Metadata-Version: 2.4
Name: gempy
Version: 2026.1.0a3
Summary: An Open-source, Python-based 3-D structural geological modeling software.
Home-page: https://github.com/cgre-aachen/gempy
Author: Miguel de la Varga, Alexander Zimmerman, Elisa Heim, Alexander Schaaf, Fabian Stamm, Florian Wellmann, Jan Niederau, Andrew Annex
Author-email: gempy@terranigma-solutions.com
License: EUPL-1.2
Keywords: geology,3-D modeling,structural geology,uncertainty
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: European Union Public Licence 1.2 (EUPL 1.2)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: AUTHORS.rst
Requires-Dist: gempy_engine>=2026.1.0a1
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## What's New: GemPy 2026.0.3 — June 2026

GemPy 2026.0.3 is the latest stable release of GemPy v3. It brings together matching releases of `gempy`, `gempy_engine`, and `gempy_viewer`, with updates across model setup, serialization, octree handling, PyTorch support, visualization, and example models.

For the full release notes, see the [GemPy 2026.0.3 release](https://github.com/gempy-project/gempy/releases/tag/v2026.0.3). For background on the GemPy v3 transition, see [What's New in GemPy v3](WhatsNewGemPy3.md).

GemPy v2 remains available as [gempy_legacy](https://github.com/gempy-project/gempy_legacy) for users who still depend on previous workflows. The core team is not developing new features for this version, but maintenance can continue based on community needs.

## Overview

[GemPy](https://www.gempy.org/) is a Python-based, **open-source geomodeling library**. It is
capable of constructing complex **3D geological models** of folded
structures, fault networks and unconformities, based on the underlying
powerful **implicit representation** approach. 

## Installation

We provide the latest release version of GemPy via PyPi package services. We highly recommend using PyPi,

`$ pip install gempy[base]`
 
## Resources

After installation, you can either check the [notebook tutorials](https://docs.gempy.org/getting_started/get_started.html#sphx-glr-getting-started-get-started-py) 
or the [video introduction](https://www.youtube.com/watch?v=n0btC5Zilyc) to get started.

Go to the [documentation site](http://docs.gempy.org/) for further information and enjoy the [tutorials and examples](https://www.gempy.org/tutorials).

For questions and support, please use [discussions](https://github.com/cgre-aachen/gempy/discussions).

If you find a bug or have a feature request, create an [issue](https://github.com/cgre-aachen/gempy/issues).

Follow these [guidelines](https://github.com/cgre-aachen/gempy/blob/WIP_readme-update-march21/CONTRIBUTING.md) to contribute to GemPy.



## Gallery
### Geometries

<p>
<table>
<tr>

  <td>
  <a href="https://docs.gempy.org/examples/geometries/a01_horizontal_stratigraphic.html#sphx-glr-examples-geometries-a01-horizontal-stratigraphic-py">
  <img alt="colormapped image plot thumbnail" src="docs/readme_images/model1_nodata.png" width="300" />
  </a>
  </td>
  
  <td>
  <a href="https://docs.gempy.org/examples/geometries/b02_fold.html#sphx-glr-examples-geometries-b02-fold-py">
  <img alt="colormapped image plot thumbnail" src="docs/readme_images/model2_nodata.png" width="300" />
  </a>
  </td>
  
   <td>
  <a href="https://docs.gempy.org/examples/geometries/c03_recumbent_fold.html#sphx-glr-examples-geometries-c03-recumbent-fold-py">
  <img alt="colormapped image plot thumbnail" src="docs/readme_images/model3_nodata.png" width="300" />
  </a>
  </td>

</tr>
<tr>

  <td>
  <a href="https://docs.gempy.org/examples/geometries/d04_pinchout.html#sphx-glr-examples-geometries-d04-pinchout-py">
  <img alt="colormapped image plot thumbnail" src="docs/readme_images/model4_nodata.png" width="300" />
  </a>
  </td>
  
