GeoConvention highlights

We were in Calgary last week at the Canada GeoConvention 2017. The quality of the talks seemed more variable than usual but, as usual, there were some gems in there too. Here are our highlights from the technical talks...

Filling in gaps

Mauricio Sacchi (University of Alberta) outlined a new reconstruction method for vector field data. In other words, filling in gaps in multi-compononent seismic records. I've got a soft spot for Mauricio's relaxed speaking style and the simplicity with which he presents linear algebra, but there are two other reasons that make this talk worthy of a shout out:

1. He didn't just show equations in his talk, he used pseudocode to show the algorithm.
2. He linked to his lab's seismic processing toolkit, SeismicJulia, on GitHub.

I am sure he'd be the first to admit that it is early days for for this library and it is very much under construction. But what isn't? All the more reason to showcase it openly. We all need a lot more of that.

Update on 2017-06-7 13:45 by Evan Bianco: Mauricio, has posted the slides from his talk. 

Learning about errors

Anton Birukov (University of Calgary & graduate intern at Nexen) gave a great talk in the induced seismicity session. It was a lovely mashing-together of three of our favourite topics: seismology, machine-learning, and uncertainty. Anton is researching how to improve microseismic and earthquake event detection by framing it as a machine-learning classification problem. He's using Monte Carlo methods to compute myriad synthetic seismic events by making small velocity variations, and then using those synthetic events to teach a model how to be more accurate about locating earthquakes.

Figure 2 from Anton Biryukov's abstract. An illustration of the signal classification concept. The signals originating from the locations on the grid (a) are then transformed into a feature space and labeled by the class containing the event origin. From Biryukov (2017). Event origin depth uncertainty - estimation and mitigation using waveform similarity. Canada GeoConvention, May 2017.

The bright lights of geothermal energy
Matt Hall

Two interesting sessions clashed on Wednesday afternoon. I started off in the Value of Geophysics panel discussion, but left after James Lamb's report from the mysterious Chief Geophysicists' Forum. I had long wondered what went on in that secretive organization; it turns out they mostly worry about how to make important people like your CEO think geophysics is awesome. But the large room was a little dark, and — in keeping with the conference in general — so was the mood.

Feeling a little down, I went along to the Diversification of the Energy Industry session instead. The contrast was abrupt and profound. The bright room was totally packed with a conspicuously young audience numbering well over 100. The mood was hopeful, exuberant even. People were laughing, but not wistfully or ironically. I think I saw a rainbow over the stage.

If you missed this uplifting session but are interested in contributing to Canada's geothermal energy scene, which will certainly need geoscientists and reservoir engineers if it's going to get anywhere, there are plenty of ways to find out more or get involved. Start at cangea.ca and follow your nose.

We'll be writing more about the geothermal scene — and some of the other themes in this post — so stay tuned. 

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Running away from easy

Matt and I are in Calgary at the 2017 GeoConvention. Instead of writing about highlights from Day 1, I wanted to pick on one awesome thing I saw. Throughout the convention, there is a air of sadness, of nostalgia, of struggle. But I detect a divide among us. There are people who are waiting for things to return to how they were, when life was easy. Others are exploring how to be a part of the change, instead of a victim of it. Things are no longer easy, but easy is boring. 

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Want to start an oil and gas company? What resources are you going to need? Computers, pricey software applications, data. Purchase all of this stuff as a one-time capital expense, build a team, get an office lease, buy desks and a Keurig. Then if all goes well, 18 months later you'll have a slide deck outlining a play that you could pitch to investors. 

Imagine getting started without laying down a huge amount of capital for all those things. What if you could rent a desk at a co-working space, access the suite of software tools that you're used to, and use their Keurig. The computer infrastructure and software is managed and maintained by an IT service company so you don't have to worry about it. 

Yesterday at the Calgary Geoconvention I heard all about ReSourceYYC, a co-working space catering to oil and gas professionals, introduced ResourceNET, a subscription-based cloud workstation environment for freelancers, consultants, startups, and the newly and not-so-newly underemployed community of subsurface professionals.

