2016-ml-contest

Welcome to the Geophysical Tutorial Machine Learning Contest 2016! Read all about the contest in the October 2016 issue of the magazine. Look for Brendon Hall's tutorial on lithology prediction with machine learning.

You can run the notebooks in this repo in the cloud, just click the badge below:

You can also clone or download this repo with the green button above, or just read the documents:

• index.ipynb — All about the contest.
• Facies_classification.ipynb — Brendon's notebook with all the code you need to get started in machine learning in Python.

Leaderboard

F1 scores of models against secret blind data in the STUART and CRAWFORD wells. The logs for those wells are available in the repo, but contestants do not have access to the facies.

Please note that after the contest closes, we will be applying a stochastic scoring approach to the leading models. So these scores are subject to change.

	Team	F1	Algorithm	Language	Solution
1	SHandPR	0.631	Boosted trees	Python	Notebook
2	HouMath	0.630	Boosted trees	Python	Notebook
3	ar4	0.606	Random forest	Python	Notebook
4	bestagini	0.604	Random forest	Python	Notebook
5	LA_Team	0.5991	Boosted trees	Python	Notebook
6	Bird Team	0.5981	Random forest	Python	Notebook
7	geoLEARN	0.594	Random forest	Python	Notebook
8	gccrowther	0.589	Random forest	Python	Notebook
9	PA Team	0.585	Deep neural net	Python	Notebook
10	thanish	0.580	Random forest	R	Code
	MandMs	0.579	Majority voting	Python	Notebook
	PA Team	0.573	Deep neural net	Python	Notebook
	kr1m	0.570	AdaBoosted trees	Python	Notebook
	ShiangYong	0.570	ConvNet	Python	Notebook
	fvf1361	0.568	Majority voting	Python	Notebook
	gganssle	0.561	Deep neural net	Lua	Notebook
	CarlosFuerte	0.561	Multilayer perceptron	Python	Notebook
	evgenizer	0.561	Majority voting	Python	Notebook
	wouterk1MSS	0.557	Random forest	Python	Notebook
	CarthyCraft	0.552	Boosted trees	Python	Notebook
	CEsprey	0.550	Majority voting	Python	Notebook
	osorensen	0.549	Boosted trees	R	Notebook
	JesperDramsch	0.530	Random forest	Python	Notebook
	BGC_Team	0.519	DNN	Python	Notebook
	CannedGeo	0.512	Support vector machine	Python	Notebook
	ARANZGeo	0.511	DNN	Python	Code
	daghra	0.506	k-nearest neighbours	Python	Notebook
	jpoirier	0.469	Custom	Python	Notebook
	BrendonHall	0.412	Support vector machine	Python	Initial score in article

¹ Pending complete validation. This usually takes us a few days.

Getting started with Python

Please refer to the User guide to the geophysical tutorials for tips on getting started in Python and find out more about Jupyter notebooks.

Find out more about the contest

If you intend to enter this contest, I suggest you check the open issues and read through the closed issues too. There's some good info in there.

To find out more please read the article in the October issue or read the manuscript in the tutorials-2016 repo.

Rules

We've never done anything like this before, so there's a good chance these rules will become clearer as we go. We aim to be fair at all times, and reserve the right to make judgment calls for dealing with unforeseen circumstances.

IMPORTANT: When this contest was first published, we asked you to hold the SHANKLE well blind. This is no longer necessary. You can use all the published wells in your training. Related: I am removing the file of predicted facies for the STUART and CRAWFORD wells, to reduce confusion — they are not actual facies, only those predicted by Brendon's first model.

• You must submit your result as code and we must be able to run your code.
• Entries will be scored by a comparison against known facies in the STUART and CRAWFORD wells, which do not have labels in the contest dataset. We will use the F1 cross-validation score. See issue #2 regarding this point. The scores in the 'leaderboard' reflect this.
• Where there is stochastic variance in the predictions, the median average of 100 realizations will be used as the cross-validation score. See issue #114 regarding this point. The scores in the leaderboard do not currently reflect this. Probably only the top entries will be scored in this way. [updated 23 Jan]
• The result we get with your code is the one that counts as your result.
• To make it more likely that we can run it, your code must be written in Python or R or Julia or Lua [updated 26 Oct].
• The contest is over at 23:59:59 UT (i.e. midnight in London, UK) on 31 January 2017. Pull requests made aftetr that time won't be eligible for the contest.
• If you can do even better with code you don't wish to share fully, that's really cool, nice work! But you can't enter it for the contest. We invite you to share your result through your blog or other channels... maybe a paper in The Leading Edge.
• This document and documents it links to will be the channel for communication of the leading solution and everything else about the contest.
• This document contains the rules. Our decision is final. No purchase necessary. Please exploit artificial intelligence responsibly.

Licenses

Please note that the dataset is not openly licensed. We are working on this, but for now please treat it as proprietary. It is shared here exclusively for use on this problem, in this contest. We hope to have news about this in early 2017, if not before.

All code is the property of its author and subject to the terms of their choosing. If in doubt — ask them.

The information about the contest, and the original article, and everything in this repo published under the auspices of SEG, is licensed CC-BY and OK to use with attribution.