Woo yeah perfect: hacking in Salt Lake City

Thirty geoscientist-coders swarmed into Salt Lake City this past weekend to hack at Church & State, a co-working space in a converted church. There, we spent two days appealing to the almighty power of machine learning.

Nine teams worked on the usual rich variety of projects around the theme. Projects included AIs that pick unconformities, natural language processing to describe stratigraphy, and designing an open data platform in service of machine learning. 

I'll do a run-down of the projects soon, but if you can't wait until then for my summary, you can watch the demos here; the first presentation starts at the 38 minute mark of the video. And you can check out some pictures from the event:

Pictures can say a lot but a few simple words, chosen at the right time, can speak volumes too. Shortly before we launched the demos, we asked the participants to choose words that best described how they were feeling. Here's what we got:

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Each participant was able to submit three responses, and although we aren't able to tell who said what, we were able to scrape the data and look at each person's chosen triplet of words. A couple of noteworthy ones were: educated, naptimeinspired and the expressive woo, yeah, perfect. But my personal favorite, by far, has to be the combination of: dead, defeated, inspired.

The creative process can be a rollercoaster of emotions. It's not easy. It's not always comfortable. Things don't always work out. But that's entirely ok. Indeed, facing up to this discomfort, as individuals and as organizations, is a necesary step in the path to digital transformation.

Enough Zen! To all the participants who put in the hard work this weekend, and to our wonderful sponsors who brought all kinds of support, I thank you and I salute you.

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This year's social coding events

If you've always wondered what goes on at our hackathons, make 2018 the year you find out. There'll be plenty of opportunities. We'll be popping up in Salt Lake City, right before the AAPG annual meeting, then again in Copenhagen, before EAGE. We're also running events at the AAPG and EAGE meetings. Later, in the autumn, we'll be making some things happen around SEG too. 

If you just want to go sign up right now, head to the Events page. If you want more deets first, read on.

Salt Lake City in May: machine learning and stratigraphy

This will be one of our 'traditional' hackathons. We're looking for 7 or 8 teams of four to come and dream up, then hack on, new ideas in geostatistics and machine learning, especially around the theme of stratigraphy. Not a coder? No worries! Come along to the bootcamp on Friday 18 May and acquire some new skills. Or just show up and be a brainstormer, tester, designer, or presenter.

Thank you to Earth Analytics for sponsoring this event. If you'd like to sponsor it too, check out your options. The bottom line is that these events cost about $20,000 to put on, so we appreciate all the help we can get. 

It doesn't stop with the hackathon demos on Sunday. At the AAPG ACE, Matt is part of the team bringing you the Machine Learning Unsession on Wednesday afternoon. If you're interested in the future of computation and geoscience, come along and be heard. It wouldn't be the same without you.

Copenhagen in June: visualization and interaction

After events in Vienna in 2016 and Paris in 2017, we're looking forward to being back in Europe in June. The weekend before the EAGE conference, we'll be hosting the Subsurface Hackathon once again. Partnering with Dell EMC and Total E&P, as last year, we'll be gathering 60 eager geoscientists to explore data visualization, from plotting to virtual reality. I can't wait.

In the EAGE Exhibition itself, we're cooking up something else entirely. The Codeshow is a new kind of conference event, mixing coding tutorials with demos from the hackathon and even some mini-hackathon projects to get you started on your own. It's 100% experimental, just the way we like it.

Anaheim in October: something exciting

We'll be at SEG in Anaheim this year, in the middle of October. No idea what exactly we'll be up to, but there'll be a hackathon for sure (sign up for alerts here). And tacos, lots of those. 

You can get tickets to most of these events on the Event page. If you have ideas for future events, or questions about them, drop us a line or leave a comment on this post!


I'll leave you with a short and belated look at the hackathon in Paris last year...

A quick look at the Subsurface Hackathon in Paris, June 2017. 

Hacking in Houston

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Houston 2013
Houston 2014
Denver 2014
Calgary 2015
New Orleans 2015
Vienna 2016
Paris 2017
Houston 2017... The eighth geoscience hackathon landed last weekend!

We spent last weekend in hot, humid Houston, hacking away with a crowd of geoscience and technology enthusiasts. Thirty-eight hackers joined us on the top-floor coworking space, Station Houston, for fun and games and code. And tacos.

Here's a rundown of the teams and what they worked on.

Seismic Imagers

Jingbo Liu (CGG), Zohreh Souri (University of Houston).