In making this offering, ReSourceYYC has partnered up with a number of software companies: Entero, Seisware, Surfer, ValNav, geoLOGIC, and Divestco, to name a few. The limitations and restrictions around this environment, if any, weren't totally clear. I wondered: Could I append or swap my own tools with this stack? Can I access this environment from anywhere?

It could be awesome. I think it could serve just as many freelancers and consultants as "oil and gas startups". It seems a bit too early to say, but I reckon there are literally thousands of geoscientists and engineers in Calgary that'd be all over this.

I think it's interesting and important and I hope they get it right.

Two new short courses in Calgary

We're running two one-day courses in Calgary for the CSPG Spring Education Week. One of them is a bit... weird, so I thought I'd try to explain what we're up to.

Both classes run from 8:30 till 4:00, and both of them cost just CAD 425 for CSPG members. 

Get introduced to Python

The first course is Practical programming for geoscientists. Essentially a short version of our 2 to 3 day Creative geocomputing course, we'll take a whirlwind tour through the Python programming language, then spend the afternoon looking at some basic practical projects. It might seem trivial, but leaving with a machine fully loaded with all the tools you'll need, plus long list of resources and learning aids, is worth the price of admission alone.

If you've always wanted to get started with the world's easiest-to-learn programming language, this is the course you've been waiting for!

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Hashtag geoscience

This is the weird one. Hashtag geoscience: communicating geoscience in the 21st century. Join me, Evan, Graham Ganssle (my co-host on Undersampled Radio) — and some special guests — for a one-day sci comm special. Writing papers and giving talks is all so 20th century, so let's explore social media, blogging, podcasting, open access, open peer review, and all the other exciting things that are happening in scientific communication today. These tools will not only help you in your job, you'll find new friends, new ideas, and you might even find new work.

I hope a lot of people come to this event. For one, it supports the CSPG (we're not getting paid, we're on expenses only). Secondly, it'll be way more fun with a crowd. Our goal is for everyone to leave burning to write a blog, record a podcast, or at least create a Twitter account. 

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One of our special guests will be young-and-famous geoscience vlogger Dr Chris. Coincidentally, we just interviewed him on Undersampled Radio. Here's the uncut video version; audio will be on iTunes and Google Play in a couple of days:

Unearthing gold in Toronto

I just got home from Toronto, the mining capital of the world, after an awesome weekend hacking with Diego Castañeda, a recent PhD grad in astrophysics that is working with us) and Anneya Golob (another astrophysicist and Diego's partner). Given how much I bang on about hackathons, it might surprise you to know that this was the first hackathon I have properly participated in, without having to order tacos or run out for more beer every couple of hours.

PArticipants being briefed by one of the problem sponsors on the first evening.

What on earth is Unearthed?

The event (read about it) was part of a global series of hackathons organized by Unearthed Solutions, a deservedly well-funded non-profit based in Australia that is seeking to disrupt every single thing in the natural resources sector. This was their fourteenth event, but their first in Canada. Remarkably, they got 60 or 70 hackers together for the event, which I know from my experience organizing events takes a substantial amount of work. Avid readers might remember us mentioning them before, especially in a guest post by Jelena Markov and Tom Horrocks in 2014.

A key part of Unearthed's strategy is to engage operating companies in the events. Going far beyond mere sponsorship, Barrick Gold sent several mentors to the event, the Chief Innovation Officer Michelle Ash, as well as two judges, Ed Humphries (head of digital transformation) and Iain Allen (head of digital mining). Barrick provided the chellenge themes, as well as data and vivid descriptions of operational challenges. The company was incredibly candid with the participants, and should be applauded for its support of what must have felt like a pretty wild idea. 

Team Auger Effect: Diego and Anneya hacking away on Day 2.

What went down?

It's hard to describe a hackathon to someone who hasn't been to one. It's like trying to describe the Grand Canyon, ice climbing, or a 1985 Viña Tondonia Rioja. It's always fun to see and hear the reactions of the judges and other observers that come for the demos in the last hours of the event: disbelief at what small groups of humans can do in a weekend, for little tangible reward. It flies in the face of everything you think you know about creativity, productivity, motivation, and collaboration. Not to mention intellectual property.

