# Welcome to GStatSim

GStatSim is a Python package specifically designed for geostatistical interpolation and simulation. It is inspired by open source geostatistical resources such as GeostatsPy and SciKit-GStat. The functions are intended to address the challenges of working with datasets with large crossover errors, non-linear trends, variability in measurement density, and non-stationarity. These tools are part of our ongoing effort to develop and adapt open-access geostatistical functions.

In its current state, the demos focus on the geostatistical simulation of subglacial topography. However, these protocols could be applied to a number topics in glaciology, or geoscientific problems in general.

We will continuously develop new tools and tutorials to address specific technical challenges in geostatistics. Do you have feedback or suggestions? Specific things that we should account for? Feel free to contact us at emackie@ufl.edu. Our goal is to create tools that are useful and accessible, so we welcome your thoughts and insight.

## Contributors
(Emma) Mickey MacKie, University of Florida

Michael Field, University of Florida

Lijing Wang, Stanford University

(Zhen) David Yin, Stanford University

Nathan Schoedl, University of Florida

Matthew Hibbs, University of Florida


## How to cite

MacKie, Emma, Field, Michael, Wang, Lijing, Yin, Zhen, Schoedl, Nathan, & Hibbs, Matthew. (2022). GStatSim (1.0). Zenodo. https://doi.org/10.5281/zenodo.7230276

or

@software{mackie_emma_2022_7274640,
  author       = {MacKie, Emma and
                  Field, Michael and
                  Wang, Lijing and
                  Yin, Zhen and
                  Schoedl, Nathan and
                  Hibbs, Matthew},
  title        = {GStatSim},
  month        = oct,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {1.0},
  doi          = {10.5281/zenodo.7274640},
  url          = {https://doi.org/10.5281/zenodo.7274640}
}

## Datasets

The demos use radar bed measurements from the Center for the Remote Sensing of Ice Sheets (CReSIS, 2020).

CReSIS. 2020. Radar depth sounder, Lawrence, Kansas, USA. Digital Media. http://data.cresis.ku.edu/.


## Acknowledgments

This work was supported by the Earth Sciences Information Partners (ESIP) Lab and the National Science Foundation (NSF) GeoSMART program.


## Functionalities

```{tableofcontents}
```