date of ~ 60 Ma for corona formation and deformation in the Austroalpine basement.
Cited articles
Aharonov, E., Whitehead, J. A., Kelemen, P. B., and Spiegelman, M.: Channeling Instability of Upwelling Melt in the Mantle, J. Geophys. Res.-Sol. Ea., 100, 20433–20450, https://doi.org/10.1029/95JB01307, 1995a. a
Aharonov, E., Whitehead, J. A., Kelemen, P. B., and Spiegelman, M.: Channeling Instability of Upwelling Melt in the Mantle, J. Geophys. Res.-Sol. Ea., 100, 20433–20450, https://doi.org/10.1029/95JB01307, 1995b. a
Aharonov, E., Spiegelman, M., and Kelemen, P.: Three-Dimensional Flow and Reaction in Porous Media: Implications for the Earth's Mantle and Sedimentary Basins, J. Geophys. Res.-Sol. Ea., 102, 14821–14833, https://doi.org/10.1029/97JB00996, 1997. a, b
Aitchison, J.: The Statistical Analysis of Compositional Data, J. Roy. Stat. Soc. B, 44, 139–160, https://doi.org/10.1111/j.2517-6161.1982.tb01195.x, 1982. a
Bank, R., Coughran, W., Fichtner, W., Grosse, E., Rose, D., and Smith, R.: Transient Simulation of Silicon Devices and Circuits, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 4, 436–451, https://doi.org/10.1109/TCAD.1985.1270142, 1985. a
Barcilon, V. and Lovera, O. M.: Solitary Waves in Magma Dynamics, J. Fluid Mech., 204, 121, https://doi.org/10.1017/S0022112089001680, 1989. a
Bercovici, D., Ricard, Y., and Schubert, G.: A Two-Phase Model for Compaction and Damage: 1. General Theory, J. Geophys. Res.-Sol. Ea., 106, 8887–8906, https://doi.org/10.1029/2000JB900430, 2001. a
Bermejo, R.: Analysis of a Class of Quasi-Monotone and Conservative Semi-Lagrangian Advection Schemes, Numerische Mathematik, 87, 597–623, https://doi.org/10.1007/PL00005425, 2001. a, b
Bermejo, R. and Staniforth, A.: The Conversion of Semi-Lagrangian Advection Schemes to Quasi-Monotone Schemes, Mon. Weather Rev., 120, 2622–2632, https://doi.org/10.1175/1520-0493(1992)120<2622:TCOSLA>2.0.CO;2, 1992. a, b
Bessat, A., Pilet, S., Podladchikov, Y. Y., and Schmalholz, S. M.: Melt Migration and Chemical Differentiation by Reactive Porosity Waves, Geochem. Geophys. Geosyst., 23, e2021GC009963, https://doi.org/10.1029/2021GC009963, 2022. a
Bouilhol, P., Connolly, J. A., and Burg, J.-P.: Geological Evidence and Modeling of Melt Migration by Porosity Waves in the Sub-Arc Mantle of Kohistan (Pakistan), Geology, 39, 1091–1094, https://doi.org/10.1130/G32219.1, 2011. a
Brown, M.: Granite: From Genesis to Emplacement, Geol. Soc. Am. Bull., 125, 1079–1113, https://doi.org/10.1130/B30877.1, 2013. a
Carman, P. C.: Permeability of Saturated Sands, Soils and Clays, J. Agric. Sci., 29, 262–273, https://doi.org/10.1017/S0021859600051789, 1939. a
Carrera, J., Saaltink, M. W., Soler-Sagarra, J., Wang, J., and Valhondo, C.: Reactive Transport: A Review of Basic Concepts with Emphasis on Biochemical Processes, Energies, 15, 925, https://doi.org/10.3390/en15030925, 2022. a
Chandrasekar, A.: Numerical Methods for Atmospheric and Oceanic Sciences, Cambridge University Press, 1st edn., ISBN 978-1-00-911923-8 978-1-00-910056-4, https://doi.org/10.1017/9781009119238, 2022. a, b
Clemens, J. D., Bryan, S. E., Mayne, M. J., Stevens, G., and Petford, N.: How Are Silicic Volcanic and Plutonic Systems Related? Part 1: A Review of Geological and Geophysical Observations, and Insights from Igneous Rock Chemistry, Earth-Sci. Rev., 235, 104249, https://doi.org/10.1016/j.earscirev.2022.104249, 2022. a
