.. GeMpy documentation master file, created by sphinx-quickstart on Wed Dec 14 12:44:40 2016. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. ../logos/gempy1.png :width: 30% About ===== Open-source software for implicit 3D structural geological modeling in Python. ****************************************************************************** Overview -------- ``GemPy`` is a Python-based, community-driven, **open-source geomodeling library**. It is capable of constructing complex **3D geological models** including various features such as fold structures, fault networks and unconformities, based on an underlying powerful **implicit** approach. From the ground up, ``GemPy`` was designed to be easily embedded in probabilistic frameworks for conducting uncertainty analysis regarding subsurface structures. .. Check out the documentation either in `gempy.org `_ (better option), or `read the docs `_. 3D models created with GemPy may look like this: .. image:: ./_images/AlesModel_example.png Contents: .. toctree:: :maxdepth: 1 self installation .. toctree:: :maxdepth: 1 tutorials/index examples/index .. toctree:: :maxdepth: 1 external/external_examples .. toctree:: :maxdepth: 1 api_reference Features -------- Geological features ^^^^^^^^^^^^^^^^^^^ ``GemPy`` is capable of modeling complex 3D geological scenarios, including: * Multiple conformal layers (e.g. sequences of sedimentary layers) * Several sequences of layers, with conformal continuation or unconformities * Magmatic bodies of (almost) arbitrary shapes * Faults (offset calculated automatically from affected geological objects) * Full fault networks (faults affecting faults) * Folds (affecting single layers or entire layer stacks, including overturned and recumbent folds) Combining these elements in GemPy allows for the generation of realistic 3D geological models, on a par with most commercial geomodeling software. Interpolation approach ^^^^^^^^^^^^^^^^^^^^^^ The generation of complex structural settings is based on the powerful interpolation algorithm underlying ``GemPy``\ , a unviersal cokriging method devised by `Lajaunie et al. (1997)` and extended by `Calcagno et al. (2008)`\ . This method is used to interpolate a 3D scalar field, such that geologically significant interfaces are isosurfces in this field. The algorithm allows for a direct integration of two of the most relevant geological input data types: * **Surface contact points**\ : 3D coordinates of points marking the boundaries between different features (e.g. layer interfaces, fault planes, unconformities). * **Orientation measurements**\ : Orientation of the poles perpendicular to the dipping of surfaces at any point in 3D space. ``GemPy`` also allows for the definition of topological elements such as combining multiple stratigraphic sequences and complex fault networks to be considered in the modeling process. .. image:: ./_images/data_to_model.png Integrated visualization ^^^^^^^^^^^^^^^^^^^^^^^^ Models generated with ``GemPy`` can be visualized in several ways: * direct visualization of 2D model sections (or geological maps) using `matplotlib `_, including hillshading and other options for intuitive representation of results; * 3D visualization using `Pyvista `_, including interactive plots .. image:: _images/3D_view_pyvista_example.png :target: https://cgre-aachen.github.io/gempy/_images/sphx_glr_ch1_1_basics_009.png :width: 70% For a more detailed elaboration of the theory behind ``GemPy``\ , we refer to the **open access scientific publication**\ : `\ "GemPy 1.0: open-source stochastic geological modeling and inversion" by de la Varga et al. (2019) `_. Publications using GemPy ------------------------ - Marquetto, L., Jüstel, A., Troian, G.C., Reginato, P.A.R & Simões, J.C. (2024). `Developing a 3D hydrostratigraphical model of the emerged