Reproducibility Zoo

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The Repro Zoo was a new kind of event at the SEG Annual Meeting this year. The goal: to reproduce the results from well-known or important papers in GEOPHYSICS or The Leading Edge. By reproduce, we meant that the code and data should be open and accessible. By results, we meant equations, figures, and other scientific outcomes.

And some of the results are scary enough for Hallowe’en :)

What we did

All the work went straight into GitHub, mostly as Jupyter Notebooks. I had a vague goal of hitting 10 papers at the event, and we achieved this (just!). I’ve since added a couple of other papers, since the inspiration for the work came from the Zoo… and I haven’t been able to resist continuing.

The scene at the Repro Zoo. An air of quiet productivity hung over the booth. Yes, that is Sergey Fomel and Jon Claerbout. Thank you to David Holmes of Dell EMC for the picture.

The scene at the Repro Zoo. An air of quiet productivity hung over the booth. Yes, that is Sergey Fomel and Jon Claerbout. Thank you to David Holmes of Dell EMC for the picture.

Here’s what the Repro Zoo team got up to, in alphabetical order:

  • Aldridge (1990). The Berlage wavelet. GEOPHYSICS 55 (11). The wavelet itself, which has also been added to bruges.

  • Batzle & Wang (1992). Seismic properties of pore fluids. GEOPHYSICS 57 (11). The water properties, now added to bruges.

  • Claerbout et al. (2018). Data fitting with nonstationary statistics, Stanford. Translating code from FORTRAN to Python.

  • Claerbout (1975). Kolmogoroff spectral factorization. Thanks to Stewart Levin for this one.

  • Connolly (1999). Elastic impedance. The Leading Edge 18 (4). Using equations from bruges to reproduce figures.

  • Liner (2014). Long-wave elastic attentuation produced by horizontal layering. The Leading Edge 33 (6). This is the stuff about Backus averaging and negative Q.

  • Luo et al. (2002). Edge preserving smoothing and applications. The Leading Edge 21 (2).

  • Yilmaz (1987). Seismic data analysis, SEG. Okay, not the whole thing, but Sergey Fomel coded up a figure in Madagascar.

  • Partyka et al. (1999). Interpretational aspects of spectral decomposition in reservoir characterization.

  • Röth & Tarantola (1994). Neural networks and inversion of seismic data. Kudos to Brendon Hall for this implementation of a shallow neural net.

  • Taner et al. (1979). Complex trace analysis. GEOPHYSICS 44. Sarah Greer worked on this one.

  • Thomsen (1986). Weak elastic anisotropy. GEOPHYSICS 51 (10). Reproducing figures, again using equations from bruges.

As an example of what we got up to, here’s Figure 14 from Batzle & Wang’s landmark 1992 paper on the seismic properties of pore fluids. My version (middle, and in red on the right) is slightly different from that of Batzle and Wang. They don’t give a numerical example in their paper, so it’s hard to know where the error is. Of course, my first assumption is that it’s my error, but this is the problem with research that does not include code or reference numerical examples.

Figure 14 from Batzle & Wang (1992). Left: the original figure. Middle: My attempt to reproduce it. Right: My attempt in red, overlain on the original.

This was certainly not the only discrepancy. Most papers don’t provide the code or data to reproduce their figures, and this is a well-known problem that the SEG is starting to address. But most also don’t provide worked examples, so the reader is left to guess the parameters that were used, or to eyeball results from a figure. Are we really OK with assuming the results from all the thousands of papers in GEOPHYSICS and The Leading Edge are correct? There’s a long conversation to have here.

What next?

One thing we struggled with was capturing all the ideas. Some are on our events portal. The GitHub repo also points to some other sources of ideas. And there was the Big Giant Whiteboard (below). Either way, there’s plenty to do (there are thousands of papers!) and I hope the zoo continues in spirit. I will take pull requests until the end of the year, and I don’t see why we can’t add more papers until then. At that point, we can start a 2019 repo, or move the project to the SEG Wiki, or consider our other options. Ideas welcome!

