Underworld2: Python Geodynamics Modelling for Desktop, HPC and Cloud

Mansour, John; Giordani, Julian; Moresi, Louis; Beucher, Romain; Kaluza, Owen; Velic, Mirko; Farrington, Rebecca; Quenette, Steve; Beall, Adam

doi:10.21105/joss.01797

Cited by 54 publications

(34 citation statements)

References 9 publications

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“…The model setups and physical parameters used in this study are listed in Tables 1 and S1 in the supporting information, respectively. The visco‐plastic thermomechanical mantle convection models presented in this study are solved with the finite‐element software Underworld2 (Mansour et al, 2020; Moresi et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

Yanshanian Orogeny During North China's Drifting Away From the Trench: Implications of Numerical Models

et al. 2020

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Tectonics in North China and adjacent areas were dominated by the Pacific Plate subduction since the Jurassic. Although the extension-dominated tectonics in North China since the Early Cretaceous can be well explained by the Pacific Plate subduction, it is still not well understood why North China and adjacent areas underwent intense compression (Yanshanian orogeny) during the Late Jurassic and why these regions rapidly switched to extension-dominated tectonics during the Early Cretaceous. Plate reconstructions suggest that East Asia was moving rapidly in the direction opposite the trench during the Jurassic when North China was undergoing strong compressional deformation. This relationship between back-arc deformation and overriding plate's motion seems to contradict previous compilations and geodynamic models. We suggest that large-scale mantle convection and exotic terrane accretions along the East Asian margin have jointly contributed to the Yanshanian orogeny across North China during the Jurassic. We verify this hypothesis with numerical geodynamic models. Our numerical experiments also suggest that large-scale downwelling mantle flow beneath Asia has promoted continued subduction beneath the Asian margin despite the subduction was often punctuated by collisional and accretionary events.

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Section: Methodsmentioning

confidence: 99%

Yanshanian Orogeny During North China's Drifting Away From the Trench: Implications of Numerical Models

et al. 2020

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“…The particle-in-cell, finite-element, geodynamics code Underworld2 (Mansour et al, 2020) (https://doi.org/10.5281/zenodo.1436039) is used to model the 3D thermo-mechanical deformation in the SAF system (Moresi et al, 2007). Underworld2 solves the equations for conservation of momentum and mass (incompressible material).…”

Section: Methodsmentioning

confidence: 99%

Crustal-scale listric geometry of the San Andreas Fault driven by lower crustal flow

Yang¹,

Moresi²,

Quigley³

2021

Preprint

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The San Andreas Fault (SAF) is one of the dominant components of the transform boundary between the Pacific and the North American Plate. Although the fault is verticalto sub-vertical at shallow (<10 km) depth, it variably dips at angles of ca. 40-70º to the southwest near the western Transverse Range and to the northeast in its southern segment at depths of ca. 10-20 km, and thus can be described as having a listric geometry at the crustal scale. The mechanism controlling the fault dip direction variation at depth along SAF is not well understood. We utilize a 3D, finite element thermomechanical, viscoplastic model to simulate lithospheric deformation associated with the SAF. The Big Bend of the fault near the western Transverse Range is taken as a geometrical initial condition. Numerical experiments demonstrate that regional lower crust strength variation along the SAF strike is an important control on fault dip direction. For two blocks separated by transpressional faults, viscous lower crustal material moves from the high viscosity (strong) block to the lower viscosity (weak) lower crustal block. Fault-plane-normal flow in the viscous lower crust forces fault dip direction at brittle-ductile transitional depth to rotate in the flow direction. Geophysical data suggest that the Great Valley (eastern block) and south coast area (western block) have stronger lower crust than their opposing fault blocks and that the SAF dips towards the weaker block in both instances. Our self-consistent model also sheds light on the left-lateral Garlock Fault, which intersects the right-lateral SAF in the western Transverse Range. To maintain a vertical dip in the shallow crust and structural-kinematic connectivity with the migrating fault zone at visco-elastic depths, the SAF may exhibit spatiotemporal transience in upper crustal fault location and geometry over geological time scales, including strain zone widening and dispersions. This behavior is expected to have ramifications for SAF earthquake behaviors including rupture nucleation locations and segmentation and adds 3 complexity to expectations that strain should localize on sub-vertical strike-slip faults with increasing fault maturity.

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“…We examine the effectiveness of several smoothing methods in eliminating spurious stress fluctuations within the framework of Underworld, which is a Python package used to simulate geodynamics processes (Moresi et al, 2007;Mansour et al, 2020). The smoothing methods we test can be conveniently implemented and efficiently run with Underworld.…”

Section: Introductionmentioning

confidence: 99%

Stress Recovery for the Particle-in-cell Finite Element Method

Yang¹,

Moresi²,

Mansour³

2020

Preprint

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The interelement stress in the Finite Element Method is not continuous in nature, and stress projections from quadrature points to mesh nodes often causes oscillations. The widely used particle-in-cell method cannot avoid this issue and produces worse results when there are mixing materials of large strength (e.g., viscosity in Stokes problems) contrast in one element. The post-processing methods including (1) distance weighted average from surrounding particles to the centroid mesh node (Post-local), (2) global projection with least square fit (Post-global), and (3) superconvergent point recovery method (SPR), cannot effectively eliminate the stress fluctuations. We propose three pre-processing methods to reduce the interface contrast in mixing elements: (1) global method with harmonic-mean averaging (GHM), (2) unification of properties at mixed-material elements (UnE), and (3) averaging particle properties within a specified distance to gauss quadrature points (AGP). For tests of Q1 elements, the results processed by combining either pre-processing method with the Post-local projection can increase the precision. The GHM pre-processing method is the least computationally expensive application and the easiest to implement, the AGP pre-processing method is the most expensive and the UnE in-between. However, for Q2 elements, the GHM pre-processing method fails in stress recovery, and produces worse results than those without any pre-processing procedures. For general cases (both Q1 and Q2 elements), the AGP pre-processing method is recommended. The optimal sampling radius used in the AGP method is close to that size of one element, beyond which it increases computational time, but does not significantly increases the accuracy of recovered stresses. In terms of the averaging approaches used in the AGP method, the harmonic mean is suitable for simple-shear-dominated processes and the arithmetic mean is better for the pure-shear-dominated models. For complex models, the AGP method of harmonic mean combined with the SPR post-procedure is recommended. The AGP method is found to be able to efficiently reduce stress perturbations in a synthetic model of complex fault geometries like the San Andreas Fault system.

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Underworld2: Python Geodynamics Modelling for Desktop, HPC and Cloud

Cited by 54 publications

References 9 publications

Yanshanian Orogeny During North China's Drifting Away From the Trench: Implications of Numerical Models

Yanshanian Orogeny During North China's Drifting Away From the Trench: Implications of Numerical Models

Crustal-scale listric geometry of the San Andreas Fault driven by lower crustal flow

Stress Recovery for the Particle-in-cell Finite Element Method

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