Tag Archives: machine learning

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

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In the first post of this series I showed how to use Pandas, Seaborn, and Matplotlib to:

  • load a dataset
  • test, clean up, and summarize the data
  • start looking for relationships between variables using scatterplots and correlation coefficients

In this second post, I will expand on the latter point by introducing some tests and visualizations that will help highlight the possible criteria for choosing some variables, and dropping others. All in Python.

I will use a different dataset than that in the previous post. This one is from the paper “Many correlation coefficients, null hypotheses, and high value“(Lee Hunt, CSEG Recorder, December 2013).

The target to be predicted is oil production from a marine barrier sand. We have measured production (in tens of barrels per day) and 7 unknown (initially) predictors, at 21 wells.

Hang on tight, and read along, because it will be a wild ride!

I will show how to:

1) automatically flag linearly correlated predictors, so we can decide which might be dropped. In the example below (a matrix of pair-wise correlation coefficients between variables) we see that X2, and X7, the second and third best individual predictors of production (shown in the bottom row) are also highly correlated to X1, the best overall predictor.

2) automatically flag predictors that fail a critical r test

3) create a table to assess the probability that a certain correlation is spurious, in other words the probability of getting at least the correlation coefficient we got with our the sample, or even higher, purely by chance.

I will not recommend to run these tests and apply the criteria blindly. Rather, I will suggest how to use them to learn more about the data, and in conjunction with domain knowledge about the problem at hand (in this case oil production), make more informed choices about which variables should, and which should not be used.

And, of course, I will show how to make the prediction.

Have fun reading: get the Jupyter notebook on GitHub.

Machine learning in Planetary Science: compressing Pluto images with scikit-learn and PCA

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In a previous post I showed some of the beautiful new images of Pluto from New Horizon’s mission,  coloured using the new Matplotlib perceptual colormaps:

colormappedNew_Horizons_Pluto

More recently I was experimenting with Principal Component Analysis in scikit-learn, and one of the things I used it for was compression of some of these Pluto images. Below is an example of the first two components from the False Color Pluto image:

You can take a look at the Python code available on this Jupyter Notebook. There are of course better ways of compressing images, but this was a fun way to play around with PCA.

In a follow-up post I will use image registration and image processing techniques to reproduce from the raw channels NASA’s Psychedelic Pluto Image.

 

 

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

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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:

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):

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!

Machine learning in geoscience and planetary science with scikit-learn: series outline

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  • Machine learning in geoscience with scikit-learn. Part 3: the SEG ML contest
  • Machine Learning in Geoscience with Scikit-learn. Part 4: TBE

Looking for opportunities

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As of yesterday,  I no longer have a full-time day job.

I am looking for opportunities.

I’d love to hear about projects in geophysics, computational geoscience, data science, machine learning. Feel free to get in touch with me at matteo@mycarta.ca.

Thanks,

Matteo