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Thank you!

The following people and organizations deserve accolades for their dedication to the idea and hard work making it a reality. Please give them a hug or a high five when you see them.

  • David Holmes (Dell EMC) and Chance Sanger worked their tails off on the booth over the weekend, as well as having the neighbouring Dell EMC booth to worry about. David also sourced the amazing Dell tech we had at the booth, just in case anyone needed 128GB of RAM and an NVIDIA P5200 graphics card for their Jupyter Notebook. (The lights in the convention centre actually dimmed when we powered up our booths in the morning.)

  • Luke Decker (UT Austin) organized a corps of volunteer Zookeepers to help manage the booth, and provided enthusiasm and coding skills. Karl Schleicher (UT Austin), Sarah Greer (MIT), and several others were part of this effort.

  • Andrew Geary (SEG) for keeping things moving along when I became delinquent over the summer. Lots of others at SEG also helped, mainly with the booth: Trisha DeLozier, Rebecca Hayes, and Beth Donica all contributed.

  • Diego Castañeda got the events site in shape to support the Repro Zoo, with a dashboard showing the latest commits and contributors.

Café con leche

At the weekend, 28 digital geoscientists gathered at MAZ Café in Santa Ana, California, to sprint on some open geophysics software projects. Teams and individuals pushed pull requests — code contributions to open source projects — left, right, and centre. Meanwhile, Senah and her team at MAZ kept us plied with coffee and horchata, with fantastic food on the side.

Because people were helping each other and contributing where they could, I found it a bit hard to stay on top of what everyone was working on. But here are some of the things I heard at the project breakdown on Sunday afternoon:

Gerard Gorman, Navjot Kukreja, Fabio Luporini, Mathias Louboutin, and Philipp Witte, all from the devito project, continued their work to bring Kubernetes cluster management to devito. Trying to balance ease of use and unlimited compute turns out to be A Hard Problem! They also supported the other teams hacking on devito.

Thibaut Astic (UBC) worked on implementing DC resistivity models in devito. He said he enjoyed the expressiveness of devito’s symbolic equation definitions, but that there were some challenges with implementing the grad, div, and curl operator matrices for EM.

Vitor Mickus and Lucas Cavalcante (Campinas) continued their work implementing a CUDA framework for devito. Again, all part of the devito project trying to give scientists easy ways to scale to production-scale datasets.

That wasn’t all for devito. Alongside all these projects, Stephen Alwon worked on adapting segyio to read shot records, Robert Walker worked on poro-elastic models for devito, and Mohammed Yadecuri and Justin Clark (California Resources) contributed too. On the second day, the devito team was joined by Felix Hermann (now Georgia Tech), with Mengmeng Yang, and Ali Siakoohi (both UBC). Clearly there’s something to this technology!

Brendon Hall and Ben Lasscock (Enthought) hacked on an open data portal concept, based on the UCI Machine Learning Repository, coincidentally based just down the road from our location. The team successfully got some examples of open data and code snippets working.

Jesper Dramsch (Heriot-Watt), Matteo Niccoli (MyCarta), Yuriy Ivanov (NTNU) and Adriana Gordon and Volodymyr Vragov (U Calgary), hacked on bruges for the weekend, mostly on its documentation and the example notebooks in the in-bruges project. Yuriy got started on a ray-tracing code for us.

Nathan Jones (California Resources) and Vegard Hagen (NTNU) did some great hacking on an interactive plotting framework for geoscience data, based on Altair. What they did looked really polished and will definitely come in useful at future hackathons.

All in all, an amazing array of projects!

This event was low-key compared to recent hackathons, and I enjoyed the slightly more relaxed atmosphere. The venue was also incredibly supportive, making my life very easy.

A big thank you as always to our sponsors, Dell EMC and Enthought. The presence of the irrepressible David Holmes and Chris Lenzsch (both Dell EMC), and Enthought’s new VP of Energy, Charlie Cosad, was greatly appreciated.

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We will definitely be revisiting the sprint concept in the future einmal ist keinmal, as they say. Devito and bruges both got a boost from the weekend, and I think all the developers did too. So stay tuned for the next edition!