As the fifteen (!) teams made their final 5-minute pitches, it was clear that every single one of them had created something unique and useful. The judges seemed genuinely blown away by the level of accomplishment. It's hard to capture the variety, but I'll have a go with a non-comprehensive list. First, there was a challenge around learning from geoscience data:

• BGC Engineering, one of the few pro teams and First Place winner, produced an impressive set of tools for scraping and analysing public geoscience data. I think it was a suite of desktop tools rather than a web application.
• Mango (winners of the Young Innovators award), Smart Miner (second place overall), Crater Crew, Aureka, and Notifyer and others presented map-based browsers for public mining data, with assistance from varying degrees of machine intelligence.
• Auger Effect (me, Diego, and Anneya) built a three-component system consisting of a browser plugin, an AI pipeline, and a social web app, for gathering, geolocating, and organizing data sources from people as they research.

The other challenge was around predictive maintenance:

• Tyrelyze, recognizing that two people a year are killed by tyre failures, created a concept for laser scanning haul truck tyres during operations. These guys build laser scanners for core, and definitely knew what they were doing.
• Decelerator (winners of the People's Choice award) created a concept for monitoring haul truck driving behaviour, to flag potentially expensive driving habits.
• Snapfix.io looked at inventory management for mine equipment maintenance shops.
• Arcana, Leo & Zhao, and others looked at various other ways of capturing maintenance and performace data from mining equipment, and used various strategies to try to predict 

I will try to write some more about the thing we built... and maybe try to get it working again! The event was immensely fun, and I'm so glad we went. We learned a huge amount about mining too, which was eye-opening. Massive thanks to Unearthed and to Barrick on all fronts. We'll be back!

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Brad BEchtold of Cisco (left) presenting the Young Innovator award for under-25s to Team Mango.

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The winners of the People's Choice Award, Team Decelerate.

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The winners of the contest component of the event, BGC Engineering, with Ed Humphries of Barrick (left).

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UPDATE  View all the results and submissions from the event.

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Wish there was a hackathon just for geoscientists and subsurface engineers?
You're in luck! Join us in Paris for the Subsurface Hackathon — sponsored by Dell EMC, Total E&P, NVIDIA, Teradata, and Sandstone. The theme is machine learning, and registration is open. There's even a bootcamp for anyone who'd like to pick up some skills before the hack.

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.

News and updates and a sandwich

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Plans for the hackathon in Paris in June are well underway. We now have two major sponsors: Dell EMC and now Total E&P too will be supporting the event with generous funding. Bolstered by this, I've set a goal of getting 50 participants in the event. Imagine that!

If you would like to help us reach this goal, please consider printing out some of these posters (right) and putting them up in your place of work or study >> hi-res PDF << It should even be readable in black & white, if that's your only option.

You can find links to everything you need to know about the event at agilescientific.com/paris.

Le grand sandwich délicieux

The hackathon is really just the filling in a delicious Parisian sandwich of geocomputing goodness. The bread at the bottom is the Hacker Bootcamp on 9 June. The filling is the hackathon weekend... and the final piece is the EAGE workshop on machine learning. Convened by geoscientists at Total and IFP, it should be a great day of knowledge sharing and discussion. I can't wait.

11 days to go!

There are only 11 days left to take part in the SEG Machine Learning contest, in which you are challenged to predict lithologies in two wells, given some wireline logs and lithologies in several other nearby wells. Everything you need to get started, even if you've never tried anything like this before, is right here. See Brendon Hall's TLE article for more deets.

The radio show for geo-nerds

Undersampled Radio is still going strong. We just recorded episode 32 today. Last week's chat with Prof Chris Jackson (Imperial College London) — who's embarking on a GSA lecture tour this year — was a real cracker, check it out:

The other thing you need to know about Chris is that he's started writing his blog again. It's awesome, of course, and you should probably just go and read it now...

SEG machine learning contest: there's still time

Have you been looking for an excuse to find out what machine learning is all about? Or maybe learn a bit of Python programming language? If so, you need to check out Brendon Hall's tutorial in the October issue of The Leading Edge. Entitled, "Facies classification using machine learning", it's a walk-through of a basic statistical learning workflow, applied to a small dataset from the Hugoton gas field in Kansas, USA.