Connolly, J. A. D. and Podladchikov, Y. Y.: Decompaction Weakening and Channeling Instability in Ductile Porous Media: Implications for Asthenospheric Melt Segregation, J. Geophys. Res., 112, B10205, https://doi.org/10.1029/2005JB004213, 2007a. a, b
Connolly, J. A. D. and Podladchikov, Y. Y.: Decompaction Weakening and Channeling Instability in Ductile Porous Media: Implications for Asthenospheric Melt Segregation, J. Geophys. Res., 112, B10205, https://doi.org/10.1029/2005JB004213, 2007b. a, b
Connolly, J. A. D. and Podladchikov, Yu. Yu.: Compaction-Driven Fluid Flow in Viscoelastic Rock, Geodinam. Acta, 11, 55–84, https://doi.org/10.1080/09853111.1998.11105311, 1998. a
Costa, A.: Permeability-Porosity Relationship: A Reexamination of the Kozeny-Carman Equation Based on a Fractal Pore-Space Geometry Assumption, Geophys. Res. Lett., 33, L02318, https://doi.org/10.1029/2005GL025134, 2006. a
Courant, R., Isaacson, E., and Rees, M.: On the Solution of Nonlinear Hyperbolic Differential Equations by Finite Differences, Commun, Pure Appl. Math., 5, 243–255, https://doi.org/10.1002/cpa.3160050303, 1952. a
Dominguez, H.: neoscalc/ChemicalAdvectionPorosityWave.jl: ChemicalAdvectionPorosityWave.jl 1.0.0 (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.8411354, 2024a. a
Dominguez, H.: Data produced and used in the publication “Modelling chemical advection during magma ascent” by Dominguez et al., 2024, Zenodo [data set], https://doi.org/10.5281/zenodo.13306073, 2024b. a
Duretz, T., May, D. A., Gerya, T. V., and Tackley, P. J.: Discretization Errors and Free Surface Stabilization in the Finite Difference and Marker-in-Cell Method for Applied Geodynamics: A Numerical Study, Geochem. Geophys. Geosyst., 12, 7, https://doi.org/10.1029/2011GC003567, 2011. a, b
Filbet, F. and Prouveur, C.: High Order Time Discretization for Backward Semi-Lagrangian Methods, J. Comput. Appl. Math., 303, 171–188, https://doi.org/10.1016/j.cam.2016.01.024, 2016. a
Gerya, T. V. and Yuen, D. A.: Characteristics-Based Marker-in-Cell Method with Conservative Finite-Differences Schemes for Modeling Geological Flows with Strongly Variable Transport Properties, Phys. Earth Planet. In., 140, 293–318, https://doi.org/10.1016/j.pepi.2003.09.006, 2003a. a
Gerya, T. V. and Yuen, D. A.: Characteristics-Based Marker-in-Cell Method with Conservative Finite-Differences Schemes for Modeling Geological Flows with Strongly Variable Transport Properties, Phys. Earth Planet. In., 140, 293–318, https://doi.org/10.1016/j.pepi.2003.09.006, 2003b. a
Ghosh, D. and Baeder, J. D.: Compact Reconstruction Schemes with Weighted ENO Limiting for Hyperbolic Conservation Laws, SIAM J. Sci. Comput., 34, A1678–A1706, https://doi.org/10.1137/110857659, 2012. a
Giordano, D. and Dingwell, D. B.: Non-Arrhenian Multicomponent Melt Viscosity: A Model, Earth Planet. Sc. Lett., 208, 337–349, https://doi.org/10.1016/S0012-821X(03)00042-6, 2003. a
Godunov, S. K. and Bohachevsky, I.: Finite Difference Method for Numerical Computation of Discontinuous Solutions of the Equations of Fluid Dynamics, Matematičeskij sbornik, 47, 271–306, 1959. a