Reproduce this!

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There’s a saying in programming: untested code is broken code. Is unreproducible science broken science?

I hope not, because geophysical research is — in general — not reproducible. In other words, we have no way of checking the results. Some of it, hopefully not a lot of it, could be broken. We have no way of knowing.

Next week, at the SEG Annual Meeting, we plan to change that. Well, start changing it… it’s going to take a while to get to all of it. For now we’ll be content with starting.

We’re going to make geophysical research reproducible again!

Welcome to the Repro Zoo!

If you’re coming to SEG in Anaheim next week, you are hereby invited to join us in Exposition Hall A, Booth #749.

We’ll be finding papers and figures to reproduce, equations to implement, and data tables to digitize. We’ll be hunting down datasets, recreating plots, and dissecting derivations. All of it will be done in the open, and all the results will be public and free for the community to use.

You can help

There are thousands of unreproducible papers in the geophysical literature, so we are going to need your help. If you’ll be in Anaheim, and even if you’re not, here some things you can do:

That’s all there is to it! Whether you’re a coder or an interpreter, whether you have half an hour or half a day, come along to the Repro Zoo and we’ll get you started.

Figure 1 from Connolly’s classic paper on elastic impedance. This is the kind of thing we’ll be reproducing.

Figure 1 from Connolly’s classic paper on elastic impedance. This is the kind of thing we’ll be reproducing.

FORCE ML Hackathon: project round-up

The FORCE Machine Learning Hackathon last week generated hundreds of new relationships and nine new projects, including seven new open source tools. Here’s the full run-down, in no particular order…


Predicting well rates in real time

Team Virtual Flow Metering: Nils Barlaug, Trygve Karper, Stian Laagstad, Erlend Vollset (all from Cognite) and Emil Hansen (AkerBP).

Tech: Cognite Data Platform, scikit-learn. GitHub repo.

Project: An engineer from AkerBP brought a problem: testing the rate from a well reduces the pressure and therefore reduces the production rate for a short time, costing about $10k per day. His team investigated whether they could instead predict the rate from other known variables, thereby reducing the number of expensive tests.

This project won the Most Commercial Potential award.

The predicted flow rate (blue) compared to the true flow rate (orange). The team used various models, from multilinear regression to boosted trees.


Reinforcement learning tackles interpretation

Team Gully Attack: Steve Purves, Eirik Larsen, JB Bonas (all Earth Analytics), Aina Bugge (Kalkulo), Thormod Myrvang (NTNU), Peder Aursand (AkerBP).

Tech: keras-rl. GitHub repo.

Project: Deep reinforcement learning has proven adept at learning, and winning, games, and at other tasks including image segmentation. The team tried training an agent to pick these channels in the Parihaka 3D, as well as some other automatic interpretation approaches.

The agent learned something, but in the end it did not prevail. The team learned lots, and did prevail!

This project won the Most Creative Idea award.

Early in training, the learning agent wanders around the image (top left). After an hour of training, the agent tends to stick to the gullies (right).


A new kind of AVO crossplot?

Team ASAP: Per Avseth (Dig), Lucy MacGregor (Rock Solid Images), Lukas Mosser (Imperial), Sandeep Shelke (Emerson), Anders Draege (Equinor), Jostein Heredsvela (DEA), Alessandro Amato del Monte (ENI).

Tech: t-SNE, UMAP, VAE. GitHub repo.

Project: If you were trying to come up with a new approach to AVO analysis, these are the scientists you’d look for. The idea was to reduce the dimensionality of the input traces — using first t-SNE and UMAP then a VAE. This resulted in a new 2-space in which interesting clusters could be probed, chiefly by processing synthetics with known variations (e.g. in thickness or porosity).

This project won the Best In Show award. Look out for the developments that come from this work!

Top: Illustration of the variational autoencoder, which reduces the input data (top left) into some abstract representation — a crossplot, essentially (top middle) — and can also reconstruct the data, but without the features that did not discriminate between the datasets, effectively reducing noise (top right).