But it was also the launch of a strictly fun contest to see who can get the best prediction from the available data. The rules are spelled out in ther contest's README, but in a nutshell, you can use any reproducible workflow you like in Python, R, Julia or Lua, and you must disclose the complete workflow. The idea is that contestants can learn from each other.

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Left: crossplots and histograms of wireline log data, coloured by facies — the idea is to highlight possible data issues, such as highly correlated features. Right: true facies (left) and predicted facies (right) in a validation plot. See the rest of the paper for details.

What's it all about?

The task at hand is to predict sedimentological facies from well logs. Such log-derived facies are sometimes called e-facies. This is a familiar task to many development geoscientists, and there are many, many ways to go about it. In the article, Brendon trains a support vector machine to discriminate between facies. It does a fair job, but the accuracy of the result is less than 50%. The challenge of the contest is to do better.

Indeed, people have already done better; here are the current standings:

	Team	F1	Algorithm	Language	Solution
1	gccrowther	0.580	Random forest	Python	Notebook
2	LA_Team	0.568	DNN	Python	Notebook
3	gganssle	0.561	DNN	Lua	Notebook
4	MandMs	0.552	SVM	Python	Notebook
5	thanish	0.551	Random forest	R	Notebook
6	geoLEARN	0.530	Random forest	Python	Notebook
7	CannedGeo	0.512	SVM	Python	Notebook
8	BrendonHall	0.412	SVM	Python	Initial score in article

As you can see, DNNs (deep neural networks) are, in keeping with the amazing recent advances in the problem-solving capability of this technology, doing very well on this task. Of the 'shallow' methods, random forests are quite prominent, and indeed are a great first-stop for classification problems as they tend to do quite well with little tuning.

How do I enter?

There is still over 6 weeks to enter: you have until 31 January. There is a little overhead — you need to learn a bit about git and GitHub, there's some programming, and of course machine learning is a massive field to get up to speed on — but don't be discouraged. The very first entry was from Bryan Page, a self-described non-programmer who dusted off some basic skills to improve on Brendon's notebook. But you can run the notebook right here in mybinder.org (if it's up today — it's been a bit flaky lately) and a play around with a few parameters yourself.

The contest aspect is definitely low-key. There's no money on the line — just a goody bag of fun prizes and a shedload of kudos that will surely get the winners into some awesome geophysics parties. My hope is that it will encourage you (yes, you) to have fun playing with data and code, trying to do that magical thing: predict geology from geophysical data.

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Reference

Hall, B (2016). Facies classification using machine learning. The Leading Edge 35 (10), 906–909. doi: 10.1190/tle35100906.1. (This paper is open access: you don't have to be an SEG member to read it.)

Le meilleur hackathon du monde

Hackathons are short bursts of creative energy, making things that may or may not turn out to be useful. In general, people work in small teams on new projects with no prior planning. The goal is to find a great idea, then manifest that idea as something that (barely) works, but might not do very much, then show it to other people.

Hackathons are intellectually and professionally invigorating. In my opinion, there's no better team-building, networking, or learning event.

The next event will be 10 & 11 June 2017, right before the EAGE Conference & Exhibition in Paris. I hope you can come.

The theme for this event will be machine learning. We had the same theme in New Orleans in 2015, but suffered a bit from a lack of data. This time we will have a collection of open datasets for participants to build off, and we'll prime hackers with a data-and-skills bootcamp on Friday 9 June. We did this once before in Calgary – it was a lot of fun. 

Can you help?

It's my goal to get 52 participants to this edition of the event. But I'll need your help to get there. Please share this post with any friends or colleagues you think might be up for a weekend of messing about with geoscience data and ideas. 

Other than participants, the other thing we always need is sponsors. So far we have three organizations sponsoring the event — Dell EMC is stepping up once again, thanks to the unstoppable David Holmes and his team. And we welcome Sandstone — thank you to Graham Ganssle, my Undersampled Radio co-host, who I did not coerce in any way.

