.nodeKeep

egusphere-2022-1224.pdf

GStatSim

Node Root

root

GStatSim paper

Run the Jupyter Notebook code to reproduce figure 1a


[GStatSim: 1_Experimental_Variogram.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/1_Experimental_Variogram.ipynb)



Run the Jupyter Notebook code to reproduce figure 2


[GStatSim: 1_Experimental_Variogram.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/1_Experimental_Variogram.ipynb)


Run the Jupyter Notebook code to reproduce figure 3a


[GStatSim: 2_Variogram_model.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/2_Variogram_model.ipynb)


Run the Jupyter Notebook code to reproduce figure 3b


[GStatSim: 1_Experimental_Variogram.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/1_Experimental_Variogram.ipynb)


Run the Jupyter Notebook code to reproduce figure 4


[GStatSim: 3_Simple_kriging_and_ordinary_kriging.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/3_Simple_kriging_and_ordinary_kriging.ipynb)


Run the Jupyter Notebook code to reproduce figure 5a


[GStatSim: 3_Simple_kriging_and_ordinary_kriging.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/3_Simple_kriging_and_ordinary_kriging.ipynb)


Run the Jupyter Notebook code to reproduce figure 5b


[GStatSim: 4_Sequential_Gaussian_Simulation.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/4_Sequential_Gaussian_Simulation.ipynb)


Run the Jupyter Notebook code to reproduce figure 6


[GStatSim: 4_Sequential_Gaussian_Simulation.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/4_Sequential_Gaussian_Simulation.ipynb)


Run the Jupyter Notebook code to reproduce figure 7


[GStatSim: 6_interpolation_with_anisotropy.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/6_interpolation_with_anisotropy.ipynb)


Run the Jupyter Notebook code to reproduce figure 8


[GStatSim: 9_interpolation_with_a_trend.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/9_interpolation_with_a_trend.ipynb)


Run the Jupyter Notebook code to reproduce figure 9


[GStatSim: 9_interpolation_with_a_trend.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/9_interpolation_with_a_trend.ipynb)


Run the Jupyter Notebook code to reproduce figure 10


[GStatSim: 7_non-stationary_SGS_example1.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/7_non-stationary_SGS_example1.ipynb)


Run the Jupyter Notebook code to reproduce figure 11


[GStatSim: 8_non-stationary_SGS_example2.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/8_non-stationary_SGS_example2.ipynb)


Run the Jupyter Notebook code to reproduce figure 12


[GStatSim: 10_cokriging_and_cosimulation_MM1.ipynb](#/code/99edcac7-a7e8-47c6-8fd4-e5db5785dbef?path=demos/10_cokriging_and_cosimulation_MM1.ipynb)


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 large data sets, 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.

GStatSim V1.0: A Python package for geostatistical interpolation and simulation