Tech — DCGAN in Tensorflow, Amazon AWS EC2 compute.

The team looked for patterns that make seismic data different from other images, using a deep convolutional generative adversarial network (DCGAN). Using a seismic volume and a set of 2D lines, they made 121,000 sub-images (tiles) for their training set.

The Young And The RasLAS

William Sanger (Schlumberger), Chance Sanger (Museum of Fine Arts, Houston), Diego Castañeda (Agile), Suman Gautam (Schlumberger), Lanre Aboaba (University of Arkansas).

State of the art text detection by Google Cloud Vision API

State of the art text detection by Google Cloud Vision API

Tech — Google Cloud Vision API, Python flask web app, Scatteract (sort of). Repo on GitHub.

Digitizing well logs is a common industry task, and current methods require a lot of manual intervention. The team's automated pipeline: convert PDF files to images, perform OCR with Google Cloud Vision API to extract headers and log track labels, pick curves using a CNN in TensorFlow. The team implemented the workflow in a Python flask front-end. Check out their slides.

Hutton Rocks

Kamal Hami-Eddine (Paradigm), Didi Ooi (University of Bristol), James Lowell (GeoTeric), Vikram Sen (Anadarko), Dawn Jobe (Aramco).

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Tech — Amazon Echo Dot, Amazon AWS (RDS, Lambda).

The team built Hutton, a cloud-based cognitive assistant for gaining more efficient, better insights from geologic data. Project includes integrated cloud-hosted database, interactive web application for uploading new data, and a cognitive assistant for voice queries. Hutton builds upon existing Amazon Alexa skills. Check out their GitHub repo, and slides.

Big data > Big Lore 

Licheng Zhang (CGG), Zhenzhen Zhong (CGG), Justin Gosses (Valador/NASA), Jonathan Parker (Marathon)

The team used machine learning to predict formation tops on wireline logs, which would allow for rapid generation of structure maps for exploration play evaluation, save man hours and assist in difficuly formation-top correlations. The team used the AER Athabasca open dataset of 2193 wells (yay, open data!).

Tech — Jupyter Notebooks, SciPy, scikit-learn. Repo on GitHub.

Free near surface

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Tien-Huei Wang, Jing Wu, Clement Zhang (Schlumberger).

Multiples are a kind of undesired seismic signal and take expensive modeling to remove. The project used machine learning to identify multiples in seismic images. They attempted to use GAN frameworks, but found it difficult to formulate their problem, turning instead to the simpler problem of binary classification. Check out their slides.

Tech — CNN... I don't know the framework.

The Cowboyz

Mingliang Liu, Mohit Ayani, Xiaozheng Lang, Wei Wang (University of Wyoming), Vidal Gonzalez (Universidad Simón Bolívar, Venezuela).

A tight group of researchers joined us from the University of Wyoming at Laramie, and snagged one of the most enthusiastic hackers at the event, a student from Venezuela called Vidal. The team attempted acceleration of geostatistical seismic inversion using TensorFlow, a central theme in Mingliang's research.

Tech — TensorFlow.

Augur.ai

Altay Sensal (Geokinetics), Yan Zaretskiy (Aramco), Ben Lasscock (Geokinetics), Colin Sturm (Apache), Brendon Hall (Enthought).

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Electrical submersible pumps (ESPs) are critical components for oil production. When they fail, they can cause significant down time. Augur.ai provides tools to analyze pump sensor data to predict when pumps when pump are behaving irregularly. Check out their presentation!

Tech — Amazon AWS EC2 and EFS, Plotly Dash, SigOpt, scikit-learn. Repo on GitHub.

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The Disaster Masters

Joe Kington (Planet), Brendan Sullivan (Chevron), Matthew Bauer (CSM), Michael Harty (Oxy), Johnathan Fry (Chevron)

Hydrologic models predict floodplain flooding, but not local street flooding. Can we predict street flooding from LiDAR elevation data, conditioned with citizen-reported street and house flooding from U-Flood? Maybe! Check out their slides.

Tech — Python geospatial and machine learning stacks: rasterio, shapely, scipy.ndimage, scikit-learn. Repo on GitHub.

The structure does WHAT?!

Chris Ennen (White Oak), Nanne Hemstra (dGB Earth Sciences), Nate Suurmeyer (Shell), Jacob Foshee (Durwella).

Inspired by the concept of an iPhone 'face ageing' app, Nate recruited a team to poke at applying the concept to maps of the subsurface. Think of a simple map of a structural field early in its life, compared to how it looks after years of interpretation and drilling. Maybe we can preview the 'aged' appearance to help plan where best to drill next to reduce uncertainty!