Gottlieb, S., Shu, C.-W., and Tadmor, E.: Strong Stability-Preserving High-Order Time Discretization Methods, SIAM Review, 43, 89–112, https://doi.org/10.1137/S003614450036757X, 2001. a
Grasso, F. and Pirozzoli, S.: Shock-Wave-Vortex Interactions: Shock and Vortex Deformations, and Sound Production, Theor. Comput. Fluid Dynam., 13, 421–456, https://doi.org/10.1007/s001620050121, 2000. a
Harlow, F. H., Evans, M., and Richtmyer, R. D.: A Machine Calculation Method for Hydrodynamic Problems, Los Alamos Scientific Laboratory of the University of California, 1955. a
Harten, A., Engquist, B., Osher, S., and Chakravarthy, S. R.: Uniformly High Order Accurate Essentially Non-oscillatory Schemes, III, in: Upwind and High-Resolution Schemes, edited by: Hussaini, M. Y., van Leer, B., and Van Rosendale, J., 218–290, Springer, Berlin, Heidelberg, ISBN 978-3-642-60543-7, https://doi.org/10.1007/978-3-642-60543-7_12, 1987. a
Hirsch, C.: Numerical Computation of Internal and External Flows: Fundamentals of Computational Fluid Dynamics, Elsevier/Butterworth-Heinemann, Oxford ; Burlington, MA, 2nd edn., ISBN 978-0-7506-6594-0, 2007. a
Jackson, M., Gallagher, K., Petford, N., and Cheadle, M.: Towards a Coupled Physical and Chemical Model for Tonalite–Trondhjemite–Granodiorite Magma Formation, Lithos, 79, 43–60, https://doi.org/10.1016/j.lithos.2004.05.004, 2005. a
Jha, K., Parmentier, E. M., and Phipps Morgan, J.: The Role of Mantle-Depletion and Melt-Retention Buoyancy in Spreading-Center Segmentation, Earth Planet. Sc. Lett., 125, 221–234, https://doi.org/10.1016/0012-821X(94)90217-8, 1994. a
Jiang, G.-S. and Peng, D.: Weighted ENO Schemes for Hamilton–Jacobi Equations, SIAM J. Sci. Comput., 21, 2126–2143, https://doi.org/10.1137/S106482759732455X, 2000. a
Jiang, G.-S. and Shu, C.-W.: Efficient Implementation of Weighted ENO Schemes, J. Comput. Phys., 126, 202–228, https://doi.org/10.1006/jcph.1996.0130, 1996. a
Johnson, T., Yakymchuk, C., and Brown, M.: Crustal Melting and Suprasolidus Phase Equilibria: From First Principles to the State-of-the-Art, Earth-Sci. Rev., 221, 103778, https://doi.org/10.1016/j.earscirev.2021.103778, 2021. a
Jordan, J. S., Hesse, M. A., and Rudge, J. F.: On Mass Transport in Porosity Waves, Earth Planet. Sc. Lett., 485, 65–78, https://doi.org/10.1016/j.epsl.2017.12.024, 2018. a, b
Katz, R. F.: Magma Dynamics with the Enthalpy Method: Benchmark Solutions and Magmatic Focusing at Mid-ocean Ridges, J. Petrol., 49, 2099–2121, https://doi.org/10.1093/petrology/egn058, 2008. a
Katz, R. F. and Weatherley, S. M.: Consequences of Mantle Heterogeneity for Melt Extraction at Mid-Ocean Ridges, Earth Planet. Sc. Lett., 335–336, 226–237, https://doi.org/10.1016/j.epsl.2012.04.042, 2012. a
Katz, R. F., Jones, D. W. R., Rudge, J. F., and Keller, T.: Physics of Melt Extraction from the Mantle: Speed and Style, Annu. Rev. Earth Planet. Sci., 50, 507–540, https://doi.org/10.1146/annurev-earth-032320-083704, 2022. a
Kelemen, P. B., Hirth, G., Shimizu, N., Spiegelman, M., and Dick, H. J.: A Review of Melt Migration Processes in the Adiabatically Upwelling Mantle beneath Oceanic Spreading Ridges, Philos. T. Roy. Soc. Lond. A, 355, 283–318, 1997. a
Keller, T.: Numerical Modeling of Magma Ascent and Emplacement in the Continental Lithosphere and Crust, PhD thesis, ETH Zurich, https://doi.org/10.3929/ETHZ-A-010192539, 2013. a
