Spatial modeling using machine learning concepts.
This is a work in progress. Current functionality includes:
- Gaussian process modeling in 1D, 2D, and 3D, with anisotropy ellipsoid;
- Variational Gaussian process for classification and multivariate modeling;
- Support for compositional data;
- Support for directional data (structural geology measurements, scalar field gradients, etc.);
- Support for classification with boundary data (points lying in the boundary between two rock types);
- Deep learning for non-stationary modeling;
- Exports results to PyVista format;
- Back-end powered by TensorFlow.
Clone the repo and update the path to include the package's folder.
Dependencies:
scikit-image
pandas
numpy
tensorflow
tensorflow-probability
pyvista
andplotly
for 3D visualization
The following notebooks demonstrate the capabilities of the package (if one of them seems broken, it is probably going through an update).