Tech — OpendTect, Azure ML Studio, C#, self-boosting forest cluster. Repo on GitHub.


Thank you!

Massive thanks to our sponsors — including Pioneer Natural Resources — for their part in bringing the event to life! 

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More thank-yous

Apart from the participants themselves, Evan and I benefitted from a team of technical support, mentors, and judges — huge thanks to all these folks:

  • The indefatigable David Holmes from Dell EMC. The man is a legend.
  • Andrea Cortis from Pioneer Natural Resources.
  • Francois Courteille and Issam Said of NVIDIA.
  • Carlos Castro, Sunny Sunkara, Dennis Cherian, Mike Lapidakis, Jit Biswas, and Rohan Mathews of Amazon AWS.
  • Maneesh Bhide and Steven Tartakovsky of SigOpt.
  • Dave Nichols and Aria Abubakar of Schlumberger.
  • Eric Jones from Enthought.
  • Emmanuel Gringarten from Paradigm.
  • Frances Buhay and Brendon Hall for help with catering and logistics.
  • The team at Station for accommodating us.
  • Frank's Pizza, Tacos-a-Go-Go, Cali Sandwich (banh mi), Abby's Cafe (bagels), and Freebird (burritos) for feeding us.

Finally, megathanks to Gram Ganssle, my Undersampled Radio co-host. Stalwart hack supporter and uber-fixer, Gram came over all the way from New Orleans to help teams make sense of deep learning architectures and generally smooth things over. We recorded an episode of UR at the hackathon, talking to Dawn Jobe, Joe Kington, and Colin Sturm about their respective projects. Check it out!


[Update, 29 Sep & 3 Nov] Some statistics from the event:

  • 39 participants, including 7 women (way too few, but better than 4 out of 63 in Paris)
  • 9 students (and 0 professors!).
  • 12 people from petroleum companies.
  • 18 people from service and technology companies, including 5 from Schlumberger!
  • 13 no-shows, not including folk who cancelled ahead of time; a bit frustrating because we had a long wait list.
  • Furthest travelled: James Lowell from Newcastle, UK — 7560 km!
  • 98 tacos, 67 burritos, 96 slices of pizza, 55 kolaches, and an untold number of banh mi.

Looking ahead to SEG

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The SEG Annual Meeting is coming up. Next week sees the festival of geophysics return to the global energy capital, shaken and damp but undefeated after its recent battle with Hurricane Harvey. Even though Agile will not be at the meeting this year, I wanted to point out some highlights of the week.

The Annual Meeting

The meeting will be big, as usual: 108 talk sessions, and 50 poster and e-presentation sessions. I have no idea how many presentations we're talking about but suffice to say that there's a lot. Naturally, there's a machine learning session, with the following talks:

The Geophysics Hackathon

Even though we're not at the conference, we are in Houston this weekend — for the latest edition of the Geophysics Hackathon! The focus was set to be firmly on 'machine learning', but after the hurricane, we added the theme of 'disaster recovery and mitigation'. People are completely free to choose whatever project they'd like to work on; we'll be ready to help and advise on both topics. We also have some cool gear to play with: a Dell C4130 with 4 x NVIDIA P100s, NVIDIA Jetson TX1s, Amazon Echo Dots, and a Raspberry Shake. Many, many thanks to Dell EMC and Pioneer Natural Resources and all our other sponsors:

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If you're one of the 70 or so people coming to this event, I'm looking forward to seeing you there... if you're not, then I'm looking forward to telling you all about it next week.


Petrel User Group

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Jacob Foshee and Durwella are hosting a Petrel User Group meetup at The Dogwood, which is in midtown (not far from downtown). If you're a user of Petrel — power user or beginner, it doesn't matter — and you're interested in making the most of technology, it'd be good to see you there. Apart from anything else, you'll get to meet Jacob, who is one of those people with technology superpowers that you never know when you might need.


Rock Physics Reception

Tuesday If you've never been to the famous Rock Physics Reception, then you're missing out. It's your best shot at bumping into the luminaries of rock physics — Colin Sayers, Stefan Gelinsky, Per Avseth, Marco Perez, Bill Goodway, Tad Smith — you know the sort of thing. If the first thing you think about when you wake up in the morning is Lamé's second parameter, RSVP right now. Hurry: there are only a handful of spots left.