Keller, T. and Katz, R. F.: The Role of Volatiles in Reactive Melt Transport in the Asthenosphere, J. Petrol., 57, 1073–1108, https://doi.org/10.1093/petrology/egw030, 2016. a
Keller, T., May, D. A., and Kaus, B. J. P.: Numerical Modelling of Magma Dynamics Coupled to Tectonic Deformation of Lithosphere and Crust, Geophys. J. Int., 195, 1406–1442, https://doi.org/10.1093/gji/ggt306, 2013. a
LeVeque, R. J.: Finite Volume Methods for Hyperbolic Problems, Cambridge Texts in Applied Mathematics, Cambridge University Press, Cambridge, ISBN 978-0-521-00924-9, https://doi.org/10.1017/CBO9780511791253, 2002. a
Liu, X.-D., Osher, S., and Chan, T.: Weighted Essentially Non-oscillatory Schemes, J. Comput. Phys., 115, 200–212, https://doi.org/10.1006/jcph.1994.1187, 1994. a, b
McDonald, A.: Accuracy of Multiply-Upstream, Semi-Lagrangian Advective Schemes, Mon. Weather Rev., 112, 1267–1275, https://doi.org/10.1175/1520-0493(1984)112<1267:AOMUSL>2.0.CO;2, 1984. a, b
McKenzie, D.: The Generation and Compaction of Partially Molten Rock, J. Petrol., 25, 713–765, https://doi.org/10.1093/petrology/25.3.713, 1984. a, b, c
Neuville, D. R., Courtial, P., Dingwell, D. B., and Richet, P.: Thermodynamic and Rheological Properties of Rhyolite and Andesite Melts, Contrib. Mineral. Petrol., 113, 572–581, https://doi.org/10.1007/BF00698324, 1993. a
Omlin, S., Malvoisin, B., and Podladchikov, Y. Y.: Pore Fluid Extraction by Reactive Solitary Waves in 3-D: Reactive Porosity Waves, Geophys. Res. Lett., 44, 9267–9275, https://doi.org/10.1002/2017GL074293, 2017. a
Pawar, S. and San, O.: CFD Julia: A Learning Module Structuring an Introductory Course on Computational Fluid Dynamics, Fluids, 4, 159, https://doi.org/10.3390/fluids4030159, 2019. a
Pusok, A. E., Kaus, B. J. P., and Popov, A. A.: On the Quality of Velocity Interpolation Schemes for Marker-in-Cell Method and Staggered Grids, Pure Appl. Geophys., 174, 1071–1089, https://doi.org/10.1007/s00024-016-1431-8, 2017. a
Rackauckas, C. and Nie, Q.: DifferentialEquations.Jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia, J. Open Res. Softw., 5, 15, https://doi.org/10.5334/jors.151, 2017. a
Räss, L., Simon, N. S. C., and Podladchikov, Y. Y.: Spontaneous Formation of Fluid Escape Pipes from Subsurface Reservoirs, Sci. Rep., 8, 11116, https://doi.org/10.1038/s41598-018-29485-5, 2018. a
Reiner, M.: The Deborah Number, Physics Today, 17, 62–62, https://doi.org/10.1063/1.3051374, 1964. a
Revels, J., Lubin, M., and Papamarkou, T.: Forward-Mode Automatic Differentiation in Julia, arXiv:1607.07892 [cs.MS], 2016. a
Riel, N., Bouilhol, P., van Hunen, J., Cornet, J., Magni, V., Grigorova, V., and Velic, M.: Interaction between Mantle-Derived Magma and Lower Arc Crust: Quantitative Reactive Melt Flow Modelling Using STyx, Geological Society, London, Special Publications, 478, 65–87, https://doi.org/10.1144/SP478.6, 2019. a
Robert, A.: A Stable Numerical Integration Scheme for the Primitive Meteorological Equations, Atmosphere-Ocean, 19, 35–46, https://doi.org/10.1080/07055900.1981.9649098, 1981. a, b, c
Scott, D. R. and Stevenson, D. J.: Magma Solitons, Geophys. Res. Lett., 11, 1161–1164, https://doi.org/10.1029/GL011i011p01161, 1984. a
