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-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
@article{Goncalves2020,
abstract = {Solar cycle prediction is a key activity in space weather research. Several techniques have been employed in recent decades in order to try to forecast the next sunspot-cycle maxima and time. In this work, the Gaussian Process, a machine-learning technique, is used to make a prediction for the solar cycle 25 based on the annual sunspot number 2.0 data from 1700 to 2018. A variation known as Warped Gaussian Process is employed in order to deal with the non-negativity constraint and asymmetrical data distribution. Tests using holdout data yielded a root mean square error of 10.0 within 5 years and 25.0-35.0 within 10 years. Simulations using the predictive distribution were performed to account for the uncertainty in the prediction. Cycle 25 is expected to last from 2019 – 2029, with a peak sunspot number about 117 (110 by the median) occurring most likely in 2024. Thus our method predicts that solar Cycle 25 will be weaker than previous ones, implying a continuing trend of declining solar activity as observed in the past two cycles.},
author = {Ítalo G. Gonçalves and Ezequiel Echer and Everton Frigo},
doi = {10.1016/j.asr.2019.11.011},
journal = {Advances in Space Research},
keywords = {gaussian process,machine learning,solar cycle,sunspot number},
pages = {677-683},
title = {Sunspot Cycle Prediction Using Warped Gaussian Process Regression},
volume = {65},
url = {https://www.sciencedirect.com/science/article/pii/S0273117719308026},
year = {2020},
}
@article{Goncalves2021,
author = {Ítalo Gomes Gonçalves and Felipe Guadagnin and Sissa Kumaira and Saulo Lopes da Silva},
doi = {10.1016/j.cageo.2021.104715},
issn = {0098-3004},
issue = {January},
journal = {Computers and Geosciences},
keywords = {Gaussian Process,Implicit modeling,Kriging,Machine learning,Structural trend,Vector field},
pages = {104715},
publisher = {Elsevier Ltd},
title = {A machine learning model for structural trend fields},
volume = {149},
url = {https://doi.org/10.1016/j.cageo.2021.104715},
year = {2021},
}
@article{Goncalves2022,
author = {Ítalo Gomes Gonçalves and Felipe Guadagnin and Diogo Peixoto Cordova},
doi = {10.1016/j.cageo.2022.105056},
issn = {00983004},
journal = {Computers & Geosciences},
keywords = {Gaussian process,Kriging,Machine learning,Variational inference},
pages = {105056},
publisher = {Elsevier Ltd},
title = {Learning spatial patterns with variational Gaussian processes: Regression},
volume = {161},
url = {https://doi.org/10.1016/j.cageo.2022.105056},
year = {2022},
}
@article{,
author = {Ítalo Gomes Gonçalves and Felipe Guadagnin and Diogo Peixoto Cordova},
doi = {10.1016/j.cageo.2023.105323},
issn = {00983004},
journal = {Computers & Geosciences},
month = {5},
pages = {105323},
title = {Variational Gaussian processes for implicit geological modeling},
volume = {174},
url = {https://linkinghub.elsevier.com/retrieve/pii/S0098300423000274},
year = {2023},
}