There's more! Don't miss:

  • The Women's Network Breakfast on Wednesday.
  • The Wiki Committee meeting on Wednesday, 8:00 am, Hilton Room 344B.
  • If you're an SEG member, you can go to any committee meeting you like! Find one that matches your interests.

If you know of any other events, please drop them in the comments!

 

Newsflash: the Geophysics Hackathon is back!

Mark your calendar: 22–24 September (right before SEG), at a downtown Houston location to be confirmed.

We're filling the room with 50 geoscientists of all stripes. Interpreters, programmers, students, professionals... everyone is welcome. The plan: to imagine, design, and prototype some new tools in geophysics — all around the theme of machine learning. It's going to be awesome. 

The schedule: we'll get started at 6 pm on Friday 22 September, and go till 10 pm. Then we pick it up again on Saturday morning, and go till 6 pm, and the same again on Sunday. Teams will present a demo to everyone on Sunday after 3 pm. There will be a few prizes, a few drinks, lots of food, and a lot of new geophysical tools and widgets. 

If you want to know more about what a hackathon is, read my summary from the last one: Le grand hack! Or check out the project round-up posts, part 1 and part 2.

If you're not sure you belong, I promise that you do. One of the prize-winning teams in Paris had no coding experience! And every team needs help with brainstorming, design, testing, and presentation. Absolutely anyone can contribute, and absolutely everyone will learn something.

If you have some like-minded friends, bring them along! We need teams of 5 people, so if there are already 5 of you, you can start coding as soon as you walk in the door!

If you can't be there yourself, please share this post with someone you know.

When you're ready, click here to buy a ticket.


Thank you as always to our sponsors so far: Dell EMC and Amazon AWS. If you'd like to sponsor the Houston event, please check this page out, or just get in touch.

Subsurface Hackathon project round-up, part 2

Following on from Part 1 yesterday, here are the other seven team projects from the hackathon:


Interactive visualization of Water Table heights over many years.

Interactive visualization of Water Table heights over many years.

Water, water everywhere

Water Underground: Martin Bentley (NMMU), Joseph Barraud (Rolls Royce), Rabah Cheknoun (UPPA)

The team built readers for the groundwater data available from dinoloket.nl, both the groundwater levels and the hydrochemistry. They clustered the data by aggregating by month and then looking for similarities in levels in the boreholes and built an open Jupyter notebook.


  

 

 

Seismic from noise

OBSNoise: Fernando Villanueva-Robles (IPGP), Yann Huet (Setec-Lerm), Ngoc Huyen Luu (Ecole Polytechnique), Dorian Bagur (Telecom ParisTech), Jonathan Grandjean (Independent)

The OBSNoise project investigated the application of machine learning to coherently stack ambient noise records collected from ocean bottom seismic (OBS) arrays in order to extract reservoir information. The team's results from synthetic data showed promise. If fully developed, this technology could be a virtually real-time monitoring system of dynamic reservoir properties.


The Killers. Killing It. 

The Killers. Killing It. 

Global geochemical data analytics

The Killers: Alexandre Sache, Violaine Delahaye, Karl Sache (all from Institute Polytechnique UniLaSalle), Côme Arvis, Guillaume Ligner (Ecole Polytechnique)

Two geoscience undergrads and one automotive design student (I know right?) from UniLaSalle hooked up with two data science students from Ecole Polytechnique to interogate the massive GeoRoc database using some clever data analytics tricks and did some novel many-dimensional geochemical classifications.


Team LogFix.

Team LogFix.

Fixing broken well data

LogFix: Guillaume Coffin (Telecom Evolution), Florian Napierala (EISTI), Camille Gimenez (Université Paris-Saclay), Tristan Siméon (Université de Montpellier), Robert Leckenby (Independent)

A truly pristine, calibrated, and corrected petrophysical data is so rare it has a sort of mythical status. Team LogFix used machine learning to identify bad-data zones, repair, QC, and fill-in missing sections. They got an impressive way with the problem, using a dataset from the Athabasca of Canada.


Between the hand-drawn lines

Automagical: Louis Poirier (Independent), Maggie Baber (Independent), Georg Semmler (GiGa infosystems), Björn Wieczoreck (GiGa infosystems), Jonas Kopcsek (GiGa infosystems)

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You don't need to believe in magic. Team Automagical used machine learning to create 3D geological models from 2D cross-sections sections. They trained a predictive model using a collection of standardized hand-drawn cross-sections from human geoscientists. The model learns how to propagate rocks throughout a 3D scene. Their goal is to be able to generate cross-sections along any direction through the model. The AI learned how to do geologically realistic interpolation on simple structures. What kind of geologic complexity is possible with more input from more cross-sections?


