The idea is to predict a geophysical well log, the sonic, using a suite of other logs: depth, density, gamma ray, and neutron.
The log suite is from the same well (from Pev Avseth PhD Thesis) that Alessandro Amato del Monte used in the [Seismic Petrophysics Notebook] (https://github.com/seg/tutorials/blob/master/1506_Seismic_petrophysics_2/Seismic_petrophysics_2.ipynb) accompanying his [Geophysical tutorial] (http://library.seg.org/doi/abs/10.1190/tle34040440.1) article on The Leading Edge.
I will explore different Machine Learning methods from Scikit-Learn.
To wet your appetites, here's an example of sonic log prediction using a cross-validated linear model, which will be the benchmark for the performance of other models, such as SVM and Random Forest:
and below is a heatmap with the pairwise Spearman correlation coefficient between the logs I will use:
