[DOC] Adding readme

Author: Leguark

README.md

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+# <p align="center"><img src="docs/source/_static/Model_space3.png" width="1000"></p>
+
+> Open-source, implicit 3D structural geological modeling in Python.
+
+[![PyPI](https://img.shields.io/badge/python-3.10-blue.svg)](https://www.python.org/downloads/release/python-31013/)
+[![PyPI](https://img.shields.io/badge/pypi-1.0-blue.svg)](https://pypi.org/project/gempy/)
+[![license: EUPL v1.2](https://img.shields.io/badge/license-EUPL%20v1.2-blue.svg)](https://github.com/cgre-aachen/gempy/blob/master/LICENSE)
+[![Documentation Status](https://assets.readthedocs.org/static/projects/badges/passing-flat.svg)](http://docs.gempy.org)
+[![DOI](https://zenodo.org/badge/96211155.svg)](https://zenodo.org/badge/latestdoi/96211155)
+
+
+## Overview
+
+*GemPy Probability* is a package that extends the functionality of the
+[GemPy](https://www.gempy.org) package to include uncertainty quantification
+and stochastic geological modeling. It is based on the [Pyro (https://pyro.ai)
+probabilistic programming framework and allows for the integration of
+probabilistic models into the geological modeling workflow.
+## Installation
+
+We provide the latest release version of GemPy via PyPi package services. We highly recommend using PyPi,
+
+`$ pip install gempy_probability`
+
+<a name="ref"></a>
+## 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.
+
+## Publications using GemPy
+
+
+* 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. https://doi.org/10.3390/geosciences11040150.
+* 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.
+
+A continuously growing list of gempy-applications (e.g. listing real-world models) can be found [here](https://hackmd.io/@Japhiolite/B1juPvCxc).
+