The document on the left contains a log display with a lithology column. It's a 'hit'. The one on the right has no lithlogies and is a 'miss'. 

The document on the left contains a log display with a lithology column. It's a 'hit'. The one on the right has no lithlogies and is a 'miss'.

 

There's rocks in them hills! Hills of paper, that is

Logs on the Rocks: Daniel Stanton (Leeds University), Jack Woolam (Leeds University), Adam Goddard (Leeds University), Henri Blondelle (AgileDD)

If the oil and gas industry is to get more efficient, we better get really good at finding lithology and fluid information in the mountains of paper we've collectively built. Team Logs on the Rocks used CNNs to identify graphical depictions of rock types in a sea of unstructured PDFs and TIFFs. They introduced themselves as a team of non-coders, but these guys were were doing cloud computing on AWS and using NVIDIA's GPUs before the end of the weekend. 


Robot vision for seismic interpretation

It's not our FAULT! Claire Birnie (Leeds University), Carlos Alberto da Costa Filho (Edinburgh University), Matteo Ravasi (Statoil), Filippo Broggini (ETHZ), Gijs Straathof (SGS)

Geologic feature recognition using machine learning. The goal was to assist seismic interpreters in detecting geologic features – faults, folds, traps, etc. – in seismic data . They used Haar cascade classifiers, which are routinely used for identifying faces or kittens or beer bottles in photographs and video streams, specially trained to work on seismic data. They used the awesome OpenCV library to build this technology. At the time of writing, their website appears to be maxed out for the month, so if you're dying to see it, leave them a comment on LinkedIn asking them increase their capacity. And in the meantime, you can check out their project's repo on GitHub.

Kudos for the open source repo, team!


It was thrilling to see such a large range of data and applications. Digital thin-sections, ground water maps, seismic data, well logs, cross-sections, information in unstructured documents, and so on. Thanks to each and every individual that showed up with their expertise and enthusiasm. We're all better off because of it.

A quick reminder that our sponsors are awesome! Please high-five them next time you meet them...

Subsurface Hackathon project round-up, part 1

The dust has settled from the Hackathon in Paris two weeks ago. Been there, done that, came home with the T-shirt.

In the same random order they presented their 4-minute demos to our panel of esteemed judges, I present a (very) abbreviated round-up of what the teams made together over the course of the weekend. With the exception of a few teams who managed to spontaneously nucleate before the hackathon, most of these teams were comprised of people who had never met each other before the event.

Just let that sink in for a second: teams of mostly mutual strangers built 13 legit machine-learning-based geoscience applications in one weekend. 


Log Healer  

Log Healer

 

 

An automated well log management system

Team Un-well Loggers: James Wanstall (Glencore), Niket Doshi (Teradata), Joseph Taylor (Teradata), Duncan Irving (Teradata), Jane McConnell (Teradata).

Tech: Kylo (NiFi, HDFS, Hive, Spark)

If you're working with well logs, and if you've got lots of them, you've almost certainly got gaps or inaccuracies from curve to curve and from well to well. The team's scalable, automated well-log file management system Log Healer computes missing logs and heals broken ones. Amazing.


An early result from Team Janus. The image on the left is ground truth, that on the right is predicted. Many of the features are present. Not bad for v0.1!

An early result from Team Janus. The image on the left is ground truth, that on the right is predicted. Many of the features are present. Not bad for v0.1!

Meaningful cross sections from well logs

Team Janus: Daniel Buse, Johannes Camin, Paul Gabriel, Powei Huang, Fabian Kampe (all from GiGa Infosystems)

The team built an elegant machine learning workflow to attack the very hard problem of creating geologically realistic cross-section from well logs. The validation algorithm compares pixels to score the result. 


Think Section's mindblowing photomicrograph labeling tool can also make novel camouflage patterns.

Think Section's mindblowing photomicrograph labeling tool can also make novel camouflage patterns.

Paint-by-numbers on digital thin sections

Team Think Section: Diego Castaneda (Agile*), Brendon Hall (Enthought), Roeland Nieboer (Fugro), Jan Niederau (RWTH Aachen), Simon Virgo (RWTH Aachen)

Tech: Python (Scikit Learn, Scikit Image, Flask, NumPy, SciPy, Pandas), AWS for hosting app & Jupyter server.