If your organization might be awesome enough to help make amazing things happen in our community, I'd love to hear from you. There's info for sponsors here.

If you're still unsure what a hackathon is, or what's so great about them, check out my November article in the Recorder (Hall 2015, CSEG Recorder, vol 40, no 9).

PRESS START

The dust has settled from the Subsurface Hackathon 2016 in Vienna, which coincided with EAGE's 78th Conference and Exhibition (some highlights). This post builds on last week's quick summary with more detailed descriptions of the teams and what they worked on. If you want to contact any of the teams, you should be able to track them down via the links to Twitter and/or GitHub.

A word before I launch into the projects. None of the participants had built a game before. Many were relatively new to programming — completely new in one or two cases. Most of the teams were made up of people who had never worked together on a project before; indeed, several team mates had never met before. So get ready to be impressed, maybe even amazed, at what members of our professional community can do in 2 days with only mild provocation and a few snacks.

Traptris

An 8-bit-style video game, complete with music, combining Tetris with basin modeling.

Team: Chris Hamer, Emma Blott, Natt Turner (all MSc students at the University of Leeds), Jesper Dramsch (PhD student, Technical University of Denmark, Copenhagen). GitHub repo.

Tech: Python, with PyGame.

Details: The game is just like Tetris, except that the blocks have lithologies: source, reservoir, and seal. As you complete a row, it disappears, as usual. But in this game, the row reappears on a geological cross-section beside the main game. By completing further rows with just-right combinations of lithologies, you build an earth model. When it's deep enough, and if you've placed sources rocks in the model, the kitchen starts to produce hydrocarbons. These migrate if they can, and are eventually trapped — if you've managed to build a trap, that is. The team impressed the judges with their solid gamplay and boisterous team spirit. Just installing PyGame and building some working code was an impressive feat for the least experienced team of the hackathon.

Prize: We rewarded this rambunctious team for their creative idea, which it's hard to imagine any other set of human beings coming up with. They won Samsung Gear VR headsets, so I'm looking forward to the AR version of the game.

Flappy Trace

A ridiculously addictive seismic interpretation game. "So seismic, much geology".

Team: Håvard Bjerke (Roxar, Oslo), Dario Bendeck (MSc student, Leeds), and Lukas Mosser (PhD student, Imperial College London).

Tech: Python, with PyGame. GitHub repo.

Details: You start with a trace on the left of the screen. More traces arrive, slowly at first, from the right. The controls move the approaching trace up and down, and the pick point is set as it moves across the current trace and off the screen. Gradually, an interpretation is built up. It's like trying to fly along a seismic horizon, one trace at a time. The catch is that the better you get, the faster it goes. All the while, encouragements and admonishments flash up, with images of the doge meme. Just watching someone else play is weirdly mesmerizing.

Prize: The judges wanted to recognize this team for creating such a dynamic, addictive game with real personality. They won DIY Gamer kits and an awesome book on programming Minecraft with Python.

Guess What!

Human seismic inversion. The player must guess the geology that produces a given trace.

Team: Henrique Bueno dos Santos, Carlos Andre (both UNICAMP, Sao Paolo), and Steve Purves (Euclidity, Spain)

Tech: Python web application, on Flask. It even used Agile's nascent geo-plotting library, g3.js, which I am pretty excited about. GitHub repo. You can even play the game online!

Details: This project was on a list of ideas we crowdsourced from the Software Underground Slack, and I really hoped someone would give it a try. The team consisted of a postdoc, a PhD student, and a professional developer, so it's no surprise that they managed a nice implementation. The player is presented with a synthetic seismic trace and must place reflection coefficients that will, she hopes, forward model to match the trace. She may see how she's progressing only a limited number of times before submitting her final answer, which receives a score. There are so many ways to control the game play here, I think there's a lot of scope for this one.

Prize: This team impressed everyone with the far-reaching implications of the game — and the rich possibilities for the future. They were rewarded with SparkFun Digital Sandboxes and a copy of The Thrilling Adventures of Lovelace and Babbage.

