Abstract
Abstract. 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.
Highlights
A wide range of methods exists for the computational synthesis of geological models as interpretations about the structure of the subsurface
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
We extend the capability of an existing kinematic modelling method, implemented in the software Noddy (Jessell, 1981; Jessell and Valenta, 1996), with a flexible set of dedicated scripting modules developed in the programming language Python
Summary
A wide range of methods exists for the computational synthesis of geological models as interpretations about the structure of the subsurface (see, for example, Jessell et al, 2014, for a recent overview of methods). Each modelling method focusses on different aspects of geological data and concepts, but they can be broadly classified in terms of (1) surface- or volume-based interpolation techniques, (2) pure geophysical inversions, and (3) mechanical or kinematic modelling approaches. Structural geological models are generally produced by combining information from direct observations (e.g. measurements in outcrops or boreholes) and indirect data, for example, interpreted from geophysical data. Additional aspects of the conceptual geological model or the structural setting are, in the general case, only indirectly taken into account. Computational methods, which are able to capture several or all of the previous considerations, are used to produce the model
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