Machine learning in geoscience with scikit-learn. Part 1: checking, tidying, and analyzing the dataset

The idea behind this series of articles is to show how to predict P-wave velocity, as measured by a geophysical well log (the sonic), from a suite of other logs: density, gamma ray, and neutron, and also depth, using Machine Learning.

The log suite is from the same well that Alessandro Amato del Monte used in the Seismic Petrophysics Notebook accompanying his Geophysical tutorial article on The Leading Edge.

I will explore different Machine Learning methods from the scikit-learn Python library and compare their performances.

To wet your appetites, here’s an example of P-wave velocity, Vp, predicted using a cross-validated linear model, which will be the benchmark for the performance of other models, such as SVM and Random Forest:

multilinear

In the first notebook, which is already available on GitHub here, I show how to use the Pandas and Seaborn Python libraries to import the data, check it, clean it up, and visualize to explore relationships between the variables. For example, shown below is a heatmap with the pairwise Spearman correlation coefficient between the variables (logs):

heatmap

Stay tuned for the next post / notebook!

PS: I am very excited by the kick-off of the Geophysical Tutorial (The Leading Edge) Machine Learning Contest 2016. Check it out here!

2 responses to “Machine learning in geoscience with scikit-learn. Part 1: checking, tidying, and analyzing the dataset

  1. Pingback: Machine learning in geoscience with scikit-learn: series outline | MyCarta·

  2. Pingback: Machine Learning in Geoscience with Scikit-learn. Part 2: inferential statistics and domain knowledge to select features for oil prediction | MyCarta·

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s