gempy 2024.1.4

An Open-source, Python-based 3-D structural geological modeling software.

What's New: GemPy 2024.1 (a.k.a GemPy v3) Release!

Welcome to the era of GemPy v3! We are thrilled to announce the release of the latest version, a product of meticulous planning, redesign, and rigorous testing. While the core essence remains intact, v3 brings significant enhancements and novelties that promise to revolutionize your geomodeling experience.

Delve into the exciting new features in the What's New in GemPy v3.

The journey from GemPy v2 to v3 has been transformative. To ensure that our users don't lose out on any previous functionalities, we've shifted v2 to a package named gempy_legacy. While the core team will not develop any new features for this version, we'll continue maintaining it based on community requests.

Overview

GemPy 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 or the video introduction to get started.

Go to the documentation site for further information and enjoy the tutorials and examples.

For questions and support, please use discussions.

If you find a bug or have a feature request, create an issue.

Follow these guidelines to contribute to GemPy.

Gallery

Geometries

Features

Case studies

Publications using GemPy

• 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. and 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., and 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.

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.

References

• de la Varga, M., Schaaf, A., and Wellmann, F. (2019). GemPy 1.0: open-source stochastic geological modeling and inversion, Geosci. Model Dev., 12, 1-32.
• Wellmann, F., & Caumon, G. (2018). 3-D Structural geological models: Concepts, methods, and uncertainties. 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. 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.