The lower image shows the interpreted crossplot (left) and the implied distribution of rock properties (right).


Acquiring seismic with crayons

Team: Jesper Dramsch (Technical University of Denmark), Thilo Wrona (University of Bergen), Victor Aare (Schlumberger), Arno Lettman (DEA), Alf Veland (NPD).

Tech: pix2pix GAN (TensorFlow). GitHub repo.

Project: Not everything tht looks like a toy is a toy. The team spent a few hours drawing cartoons of small seismic sections, then re-trained the pix2pix GAN on them. The result — an app (try it!) that turns sketches into seismic!

This project won the People’s Choice award.

A sketch of a salt diapir penetrating geological layers (left) and the inferred seismic expression, generated by the neural network. In principal, the model could also be trained to work in the other direction.

A sketch of a salt diapir penetrating geological layers (left) and the inferred seismic expression, generated by the neural network. In principal, the model could also be trained to work in the other direction.


Extracting show depths and confidence from PDFs

Team: Florian Basier (Emerson), Jesse Lord (Kadme), Chris Olsen (ConocoPhillips), Anne Estoppey (student), Kaouther Hadji (Accenture).

Tech: sklearn, PyPDF2, NLTK, JavaScript. GitHub repo.

Project: A couple of decades ago, the last great digital revolution gave us PDFs. Lots of PDFs. But these pseudodigital documents still need to be wrangled into Proper Data. This team took on that project, trying in particular to extract both the depth of a show, and the confidence in its identification, from well reports.

This project won the Best Presentation award.

Kaouther Hadji (left), Florian Basier, Jesse Lord, and Anne Estoppey (right).

Kaouther Hadji (left), Florian Basier, Jesse Lord, and Anne Estoppey (right).


Grain size and structure from core images

Team: Eirik Time, Xiaopeng Liao, Fahad Dilib (all Equinor), Nathan Jones (California Resource Corp), Steve Braun (ExxonMobil), Silje Moeller (Cegal).

Tech: sklearn, skimage, fast.ai. GitHub repo.

Project: One of the many teams composed of professionals from all over the industry — it’s amazing to see this kind of collaboration. The team did a great job of breaking the problem down, going after what they could and getting some decent results. An epic task, but so many interesting avenues — we need more teams on these problems!

The pipeline was as ambitious as it looks. But this is a hard problem that will take some time to get good at. Kudos to this team for starting to dig into it and for making amazing progress in just 2 days.


Learning geological age from bugs

Team: David Wade (Equinor), Per Olav Svendsen (Equinor), Bjoern Harald Fotland (Schlumberger), Tore Aadland (University of Bergen), Christopher Rege (Cegal).

Tech: scikit-learn (random forest). GitHub repo.

Project: The team used DEX files from five wells from the recently released Volve dataset from Equinor. The goal was to learn to predict geological age from biostratigraphic species counts. They made substantial progress — and highlighted what a great resource Volve will be as the community explores it and publishes results like these.

David Wade and Per Olav Svendsen of Equinor (top), and some results (bottom)


Lost in 4D space!

Team: Andres Hatloey, Doug Hakkarinen, Mike Brhlik (all ConocoPhillips), Espen Knudsen, Raul Kist, Robin Chalmers (all Cegal), Einar Kjos (AkerBP).

Tech: scikit-learn (random forest regressor). GitHub repo.

Project: Another cross-industry collaboration. In their own words, the team set out to “identify trends between 4D seismic and well measurements in order to calculate reservoir pressures and/or thickness between well control”. They were motivated by real data from Valhall, and did a great job making sense of a lot of real-world data. One nice innovation: using the seismic quality as a weighting factor to try to understand the role of uncertainty. See the team’s presentation.

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Clustering reveals patterns in 4D maps

Team: Tetyana Kholodna, Simon Stavland, Nithya Mohan, Saktipada Maity, Jone Kristoffersen Bakkevig (all CapGemini), Reidar Devold Midtun (ConocoPhillips).

