Looking forward to EAGE

Evan, Diego and I are flying to Paris today for the EAGE Conference and Exhibition. It's exciting. We're excited. 

But the excitement starts before the conference. The Subsurface Hackathon is this weekend!

My diary

Even the hackathon excitement starts before the weekend, because tomorrow, Friday, we're running the hacker's bootcamp — a sort of short course appetizer for the hackathon. We have about 25 geoscientists coming to the Booster TOTAL (an event space at TOTAL's La Défense offices) to get some hands-on practice with Python and the latest in machine learning tools. It's especially exciting because we'll also have engineers from NVIDIA on hand to help with the coaching. The idea is to help people hit the ground running when the hackathon starts on Saturday.

After that, on Saturday and Sunday,  it's the hackathon itself. We have no fewer than 60 geoscientists and engineers registered for this breakout event. They're coming to the Booster to work on a wide array of machine learning ideas for the subsurface. It's going to be epic. You can read all about what happens next week, I promise. 

Then on Monday it's the Data Science for Geoscience workshop, at which I'm giving a keynote. Since I'm far from possessing expertise, I'm using it as a chance to get people jazzed about helping make the coming AI revolution in geoscience a positive experience. I'm really looking forward to it.

The conference itself starts on Tuesday. In the afternoon I'm co-chairing a session on machine learning (have you spotted the theme yet?) in seismic interpretation, along with Victor Aare of Schlumberger. It will be awesome to see what kind of progress our community is making in this field — it's fun to imagine what seismic interpretation might be like in a few years. There are so many fascinating problems to work on! Here are the talks in that session:

• H Di, M Shafiq and G AlRegib (Georgia Institute of Technology), Multi-attribute K-means Cluster Analysis for Salt Boundary Detection
• H Hashemi et al. (Institute of Geophysics, University of Tehran), Clustering Seismic Datasets for Optimized Facies Analysis Using a SSCSOM Technique
• S Hadiloo et al. (Research Institute of Applied Sciences, ACECR), Seismic Facies Analysis by ANFIS and Fuzzy Clustering Methods to Extract Channel Patterns
• A Waldeland and A Solberg (University of Oslo), Salt Classification Using Deep Learning
• H Di & G AlRegib (Georgia Institute of Technology), Seismic Multi-attribute Classification for Salt Boundary Detection - A Comparison
• P Xu, W Lu and B Wang (Tsinghua University), Multi-Attribute Classification Based On Sparse Autoencoder - A Gas Chimney Detection Example
• Y Alaudah and G AlRegib (Georgia Institute of Technology), Weakly Supervised Seismic Structure Labeling via Orthogonal Non-Negative Matrix Factorization
• J Amtmann et al. (University of Leoben/ Geo5 GmbH), Testing of Clustering Algorithms on Different 3D Seismic Models
• R Noemani Rad and G Gharabeigli (NIOC Research & Technology Directorate), Half Graben Evolution in the Kopet Dagh Fold-and-thrust Belt - Sedimentation and Pale Current History

On Wednesday we'll be taking in some more talks and posters, then in the afternoon I'm reprising my keynote talk at IFPEN, a subsurface research institute in the Bois de Boulogne. I've never been there before, although I have met a few IFP scientists before. I'm looking forward to it very much. 

It all ends for us on Thursday. Evan and Diego fly home and I'm off to Cambridge (the old one in the fens, not the one in Massachusetts) for a few days with family (and bookshops). Until then, expect much blogging!

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Going to EAGE?

If you're reading this and would like to meet up with us at Agile or some of the Software Underground crowd — the friendliest bunch of coding geoscientists you could hope for — let's plan to meet at the end of the workshop, at the workshop location. Look for the Software Underground shirts.

No secret codes: announcing the winners

The SEG / Agile / Enthought Machine Learning Contest ended on Tuesday at midnight UTC. We set readers of The Leading Edge the challenge of beating the lithology prediction in October's tutorial by Brendon Hall. Forty teams, mostly of 1 or 2 people, entered the contest, submitting several hundred entries between them. Deadlines are so interesting: it took a month to get the first entry, and I received 4 in the second month. Then I got 83 in the last twenty-four hours of the contest.

How it ended

	Team	F1	Algorithm	Language	Solution
1	LA_Team (Mosser, de la Fuente)	0.6388	Boosted trees	Python	Notebook
2	PA Team (PetroAnalytix)	0.6250	Boosted trees	Python	Notebook
3	ispl (Bestagini, Tuparo, Lipari)	0.6231	Boosted trees	Python	Notebook
4	esaTeam (Earth Analytics)	0.6225	Boosted trees	Python	Notebook

The winners are a pair of graduate petroelum engineers, Lukas Mosser (Imperial College, London) and Alfredo de la Fuente (Wolfram Research, Peru). Not coincidentally, they were also one of the more, er, energetic teams — it's say to say that they explored a good deal of the solution space. They were also very much part of the discussion about the contest on GitHub.com and on the Software Underground Slack chat group, aka Swung (you're in there, right?).

