Articles | Volume 9, issue 3
https://doi.org/10.5194/gmd-9-1019-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-1019-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
10 Mar 2016
Model description paper |
| 10 Mar 2016
pynoddy 1.0: an experimental platform for automated 3-D kinematic and potential field modelling
J. Florian Wellmann
CORRESPONDING AUTHOR
RWTH Aachen University, Graduate School AICES, Schinkelstr. 2, 52062 Aachen, Germany
ABC/J Geoverbund, RWTH Aachen University, Aachen, Germany
Sam T. Thiele
The University of Western Australia, Centre for Exploration Targeting, 35 Stirling Hwy, 6009 Crawley, Australia
Mark D. Lindsay
The University of Western Australia, Centre for Exploration Targeting, 35 Stirling Hwy, 6009 Crawley, Australia
Mark W. Jessell
The University of Western Australia, Centre for Exploration Targeting, 35 Stirling Hwy, 6009 Crawley, Australia
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- Stochastic inversion of gravity, magnetic, tracer, lithology, and fault data for geologically realistic structural models: Patua Geothermal Field case study A. Pollack et al. 10.1016/j.geothermics.2021.102129
- A role for virtual outcrop models in blended learning – improved 3D thinking and positive perceptions of learning C. Bond & A. Cawood 10.5194/gc-4-233-2021
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- Estimating uncertainties in 3-D models of complex fold-and-thrust belts: A case study of the Eastern Alps triangle zone S. Brisson et al. 10.1016/j.acags.2023.100115
- Inversion of Structural Geology Data for Fold Geometry L. Grose et al. 10.1029/2017JB015177
- 3D geological structure inversion from Noddy-generated magnetic data using deep learning methods J. Guo et al. 10.1016/j.cageo.2021.104701
- Stochastic Inversion of Gravity Data Accounting for Structural Uncertainty N. Athens & J. Caers 10.1007/s11004-021-09978-2
- Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1 A. Schaaf et al. 10.5194/gmd-14-3899-2021
- Gravity inversion for geothermal exploration with uncertainty quantification N. Athens & J. Caers 10.1016/j.geothermics.2021.102230
- Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models R. Scalzo et al. 10.5194/gmd-15-3641-2022
- Structural data constraints for implicit modeling of folds L. Grose et al. 10.1016/j.jsg.2017.09.013
- An automatic geological 3D cross-section generator: Geopropy, an open-source library A. Hassanzadeh et al. 10.1016/j.envsoft.2022.105309
- Integrated framework for geological modeling: integration of data, knowledge, and methods H. Li et al. 10.1007/s10064-024-03794-8
- Uncertainty estimation for a geological model of the Sandstone greenstone belt, Western Australia – insights from integrated geological and geophysical inversion in a Bayesian inference framework J. Wellmann et al. 10.1144/SP453.12
- Uncertainty Visualisation of a 3D Geological Geometry Model and Its Application in GIS-Based Mineral Resource Assessment: A Case Study in Huayuan District, Northwestern Hunan Province, China N. Li et al. 10.1007/s12583-021-1434-y
- Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications M. Jessell et al. 10.5194/essd-14-381-2022
- Topological analysis in Monte Carlo simulation for uncertainty propagation E. Pakyuz-Charrier et al. 10.5194/se-10-1663-2019
- GemPy 1.0: open-source stochastic geological modeling and inversion M. de la Varga et al. 10.5194/gmd-12-1-2019
- Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization E. Pakyuz-Charrier et al. 10.5194/se-9-385-2018
3 citations as recorded by crossref.
- A parametric fault displacement model to introduce kinematic control into modeling faults from sparse data G. Godefroy et al. 10.1190/INT-2017-0059.1
- The topology of geology 2: Topological uncertainty S. Thiele et al. 10.1016/j.jsg.2016.08.010
- The topology of geology 1: Topological analysis S. Thiele et al. 10.1016/j.jsg.2016.08.009
Saved (preprint)
Latest update: 21 Nov 2024
Short summary
We often obtain knowledge about the subsurface in the form of structural geological models, as a basis for subsurface usage or resource extraction. Here, we provide a modelling code to construct such models on the basis of significant deformational events in geological history, encapsulated in kinematic equations. Our methods simplify complex dynamic processes, but enable us to evaluate how events interact, and finally how certain we are about predictions of structures in the subsurface.
We often obtain knowledge about the subsurface in the form of structural geological models, as a...