Description: Mineral classification and point-counting on thin sections can be an incredibly tedious and time consuming task. Team Think Section trained a model to segregate, classify, and label mineral grains in 200GB of high-resolution multi-polarization-angle photomicrographs.


Team Classy's super-impressive shot gather seismic event Detection technology. Left: synthetic gather. Middle: predicted labels. Right: truth.

Team Classy's super-impressive shot gather seismic event Detection technology. Left: synthetic gather. Middle: predicted labels. Right: truth.

Event detection on seismic shot gathers

Team Classy: Princy Ikotoko Ndong (EOST), Anna Lim (NTNU), Yuriy Ivanov (NTNU), Song Hou (CGG), Justin Gosses (Valador).

Tech: Python (NumPy, Matplotlib), Jupyter notebooks.

The team created an AI which identifies and labels different events on a shot gather image. It can find direct waves, reflections, multiples or coherent noise. It uses a support vector machine for classification, and is simple and fast. 


model2seismic: An entirely new way to do modeling and inversion. Take note: the neural network that made this image knows no physics.

model2seismic: An entirely new way to do modeling and inversion. Take note: the neural network that made this image knows no physics.

Forward and inverse modeling without the physics

Team GANsters - Lukas Mosser (Imperial), Wouter Kimman (Meridian), Jesper Dramsch (Copenhagen), Alfredo de la Fuente (Wolfram), Steve Purves (Euclidity)

Tech: PyNoddy, homegrown Python ML tools.

The GANsters created a deep-learning image-translation-based seismic inversion and forward modelling system. I urge you to go and look at their project on model2seismic. If it doesn't give you goosebumps, you are geophysically inert.


Team Pick Pick Log

Team Pick Pick Log

Machine learning for for stratigraphic interpretation

Team Pick Pick LOG - Antoine Vanbesien (EOST), Fidèle Degni (Mines St-Étienne), Massinissa Mesbahi (Pau), Natsuki Gunji (Mines St-Étienne), Cédric Menut (EOST).

This team of data science and geoscience undergrads attacked an automated stratigraphic interpretation task. They used supervised learning to determine lithology from well logs in Alberta's Athabasca play, then attempted to teach their AI to pick stratigraphic tops. Impressive!


Pretty amazing, huh? The power of the hackathon to bring a project from barely-even-an-idea to actual-working-code is remarkable! And we're not even halfway through the teams: tomorrow I'll describe the other seven projects. 

Le grand hack!

It happened! The Subsurface Hackathon drew to a magnificent close on Sunday, in an intoxicating cloud of code, creativity, coffee, and collaboration. It will take some beating.

Nine months in gestation, the hackathon was on a scale we have not attempted before. Total E&P joined us as co-organizers and made this new reach possible. They also let us use their amazing Booster — a sort of intrapreneurship centre — which was perfect for the event. Their team (thanks especially to Marine and Caroline!) did an amazing job of hosting, as well as providing several professionals from their subsurface software (thanks Jonathan and Yannick!) and data science teams (thanks Victor and David!). Arnaud Rodde and Frédéric Broust, who had to do some organization hacking of their own to make something as weird as a hackathon happen, should be proud of their teams.

Instead of trying to describe the indescribable, here are some photos:

BY THE NUMBERS

16 hours of code
13 teams
62 hackers
44 students
4 robots
568 croissants
0 lost-time incidents

I won't say much about the projects for now. The diversity was high — there were projects in thin section photography, 3D geological modeling, document processing, well log prediction, seismic modeling and inversion, and fault detection. All of the projects included some kind of machine learning, and again there was diversity there, including several deep learning applications. Neural networks are back!

Feel the buzz!

If you are curious, Gram and I recorded a quick podcast and interviewed a few of the teams:

It's going to take a few days to decompress and come down from the high. In a couple of weeks I'll tell you more about the projects themselves, and we'll edit the photos and post the best ones to Flickr (and in the meantime there are a few more pics there already). 

Thank you to the sponsors!

Last thing: we couldn't have done any of this without the support of Dell EMC. David Holmes has been a rock for the hackathon project over the last couple of years, and we appreciate his love of community and code! Thank you too to Duncan and Jane at Teradata, Francois at NVIDIA, Peter and Jon at Amazon AWS, and Gram at Sandstone for all your support. Dear reader: please support these organizations!


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.

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.

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!

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

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

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


UPDATE  View all the results and submissions from the event.


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.

News and updates and a sandwich

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...