DiamondChaser

aka DiamonChaser (sic). A time- and budget-constrained drilling simulator aimed at younger players.

Team: Paul Gabriel, Björn Wieczoreck, Daniel Buse, Georg Semmler, and Jan Gietzel (all at GiGa infosystems, Freiberg)

Tech: TypeScript, which compiles to JS. BitBucket repo. You can play the game online too!

Details: This tight-knit group of colleagues — all professional developers, but using unfamiliar technology — produced an incredibly polished app for the demo. The player is presented with a blank cross section, and some money. After choosing what kind of drill bit to start with, the drilling begins and the subsurface is gradually revealed. The game is then a race against the clock and the ever-diminishing funds, as diamonds and other bonuses are picked up along the way. The team used geological models from various German geological surveys for the subsurface, adding a bit of realism.

Prize: Everyone was impressed with the careful design and polish of the app this team created, and the quiet industry they brought to the event. They each won a CellAssist OBD2 device and a copy of Charles Petzold's Code.

Some of the participants waiting for the judges to finish their deliberations. Standing, from left: Håvard Bjerke, Henrique Bueno dos Santos, Steve Purves. Seated: Jesper Dramsch, Lukas Mosser, Natt Turner, Emma Blott, Dario Bendeck, Carlos André, Björn Wieczoreck, Paul Gabriel.

Credits and acknowledgments

Thank you to all the hackers for stepping into the unknown and coming along to the event. I think it was everyone's first hackathon. It was an honour to meet everyone. Special thanks to Jesper Dramsch for all the help on the organizational side, and to Dragan Brankovic for taking care of the photography.

The Impact HUB Vienna was a terrific venue, providing us with multiple event spaces and plenty of room to spread out. HUB hosts Steliana and Laschandre were a great help. Der Mann produced the breakfasts. Il Mare pizzeria provided lunch on Saturday, and Maschu Maschu on Sunday.

Thank you to Kristofer Tingdahl, CEO of dGB Earth Sciences and a highly technical, as well as thoughtful, geoscientist. He graciously agreed to act as a judge for the demos, and I think he was most impressed with the quality of the teams' projects.

Last but far from least, a huge Thank You to the sponsor of the event, EMC, the cloud computing firm that was acquired by Dell late last year. David Holmes, the company's CTO (Energy) was also a judge, making an amazing opportunity for the hackers to show off their skills, and sense of humour, to a progressive company with big plans for our industry.

Automated interpretation highlights

As you probably know by know, I've been at the EAGE conference in Vienna this week. I skipped out yesterday and flew over to the UK for a few days. I have already written plenty about the open source workshop, and I will write more soon about the hackathon. But I thought I'd pass on my highlights from the the Automated Interpretation session on Tuesday. In light of Monday's discussion, I made a little bit of a nuisance of myself by asking the same post-paper question every time I got the chance:

Can I use your code, either commercially or for free?

I'll tell you what the authors responded.

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The universal character of salt

I especially enjoyed the presentation by Anders Waldeland and Anne Solberg (University of Oslo) on automatically detecting salt in 3D seismic. (We've reported on Anne Solberg's work before.) Anders described training eight different classifiers, from a simple nearest mean to a neural network, a supprt vector model, and a mixture of Gaussians classifier. Interestingly, but not surprisingly, the simplest model turned out to be the most effective at discrimination. He also tried a great many seismic attributes, letting the model choose the best ones. Three attributes consistently proved most useful: coherency, Haralick energy (a GLCM-based texture attribute), and the variance of the kurtosis of the amplitude distribution (how's that for meta?). What was especially interesting about his approach was that he was training the models on one dataset, and predicting on an entirely different 3D. The idea is that this might reveal the universal seismic characteristics of salt. When I asked the golden question, he said "Come and talk to me", which isn't a "yes", but it isn't a "no" either.

Waldeland and Solberg 2016. Salt probability in a North Sea dataset (left) and the fully tracked volume (right). The prediction model was trained on a Gulf of Mexico dataset. Copyright of the authors and EAGE, and used under a Fair Use claim.