Project: The team worked on real 4D data from an operating field. Reidar provided a lot of maps computed with multiple seismic attributes. Groups of maps represent different reservoir layers, and thirteen different time-lapse acquisitions. So… a lot of maps. The team attempted to correlate 4D effects across all of these dimensions — attributes, layers, and production time. Reidar, the only geoscientist on a team of data scientists, also provided one of the quotes of the hackathon: “I’m the geophysicist, and I represent the problem”.

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That’s it for the FORCE Hackathon for 2018. I daresay there may be more in the coming months and years. If they can build on what we started last week, I think more remarkable things are on the way!


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One more thing…

I mentioned the UK hackathons last time, but I went and forgot to include the links to the events. So here they are again, in case you couldn’t find them online…

What are you waiting for? Get signed up and tell your friends!

Machine learning goes mainstream

At our first machine-learning-themed hackathon, in New Orleans in 2015, we had fifteen hackers. TImes were hard in the industry. Few were willing or able to compe out and play. Well, it’s now clear that times have changed! After two epic ML hacks last year (in Paris and Houston), at which we hosted about 115 scientists, it’s clear this year is continuing the trend. Indeed, by the end of 2018 we expect to have welcomed at least 240 more digital scientists to hackathons in the US and Europe.

Conclusion: something remarkable is happening in our field.

The FORCE hackathon

Last Tuesday and Wednesday, Agile co-organized the FORCE Machine Learning Hackathon in Stavanger, Norway. FORCE is a cross-industry geoscience organization, coordinating meetings and research in subsurface. The event preceeded a 1-day symposium on the same theme: machine learning in geoscience. And it was spectacular.

Get a flavour of the spectacularness in Alessandro Amato’s beautiful photographs:

Fifty geoscientists and engineers spent two days at the Norwegian Petroleum Directorate (NPD) in Stavanger. Our hosts were welcoming, accommodating, and generous with the waffles. As usual, we gently nudged the participants into teams, and encouraged them to define projects and find data to work on. It always amazes me how smoothly this potentially daunting task goes; I think this says something about the purposefulness and resourcefulness of our community.

Here’s a quick run-down of the projects:

  • Biostrat! Geological ages from species counts.

  • Lost in 4D Space. Pressure drawdown prediction.

  • Virtual Metering. Predicting wellhead pressure in real time.

  • 300 Wells. Extracting shows and uncertainty from well reports.

  • AVO ML. Unsupervised machine learning for more geological AVO.

  • Core Images. Grain size and lithology from core photos.

  • 4D Layers. Classification engine for 4D seismic data.

  • Gully Attack. Strat trap picking with deep reinforcement learning.

  • sketch2seis. Turning geological cartoons into seismic with pix2pix.

I will do a complete review of the projects in the coming few days, but notice the diversity here. Five of the projects straddle geological topics, and five are geophysical. Two or three involve petroleum engineering issues, while two or three move into sed/strat. We saw natural language processing. We saw random forests. We saw GANs, VAEs, and deep reinforcement learning. In terms of input data, we saw core photos, PDF reports, synthetic seismograms, real-time production data, and hastily assembled label sets. In short — we saw everything.

Takk skal du ha

Many thanks to everyone that helped the event come together:

  • Peter Bormann, the mastermind behind the symposium, was instrumental in making the hackathon happen.

  • Grete Block Vargle (AkerBP) and Pernille Hammernes (Equinor) kept everyone organized and inspired.

  • Tone Helene Mydland (NPD) and Soelvi Amundrud (NPD) made sure everything was logistically honed.

  • Eva Halland (NPD) supported the event throughout and helped with the judging.

  • Alessandro Amato del Monte (Eni) took some fantastic photos — as seen in this post.

  • Diego Castaneda and Rob Leckenby helped me on the Agile side of things, and helped several teams.

And a huge thank you to the sponsors of the event — too many to name, but here they all are:

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There’s more to come!

