Pynoddy 1.0: An experimental platform for automated 3-D kinematic and potential field modelling

J. Florian Wellmann, Sam T. Thiele, Mark D. Lindsay, Mark W. Jessell

    Research output: Contribution to journalArticlepeer-review

    19 Citations (Scopus)


    © Author(s) 2016. We present a novel methodology for performing experiments with subsurface structural models using a set of flexible and extensible Python modules. We utilize the ability of kinematic modelling techniques to describe major deformational, tectonic, and magmatic events at low computational cost to develop experiments testing the interactions between multiple kinematic events, effect of uncertainty regarding event timing, and kinematic properties. These tests are simple to implement and perform, as they are automated within the Python scripting language, allowing the encapsulation of entire kinematic experiments within high-level class definitions and fully reproducible results. In addition, we provide a link to geophysical potential-field simulations to evaluate the effect of parameter uncertainties on maps of gravity and magnetics.

    We provide relevant fundamental information on kinematic modelling and our implementation, and showcase the application of our novel methods to investigate the interaction of multiple tectonic events on a pre-defined stratigraphy, the effect of changing kinematic parameters on simulated geophysical potential fields, and the distribution of uncertain areas in a full 3-D kinematic model, based on estimated uncertainties in kinematic input parameters. Additional possibilities for linking kinematic modelling to subsequent process simulations are discussed, as well as additional aspects of future research. Our modules are freely available on github, including documentation and tutorial examples, and we encourage the contribution to this project.
    Original languageEnglish
    Pages (from-to)1019-1035
    Number of pages17
    JournalGeoscientific Model Development
    Issue number3
    Publication statusPublished - 2016


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