Spatial modeling using machine learning concepts.
This package contains machine learning models specialized in spatial interpolation and smoothing with calibrated confidence intervals. 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 implicit modelling 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.
pip install git+https://github.com/italo-goncalves/geoML
Dependencies:
scikit-imagepandasnumpytensorflowtensorflow-probabilitypyvistaandplotlyfor 3D visualization
The following notebooks demonstrate the capabilities of the package (if one of them seems broken, it is probably going through an update).
- Walker Lake
- 2D classification with structural constraints
- Sunspot cycle prediction
- 3D classification
- Potential field modeling using only directional data
- Jura
- Compositional data
- Gold modeling with auxiliary variables
- Dealing with faults (experimental)
- geoML short course presentation
- Notebook 01
- Notebook 02
- Notebook 03
- Notebook 04
- Notebook 05
- Notebook 06
- Notebook 07
- Notebook 08
- Notebook 09
- 2020 - Sunspot Cycle Prediction Using Warped Gaussian Process Regression
- 2021 - A machine learning model for structural trend fields
- 2022 - Learning spatial patterns with variational Gaussian processes: Regression
- 2023 - Variational Gaussian processes for implicit geological modeling
- 2024 - Moho depth model of South America from a machine learning approach
- 2025 - Uncertainty Propagation in Deep Gaussian Process Networks (open access)