Shu, C.-W.: High Order Weighted Essentially Nonoscillatory Schemes for Convection Dominated Problems, SIAM Review, 51, 82–126, https://doi.org/10.1137/070679065, 2009. a
Smith, C. J.: The Semi-Lagrangian Method in Atmospheric Modelling, PhD thesis, 2000. a
Solano, J. M. S., Jackson, M. D., Sparks, R. S. J., Blundy, J. D., and Annen, C.: Melt Segregation in Deep Crustal Hot Zones: A Mechanism for Chemical Differentiation, Crustal Assimilation and the Formation of Evolved Magmas, J. Petrol., 53, 1999–2026, https://doi.org/10.1093/petrology/egs041, 2012. a
Sonnendrücker, E., Roche, J., Bertrand, P., and Ghizzo, A.: The Semi-Lagrangian Method for the Numerical Resolution of the Vlasov Equation, J. Comput. Phys., 149, 201–220, https://doi.org/10.1006/jcph.1998.6148, 1999. a
Spiegelman, M. and Kenyon, P.: The Requirements for Chemical Disequilibrium during Magma Migration, Earth Planet. Sc. Lett., 109, 611–620, 1992. a
Spiegelman, M., Kelemen, P. B., and Aharonov, E.: Causes and Consequences of Flow Organization during Melt Transport: The Reaction Infiltration Instability in Compactible Media, J. Geophys. Res.-Sol. Ea., 106, 2061–2077, https://doi.org/10.1029/2000JB900240, 2001. a
Staniforth, A. and Côté, J.: Semi-Lagrangian Integration Schemes for Atmospheric Models – A Review, Mon. Weather Rev., 119, 2206–2223, https://doi.org/10.1175/1520-0493(1991)119<2206:SLISFA>2.0.CO;2, 1991. a
Trim, S. J., Lowman, J. P., and Butler, S. L.: Improving Mass Conservation With the Tracer Ratio Method: Application to Thermochemical Mantle Flows, Geochem. Geophys. Geosyst., 21, e2019GC008799, https://doi.org/10.1029/2019GC008799, 2020. a
Ueda, K., Willett, S., Gerya, T., and Ruh, J.: Geomorphological–Thermo-Mechanical Modeling: Application to Orogenic Wedge Dynamics, Tectonophysics, 659, 12–30, https://doi.org/10.1016/j.tecto.2015.08.001, 2015. a
van Keken, P. E., King, S. D., Schmeling, H., Christensen, U. R., Neumeister, D., and Doin, M.-P.: A Comparison of Methods for the Modeling of Thermochemical Convection, J. Geophys. Res.-Sol. Ea., 102, 22477–22495, https://doi.org/10.1029/97JB01353, 1997. a
Vasilyev, O. V., Podladchikov, Y. Y., and Yuen, D. A.: Modeling of Compaction Driven Flow in Poro-Viscoelastic Medium Using Adaptive Wavelet Collocation Method, Geophys. Res. Lett., 25, 3239–3242, https://doi.org/10.1029/98GL52358, 1998. a
Wang, R. and Spiteri, R. J.: Linear Instability of the Fifth-Order WENO Method, SIAM J. Numer. Anal., 45, 1871–1901, https://doi.org/10.1137/050637868, 2007. a
Yarushina, V. M. and Podladchikov, Y. Y.: (De)Compaction of Porous Viscoelastoplastic Media: Model Formulation, J. Geophys. Res.-Sol. Ea., 120, 4146–4170, https://doi.org/10.1002/2014JB011258, 2015. a
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Predicting the behaviour of magmatic systems is important for understanding Earth's matter and heat transport. Numerical modelling is a technique that can predict complex systems at different scales of space and time by solving equations using various techniques. This study tests four algorithms to find the best way to transport the melt composition. The "weighted essentially non-oscillatory" algorithm emerges as the best choice, minimising errors and preserving system mass well.
Predicting the behaviour of magmatic systems is important for understanding Earth's matter and...
