# gempy-project/gempy

GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. It also offers support for stochastic modeling to address parameter and model uncertainties.

Repository: https://github.com/gempy-project/gempy
Canonical: https://ross.abutalabs.com/products/gempy
Homepage: https://gempy.org
Language: Python
License: EUPL-1.2
License Family: copyleft
Topics: uq, bayesian, interpolation, geology, modeling, python, uncertainty-analysis, uncertainties, complex-geological-models, monte-carlo-simulation, implicit, geological, geoscience, torch
Last push: 2026-08-13T14:29:00+00:00

## Health v2 (maintenance only)
Score: 96/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 92, longevity 100
- inputs: {"age_days": 3347, "days_push": 20, "days_rel": 53, "gap_med": 7.5, "n_releases_24m": 17}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1336, forks 280 (observed 2026-08-28T04:04:25.382926+00:00)

## What it is
GemPy is an open-source Python library for implicit 3D structural geological modeling, generating complex models of layers, faults, folds, and unconformities from interface and orientation data. It is GPU-accelerated, built on PyTorch with automatic differentiation, and designed for probabilistic/Bayesian uncertainty analysis.

## Use cases
- build a 3D geological model from borehole contact points and orientation data
- model fault networks, folds, and unconformities in the subsurface
- run Monte Carlo simulations to quantify geological model uncertainty
- apply Bayesian inference to update geomodels with new data
- visualize 3D geological models interactively in Jupyter with PyVista
- create implicit stratigraphic layer models for geoscience research

## When to choose
- you need open-source, scriptable 3D structural geological modeling in Python
- you want probabilistic/uncertainty-aware geomodeling with gradient-based inference
- you need to model complex structures like fault networks and folds implicitly
- you want GPU-accelerated model generation integrated with PyTorch

## When to avoid
- you need CAD-style manual surface editing or a full commercial geomodeling GUI
- your project depends on the legacy GemPy v2 workflows
- you only need simple 2D cross-sections without 3D implicit interpolation

## Facets
- artifact type: library
- maturity: active
- function: simulation, machine-learning, data-visualization, math
- domain: simulation, data-science, machine-learning
- platform: python, cross-platform
- tags: geological-modeling, geomodeling, implicit-modeling, uncertainty-quantification, bayesian-inference, monte-carlo, pytorch, 3d-modeling, subsurface, geoscience, gpu

## Member repositories
- gempy-project/gempy (main) score 96

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:25.382926+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:44:15.459382+00:00, confidence not recorded.
  - readme: https://github.com/gempy-project/gempy (fetched 2026-08-28T04:04:25.382926+00:00, sha aded1fa1493c)
  - homepage: https://gempy.org (fetched 2026-08-29T12:03:34.012120+00:00, sha 017e8afde2da)
  - site_page: https://docs.gempy.org/installation.html (fetched 2026-08-29T12:03:34.021195+00:00, sha b037744cc57d)
  - site_page: https://docs.gempy.org/index.html (fetched 2026-08-29T12:03:34.023146+00:00, sha 41d817d97027)
  - site_page: https://www.gempy.org/about-us (fetched 2026-08-29T12:03:34.025727+00:00, sha 527b07a948aa)
  - site_page: https://www.gempy.org/installation (fetched 2026-08-29T12:03:34.028464+00:00, sha 4982889924fd)
- Data as of 2026-08-30T08:39:29.467469+00:00.