Secret horizon tracker

Horizons tracked with Figueiredo et al's machine learning algorithm. The horizons correctly capture the discontinuities. Copyright of the authors and EAGE. Used under a Fair Use claim.

The most substantial piece of machine learning I saw was Eduardo Figueiredo from Pontifical Catholic University in Rio, in the same session as Waldeland. He's using a neural net called Growing Neural Gas to classify (aka or 'label') the input data in a number of different ways. This training step takes a little time. The label sets can now be compared to decide on the similarity between two samples, based on the number of labels the samples have in common but also including a comparison to the original seed, which essentially acts as a sort of brake to stop things running away. This progresses the pick. If a decision can't be reached, a new global seed is selected randomly. If that doesn't work, picking stops. Unfortunately he did not show a comparison to an ordinary autotracker, either in terms of time or quality, but the results did look quite good. The work was done 'in cooperation with Petrobras', so it's not surprising the code is not available. I was a bit surprised that Figueiredo was even unable to share any details of the implementation.

More on interpretation

The other two interesting talks in the session were two from Paul de Groot (dGB Earth Sciences) and Gaynor Paton (GeoTeric). Paul introduced the new Thalweg Tracker in OpendTect — the only piece of software from the session that you can actually run, albeit for a fee — which is a sort of conservative voxel tracker. Unsurprisingly, Paul was also very thorough with his examples, and his talk served as a tutorial in how to make use of, and give attribution to, open data. (I'm nearly done with the grumbling about openness for now, I promise, but I can't help mentioning that I find it a bit ironic that those scientists unwilling to share their work are also often a bit lax with giving credit to others whose work they depend on.)

Gaynor's talk was about colour, which you may know we enjoy thinking about. She had gathered 24 seismic interpreters, five of whom had some form of colour deficiency. She gave the group some interpretation tasks, and tried to gauge their performance in the tasks. It seemed interesting enough, and I immediately wondered if we could help out with Pick This, especially to help grow the sample size, and by blinding the study. But the conclusion seemed to be that, if there are ways in which colour blind interpreters are less capable at image interpretation, for example where hue is important, they compensate for it by interpreting other aspects, such as contrast and shape. 

Paton's research into how colour deficient people interpret attributes. There were 5 colour deficient subjects and 19 colour normal. The colour deficient subjects were more senstive to subtle changes in saturation and to feature shapes. Image copyright Paton and EAGE, and used here under a fair use claim.

That's it for now. I have a few other highlights to share; I'll try to get to them next week. There was a bit of buzz around the Seismic Apparition talks from ETHZ and Statoil, for example. If you were at the conference, I'd love to hear your highlights too, please share them in the comments.

References

A.U. Waldeland* (University of Oslo) & A.H.S. Solberg (University of Oslo). 3D Attributes and Classification of Salt Bodies on Unlabelled Datasets. 78th EAGE Conference & Exhibition 2016. DOI 10.3997/2214-4609.201600880. Available in EarthDoc.

M. Pelissier (Dagang Zhaodong Oil Company), C. Yu (Dagang Zhaodong Oil Company), R. Singh (dGB Earth Sciences), F. Qayyum (dGB Earth Sciences), P. de Groot* (dGB Earth Sciences) & V. Romanova (dGB Earth Sciences). Thalweg Tracker - A Voxel-based Auto-tracker to Map Channels and Associated Margins. 78th EAGE Conference & Exhibition 2016. DOI 10.3997/2214-4609.201600879. Available in EarthDoc. 

G. Paton* (GeoTeric). The Effect of Colour Blindness on Seismic Interpretation. 78th EAGE Conference & Exhibition 2016. DOI 10.3997/2214-4609.201600883. Available in EarthDoc.

A.M. Figueiredo* (Tecgraf / PUC-Rio), J.P. Peçanha (Tecgraf / PUC-Rio), G.M. Faustino (Tecgraf / PUC-Rio), P.M. Silva (Tecgraf / PUC-Rio) & M. Gattass (Tecgraf / PUC-Rio). High Quality Horizon Mapping Using Clustering Algorithms. 78th EAGE Conference & Exhibition 2016. DOI 10.3997/2214-4609.201600878. Available in EarthDoc.