This repo is an example of how machine learning can be used to carry out spatio-temporal data analysis of geological data along subduction zones, including mineral deposits. Modified from the workflows of Butterworth, N., D. Steinberg, R. D. Müller, S. Williams, A. S. Merdith, and S. Hardy (2016), Tectonic environments of South American porphyry copper magmatism through time revealed by spatiotemporal data mining, Tectonics, 35, 2847–2862, doi:10.1002/2016TC004289
Use Docker or install the dependencies in your computer.
Run python3 jupyter notebook in the root folder of this repoisitory(usually the folder "spatial-temporal-exploration").
If you are using Docker, run the Docker container in the root folder of this repoisitory (See Docker section -> step2).
Go into the "python" folder, open the notebooks and follow the instructions inside. The notebooks have been named with step numbers.
The easist way to run the workflow.ipynb is use docker.
go to https://docs.docker.com/install/ and follow the instructions inside.
docker build -t my_spatial_temporal_exploration .
Alternatively, you can pull the docker container image from dockerhub.com. Run docker pull gplates/spatial-temporal-exploration and docker tag gplates/spatial-temporal-exploration my_spatial_temporal_exploration.
docker run -p 8888:8888 -it --rm -v`pwd`:/workspace my_spatial_temporal_exploration /bin/bash -c "source activate pyGEOL && jupyter notebook --allow-root --ip=0.0.0.0 --no-browser"
(IMPORTANT!!! run this command in the root directory of this repository)
step 3: in web browser, go to http://127.0.0.1:8888 and open the notebooks in the "python" folder.
If you would like to setup the runtime environment on your computer, you need to install the following dependencies in Python3.
(Again, please consider using Docker if you are not too good with computers.)
For example, you can create a conda enviroment with the command below
conda create -n pyGEOL python=3.7 scipy scikit-learn matplotlib pyshp numpy jupyter cartopy pandas notebook netCDF4 opencv
pygplates -- https://www.gplates.org/download.html
scikit-learn -- https://scikit-learn.org/stable/
scipy -- https://www.scipy.org/
matplotlib -- https://matplotlib.org/
pyshp -- https://pypi.org/project/pyshp/
numpy -- https://numpy.org/
jupyter notebooks -- https://jupyter.org/
cartopy -- https://scitools.org.uk/cartopy/docs/latest/
pandas -- https://pandas.pydata.org/
netCDF4 -- https://github.com/Unidata/netcdf4-python
opencv -- https://opencv.org/
EarthByte/PlateTectonicTools -- https://github.com/EarthByte/PlateTectonicTools.git. Edit "plate_tectonic_tools_path" parameter in python/parameters.py to specify the location of PlateTectonicTools code.
The old AREPS plate model has some serious bugs which have been fixed in version 1.15.
The v1.15 age grids can be found here https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_2016_AREPS/Muller_etal_2016_AREPS_Agegrids/Muller_etal_2016_AREPS_Agegrids_v1.15/.
The v1.15 rotation model is here https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_2016_AREPS/Muller_etal_2016_AREPS_Supplement/Muller_etal_2016_AREPS_Supplement_v1.15/
Answer: The code you are trying to run depends on EarthByte/PlateTectonicTools -- https://github.com/EarthByte/PlateTectonicTools.git. You need to download the PlateTectonicTools code and edit the "plate_tectonic_tools_path" parameter in python/parameters.py to tell the code where to find the PlateTectonicTools code.
Butterworth, N., D. Steinberg, R. D. Müller, S. Williams, A. S. Merdith, and S. Hardy (2016), Tectonic environments of South American porphyry copper magmatism through time revealed by spatiotemporal data mining, Tectonics, 35, 2847–2862, doi:10.1002/2016TC004289