part of the Pelotas Basin along the northern coast of Rio Grande do Sul state, Brazil `_. Environmental Earth Sciences, 83, 329. - Brisson, S., Wellmann, F., Chudalla, N., von Harten, J., & von Hagke, C. (2023). `Estimating uncertainties in 3-D models of complex fold-and-thrust belts: A case study of the Eastern Alps triangle zone `_. Applied Computing and Geosciences, 18, 100115. - Liang, Z., de la Varga, M., & Wellmann, F. (2023). `Kernel method for gravity forward simulation in implicit probabilistic geologic modeling `_. Geophysics, 88(3), G43-G55. - Kong, S., Oh, J., Yoon, D., Ryu, D. W., & Kwon, H. S. (2023). `Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity Surveys `_. Applied Sciences, 13(10), 6250. - Thomas, A. T., Micallef, A., Duan, S., & Zou, Z. (2023). `Characteristics and controls of an offshore freshened groundwater system in the Shengsi region, East China Sea `_. Frontiers in Earth Science, 11, 1198215. - Haehnel, P., Freund, H., Greskowiak, J., & Massmann, G. (2023). `Development of a three-dimensional hydrogeological model for the island of Norderney (Germany) using GemPy `_. Geoscience Data Journal, 00, 1–17. - Jüstel, A., de la Varga, M., Chudalla, N., Wagner, J. D., Back, S., & Wellmann, F. (2023). `From Maps to Models-Tutorials for structural geological modeling using GemPy and GemGIS `_. Journal of Open Source Education, 6(66), 185. - Thomas, A. T., von Harten, J., Jusri, T., Reiche, S., & Wellmann, F. (2022). `An integrated modeling scheme for characterizing 3D hydrogeological heterogeneity of the New Jersey shelf `_. Marine Geophysical Research, 43, 11. - Sehsah, H., Eldosouky, A. M., & Pham, L. T. (2022). `Incremental Emplacement of the Sierra Nevada Batholith Constrained by U-Pb Ages and Potential Field Data `_. The Journal of Geology, 130(5), 381-391. - von Harten, J., de la Varga, M., Hillier, M., & Wellmann, F. (2021). `Informed Local Smoothing in 3D Implicit Geological Modeling `_. Minerals 2021, 11, 1281. - Schaaf, A., de la Varga, M., Wellmann, F., & Bond, C. E. (2021). `Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1 `_. Geosci. Model Dev., 14(6), 3899-3913. doi:10.5194/gmd-14-3899-2021. - Güdük, N., de la Varga, M., Kaukolinna, J., & Wellmann, F. (2021). `Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit `_. Geosciences, 11(4):150. - Wu, J., & Sun, B. (2021). `Discontinuous mechanical analysis of manifold element strain of rock slope based on open source Gempy `_. In E3S Web of Conferences (Vol. 248, p. 03084). EDP Sciences. - Stamm, F. A., de la Varga, M., & Wellmann, F. (2019). `Actors, actions, and uncertainties: optimizing decision-making based on 3-D structural geological models `_. Solid Earth, 10, 2015–2043. - Wellmann, F., Schaaf, A., de la Varga, M., & von Hagke, C. (2019). `From Google Earth to 3D Geology Problem 2: Seeing Below the Surface of the Digital Earth `_. In Developments in Structural Geology and Tectonics (Vol. 5, pp. 189-204). Elsevier. References ---------- * de la Varga, M., Schaaf, A., and Wellmann, F.: GemPy 1.0: `open-source stochastic geological modeling and inversion,` Geosci. Model Dev., 12, 1–32, https://doi.org/10.5194/gmd-12-1-2019, 2019. * Calcagno, P., Chilès, J. P., Courrioux, G., & Guillen, A. (2008). `Geological modelling from field data and geological knowledge: Part I. Modelling method coupling 3D potential-field interpolation and geological rules.` Physics of the Earth and Planetary Interiors, 171(1-4), 147-157. * `Lajaunie, C., Courrioux, G., & Manuel, L. (1997). `Foliation fields and 3D cartography in geology: principles of a method based on potential interpolation.` Mathematical Geology, 29(4), 571-584. Indices and tables ================== * :ref:`genindex` * :ref:`search` .. image:: _static/logos/logo_CGRE.png :width: 40% .. image:: _static/logos/Terranigma.png :width: 40%