  <td>
  <a href="https://docs.gempy.org/examples/geometries/e05_fault.html#sphx-glr-examples-geometries-e05-fault-py">
  <img alt="colormapped image plot thumbnail" src="docs/readme_images/model5_nodata.png" width="300" />
  </a>
  </td>
  
  <td>
  <a href="https://docs.gempy.org/examples/geometries/f06_unconformity.html#sphx-glr-examples-geometries-f06-unconformity-py">
  <img alt="colormapped image plot thumbnail" src="docs/readme_images/model6_nodata.png" width="300" />
  </a>
  </td>

</tr>
</table>
</p>

### Features

<p>
<table>
<tr>

  <td>
  <a href="https://docs.gempy.org/tutorials/ch1_fundamentals/ch1_3b_cross_sections.html#sphx-glr-tutorials-ch1-fundamentals-ch1-3b-cross-sections-py">
  <img alt="colormapped image plot thumbnail" src="https://docs.gempy.org/_images/sphx_glr_ch1_3b_cross_sections_004.png" width="300" />
  </a>
  </td>
  
  <td>
  <a href="https://docs.gempy.org/tutorials/ch1_fundamentals/ch1_4_onlap_relations.html#sphx-glr-tutorials-ch1-fundamentals-ch1-4-onlap-relations-py">
  <img alt="colormapped image plot thumbnail" src="https://docs.gempy.org/_images/sphx_glr_ch1_4_onlap_relations_002.png" width="300" />
  </a>
  </td>
  
   <td>
  <a href="https://docs.gempy.org/examples/geometries/g07_combination.html#sphx-glr-examples-geometries-g07-combination-py">
  <img alt="colormapped image plot thumbnail" src="https://docs.gempy.org/_images/sphx_glr_g07_combination_005.png" width="300" />
  </a>
  </td>

</tr>
<tr>
  <td>
  <a href="https://docs.gempy.org/tutorials/ch3-Interpolations/ch3_1_kriging_interpolation_and_simulation.html#sphx-glr-tutorials-ch3-interpolations-ch3-1-kriging-interpolation-and-simulation-py">
  <img alt="colormapped image plot thumbnail" src="https://docs.gempy.org/_images/sphx_glr_ch3_1_kriging_interpolation_and_simulation_003.png" width="300" />
  </a>
  </td>
  
  <td>
  <a href="https://docs.gempy.org/tutorials/ch4-Topology/ch4-1-Topology.html#sphx-glr-tutorials-ch4-topology-ch4-1-topology-py">
  <img alt="colormapped image plot thumbnail" src="https://docs.gempy.org/_images/sphx_glr_ch4-1-Topology_005.png" width="300" />
  </a>
  </td>
  
  <td>
  <a href="https://docs.gempy.org/tutorials/ch4-Topology/ch4-1-Topology.html#sphx-glr-tutorials-ch4-topology-ch4-1-topology-py">
  <img alt="colormapped image plot thumbnail" src="https://docs.gempy.org/_images/sphx_glr_ch4-1-Topology_004.png" width="300" />
  </a>
  </td>

</tr>
</table>



### Case studies

<p>
<table>
<tr>

  <td>
  <a href="https://docs.gempy.org/examples/real/Alesmodel.html#sphx-glr-examples-real-alesmodel-py">
  <img alt="colormapped image plot thumbnail" src="https://docs.gempy.org/_images/sphx_glr_Alesmodel_008.png" width="300" />
  </a>
  </td>
  
  <td>
  <a href="https://docs.gempy.org/examples/real/Perth_basin.html#sphx-glr-examples-real-perth-basin-py">
  <img alt="colormapped image plot thumbnail" src="https://docs.gempy.org/_images/sphx_glr_Perth_basin_006.png" width="300" />
  </a>
  </td>
  
   <td>
  <a href="https://docs.gempy.org/examples/real/Greenstone.html#sphx-glr-examples-real-greenstone-py">
  <img alt="colormapped image plot thumbnail" src="https://docs.gempy.org/_images/sphx_glr_Greenstone_004.png" width="300" />
  </a>
  </td>
</tr>
</table>

<a name="ref"></a>

## Publications using GemPy

* Montaño-Caro, J.C. & Morales-Casique, E. (2026). [Implicit 3D structural geological modeling of the Mexico Basin: a scalable and reproducible open-source workflow](https://link.springer.com/article/10.1007/s12145-025-02064-9). Earth Science Informatics, 19, 12.