I will be sending Raspberry Shakes to the winners, along with some other swag from Enthought and Agile. The second-place team will receive books from SEG (thank you SEG Book Mart!), and the third-place team will have to content themselves with swag. That team, led by Paolo Bestagini of the Politecnico di Milano, deserves special mention — their feature engineering approach was very influential, being used by most of the top-ranking teams.

Coincidentally Gram and I talked to Lukas on Undersampled Radio this week:

Back up a sec, what the heck is a machine learning contest?

To enter, a team had to predict the lithologies in two wells, given wireline logs and other data. They had complete data, including lithologies, in nine other wells — the 'training' data. Teams trained a wide variety of models — from simple nearest neighbour models and support vector machines, to sophisticated deep neural networks and random forests. These met with varying success, with accuracies ranging between about 0.4 and 0.65 (i.e., error rates from 60% to 35%). Here's one of the best realizations from the winning model:

One twist that made the contest especially interesting was that teams could not just submit their predictions — they had to submit the code that made the prediction, in the open, for all their fellow competitors to see. As a result, others were quickly able to adopt successful strategies, and I'm certain the final result was better than it would have been with secret code.

I spent most of yesterday scoring the top entries by generating 100 realizations of the models. This was suggested by the competitors themselves as a way to deal with model variance. This was made a little easier by the fact that all of the top-ranked teams used the same language — Python — and the same type of model: extreme gradient boosted trees. (It's possible that the homogeneity of the top entries was a negative consequence of the open format of the contest... or maybe it just worked better than anything else.)

What now?

There will be more like this. It will have something to do with seismic data. I hope I have something to announce soon.

I (or, preferably, someone else) could write an entire thesis on learnings from this contest. I am busy writing a short article for next month's Leading Edge, so if you're interested in reading more, stay tuned for that. And I'm sure there wil be others.

If you took part in the contest, please leave a comment telling about your experience of it or, better yet, write a blog post somewhere and point us to it.

Hard things that look easy

After working on a few data science (aka data analytics aka machine learning) problems with geoscientific data, I think we've figured out the 10-step workflow. I'm happy to share it with you now:

1. Look at all these cool problems, machine learning can solve all of these! I just need to figure out which model to use, parameterize it, and IT'S GONNA BE AWESOME, WE'LL BE RICH. Let's just have a quick look at the data...
2. Oh, there's no data.
3. Three months later: we have data! Oh, the data's a bit messy.
4. Six months later: wow, cleaning the data is gross and/or impossible. I hate my life.
5. Finally, nice clean data. Now, which model do I choose? How do I set parameters? At least you expected these problems. These are well-known problems.
6. Wait, maybe there are physical laws governing this natural system... oh well, the model will learn them.
7. Hmm, the results are so-so. I guess it's harder to make predictions than I thought it would be.
8. Six months later: OK, this sort of works. And people think it sounds cool. They just need a quick explanation.
9. No-one understands what I've done.
10. Where is everybody?

I'm being facetious of course, but only a bit. Modeling natural systems is really hard. Much harder for the earth than for, say, the human body, which is extremely well-known and readily available for inspection. Even the weather is comparitively easy.

Coupled with the extreme difficulty of the problem, we have a challenging data environment. Proprietary, heterogeneous, poor quality, lost, non-digital... There are lots of ways the data goblins can poop on the playground of machine learning.

If the machine learning lark is so hard, why not just leave it to non-artificial intelligence — humans. We already learned how to interpret data, right? We know the model takes years to train. Of course, but I don't accept that we couldn't use some of the features of intelligently applied big data analytics: objectivity, transparency, repeatability (by me), reproducibility (by others), massive scale, high speed... maybe even error tolerance and improved decisions, but those seem far off right now.

I also believe that AI models, like any software, can encode the wisdom of professionals — before they retire. This seems urgent, as the long-touted Great Crew Change is finally underway.

What will we work on?

There are lots of fascinating and tractable problems for machine learning to attack in geoscience — I hope many of them get attacked at the hackathon in June — and the next 2 to 3 years are going to be very exciting. There will be the usual marketing melée to wade through, but it's up to the community of scientists and data analysts to push their way through that with real results based on open data and, ideally with open code.

To be sure, this is happening already — we've had over 25 entrants publishing their solutions to the SEG machine learning contest already, and there will be more like this. It's the only way to building transparent problem-solving systems that we can all participate in and, ultimately, trust.

What machine learning problems are most pressing in geoscience?
I'm collecting ideas for projects to tackle in the hackathon. Please visit this Tricider question and contribute your comments, opinions, or ideas of your own. Help the community work on the problems you care about.

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Image is licensed CC-BY Ben Rimes on Flickr