If you’re reading this thinking, “I’d love to go to a geoscience hackathon”, and you happen to live in or near the UK, you’re in luck! There are two machine learning geoscience hackathons coming up this fall:

Don’t miss out! Get signed up and we’ll see you there.

What is a sprint?

In October we're hosting our first 'code sprint'! What is that?

A code sprint is a type of hackathon, in which efforts are focused around a small number of open source projects. They are related to, but not really the same as, sprints in the Scrum software development framework. They are non-competitive — the only goal is to improve the software in question, whether it's adding functionality, fixing bugs, writing tests, improving documentation, or doing any of the other countless things that good software needs. 

On 13 and 14 October, we'll be hacking on 3 projects:

  • Devito: a high-level finite difference library for Python. Devito featured in three Geophysical Tutorials at the end of 2017 and beginning of 2018 (see Witte et al. for Part 3). The project needs help with code, tests, model examples, and documentation. There will be core devs from the project at the sprint. GitHub repo is here.
  • Bruges: a simple collection of Python functions representing basic geophysical equations. We built this library back in 2015, and have been chipping away ever since. It needs more equations, better docs, and better tests — and the project is basic enough for anyone to contribute to it, even a total Python newbie. GitHub repo is here.
  • G3.js: a JavaScript wrapper for D3.js, a popular plotting toolkit for web developers. When we tried to adapt D3.js to geoscience data, we found we wanted to simplify basic tasks like making vertical plots, and plotting raster-like data (e.g. seismic) with line plots on top (e.g. horizons). Experience with JavaScript is a must. GitHub repo is here.

The sprint will be at a small joint called MAZ Café Con Leche, located in Santa Ana about 10 km or 15 minutes from the Anaheim Convention Center where the SEG Annual Meeting is happening the following week.

Thank you, as ever, to our fantastic sponsors: Dell EMC and Enthought. These two companies are powered by amazing people doing amazing things. I'm very grateful to them both for being such enthusiastic champions of the change we're working for in our science and our industry. 

If you like the sound of spending the weekend coding, talking geophysics, and enjoying the best coffee in southern California, please join us at the Geophysics Sprint! Register on Eventbrite and we'll see you there.

Results from the AAPG Machine Learning Unsession

Click here  to visit the Google Doc write-up

Click here to visit the Google Doc write-up

Back in May, I co-hosted a different kind of conference session — an 'unsession' — at the AAPG Annual Conference and Exhibition in Salt Lake City, Utah. It was successful in achieving its main goal, which was to show the geoscience community and AAPG organizers a new way of collaborating, networking, and producing tangible outcomes from conference sessions.

It also succeeded in drawing out hundreds of ideas and questions around machine learning in geoscience. We have now combed over what the 120 people (roughly) produced on that afternoon, written it up in a Google Doc (right), and present some highlights right here in this post.

Click here  to visit the Flickr photo album.

Click here to visit the Flickr photo album.

The unsession had three phases:

  1. Exploring current and future skills for geoscientists.

  2. Asking about the big questions in machine learning in geoscience.

  3. Digging into some of those questions.

Let's look at each one in turn.


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Current and future skills

As an icebreaker, we asked everyone to list three skills they have that set them apart from others in their teams or organizations — their superpowers, if you will. They wrote these on green Post-It notes. We also asked for three more skills they didn't have today, but wanted to acquire in the next decade or so. These went on orange Post-Its. We were especially interested in those skills that felt intimidating or urgent. The 8 or 10 people at each table then shared these with each other, by way of introducing themselves.

The skills are listed in this Google Sheets document.

Unsurprisingly, the most common 'skills I have' were around geoscience: seismic interpretation, seismic analysis, stratigraphy, engineering, modeling, sedimentology, petrophysics, and programming. And computational methods dominated the 'skills I want' category: machine learning, Python, coding or programming, deep learning, statistics, and mathematics.

We followed this up with a more general question — How would you rate the industry's preparedness for this picture of the future, as implied by the skill gap we've identified?. People could substitute 'industry' for whatever similar scale institution felt meaningful to them. As shown (right), this resulted in a bimodal distribution: apparently there are two ways to think about the future of applied geoscience — this may merit more investigation with a more thorough survey.