* 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](https://link.springer.com/article/10.1007/s12665-024-11609-y). 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](https://www.sciencedirect.com/science/article/pii/S2590197423000046). 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](https://pubs.geoscienceworld.org/geophysics/article/88/3/G43/621596/Kernel-method-for-gravity-forward-simulation-in?casa_token=VjCR7rYOkKoAAAAA:W81L1AXgW_j9GiYPciBvLIdL8Zo66IzYVYiU6Ri8xLgIjbzTmpcDE74rzmAwnokX_71_XKg). 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](https://www.mdpi.com/2076-3417/13/10/6250). 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](https://www.frontiersin.org/articles/10.3389/feart.2023.1198215/full). 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](https://doi.org/10.1002/gdj3.208). 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](https://jose.theoj.org/papers/10.21105/jose.00185). 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](https://link.springer.com/article/10.1007/s11001-022-09475-z). 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](https://www.journals.uchicago.edu/doi/full/10.1086/722724?casa_token=pkl8XXrtyokAAAAA:YeIh1t-qwt6AT8yz_vTj4OQapaR1_nZUjS3Az_77VZXlpyfGu0cN5DSzrz6NNjoj4Qv5iud4rdc). 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](https://www.mdpi.com/2075-163X/11/11/1281). 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](https://gmd.copernicus.org/articles/14/3899/2021/gmd-14-3899-2021.html). Geosci. Model Dev., 14(6), 3899-3913. doi:10.5194/gmd-14-3899-2021
  
* Güdük, N., de la Varga, M. Kaukolinna, J. and Wellmann, F. (2021). [Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit](https://www.mdpi.com/2076-3263/11/4/150), _Geosciences_, 11(4):150.

* Wu, J., & Sun, B. (2021). [Discontinuous mechanical analysis of manifold element strain of rock slope based on open source Gempy](https://www.e3s-conferences.org/articles/e3sconf/abs/2021/24/e3sconf_caes2021_03084/e3sconf_caes2021_03084.html). In E3S Web of Conferences (Vol. 248, p. 03084). EDP Sciences.

* Stamm, F. A., de la Varga, M., and Wellmann, F. (2019). [Actors, actions, and uncertainties: optimizing decision-making based on 3-D structural geological models](https://se.copernicus.org/articles/10/2015/2019/se-10-2015-2019.html), 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](
https://www.sciencedirect.com/science/article/pii/B9780128140482000156).
In Developments in Structural Geology and Tectonics (Vol. 5, pp. 189-204). Elsevier.

Please let us know if your publication is missing!

A continuously growing list of gempy-applications (e.g. listing real-world models) can be found [here](https://hackmd.io/@Japhiolite/B1juPvCxc).

## References 

* de la Varga, M., Schaaf, A., and Wellmann, F. (2019). [GemPy 1.0: open-source stochastic geological modeling and inversion](https://gmd.copernicus.org/articles/12/1/2019/gmd-12-1-2019.pdf), Geosci. Model Dev., 12, 1-32.
* Wellmann, F., & Caumon, G. (2018). [3-D Structural geological models: Concepts, methods, and uncertainties.](https://hal.univ-lorraine.fr/hal-01921494/file/structural_models_for_geophysicsHAL.pdf) In Advances in Geophysics (Vol. 59, pp. 1-121). Elsevier.
* 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](https://www.sciencedirect.com/science/article/abs/pii/S0031920108001258). 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](https://link.springer.com/article/10.1007/BF02775087). Mathematical Geology, 29(4), 571-584.