Get the histogram data.

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Big questions in ML

After the icebreaker, we asked the tables to respond to a big question:

What are the most pressing questions in applied geoscience that can probably be tackled with machine learning?

We realized that this sounds a bit 'hammer looking for a nail', but justified asking the question this way by drawing an anology with other important new tools of the past — well logging, or 3D seismic, or sequence stratigrapghy. The point is that we have this powerful new (to us) set of tools; what are we going to look at first? At this point, we wanted people to brainstorm, without applying constraints like time or money.

This yielded approximately 280 ideas, all documented in the Google Sheet. Once the problems had been captured, the tables rotated so that each team walked to a neighboring table, leaving all their problems behind... and adopting new ones. We then asked them to score the new problems on two axes: scope (local vs global problems) and tractability (easy vs hard problems). This provided the basis for each table to choose one problem to take to the room for voting (each person had 9 votes to cast). This filtering process resulted in the following list:

  1. How do we communicate error and uncertainty when using machine learning models and solutions? 85 votes.

  2. How do we account for data integration, integrity, and provenance in our models? 78 votes.

  3. How do we revamp the geoscience curriculum for future geoscientists? 71 votes.

  4. What does guided, searchable, legacy data integration look like? 68 votes.

  5. How can machine learning improve seismic data quality, or provide assistive technology on poor data? 65 votes.

  6. How does the interpretability of machine learning model predictions affect their acceptance? 54 votes.

  7. How do we train a model to assign value to prospects? 51 votes.

  8. How do we teach artificial intelligences foundational geology? 45 votes.

  9. How can we implement automatic core description? 42 votes.

  10. How can we contain bad uses of AI? 40 votes.

  11. Is self-steering well drilling possible? 21 votes.

I am paraphrasing most of those, but you can read the originals in the Google Sheet data harvest.


Exploring the questions

In the final stage of the afternoon, we took the top 6 questions from the list above, and dug into them a little deeper. Tables picked their way through our Solution Sketchpads — especially updated for machine learning problems — to help them navigate the problems. Clearly, these questions were too enormous to make much progress in the hour or so left in the day, but the point here was to sound out some ideas, identify some possible actions, and connect with others interested in working on the problem.

One of the solution sketches is shown here (right), for the Revamp the geoscience curriculum problem. They discussed the problem animatedly for an hour.

This team included — among others — an academic geostatistician, an industry geostatistician, a PhD student, a DOE geophysicist, an SEC geologist, and a young machine learning brainbox. Amazingly, this kind of diversity was typical of the tables.

See the rest of the solution sketches in Flickr.


That's it! Many thanks to Evan Bianco for the labour of capturing and digitizing the data from the event. Thanks also to AAPG for the great photos, and for granting them an open license. And thank you to my co-chairs Brendon Hall and Yan Zaretskiy of Enthought, and all the other folks who helped make the event happen — see the Productive chaos post for details.

To dig deeper, look for the complete write up in Google Docs, and the photos in Flickr


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Just a reminder... if it's Python and machine learning skills you want, we're running a Summer School in downtown Houston the week of 13 August. Come along and get your hands on the latest in geocomputing methods. Suitable for beginners or intermediate programmers.

Don't miss out! Find out more or register now.

Visualization in Copenhagen, part 2

In Part 1, I wrote about six of the projects teams contributed at the Subsurface Hackathon in Copenhagen in June. Today I want to tell you about the rest of them. 


A data exploration tool

Team GeoClusterFu...n: Dan Stanton (University of Leeds), Filippo Broggini (ETH Zürich), Francois Bonneau (Nancy), Danny Javier Tapiero Luna (Equinor), Sabyasachi Dash (Cairn India), Nnanna Ijioma (geophysicist). 

Tech: Plotly Dash. GitHub repo.

Project: The team set out to build an interactive web app — a totally new thing for all of them — to make interactive plots from data in a CSV. They ended up with the basis of a useful tool for exploring geoscience data. Project page.

Four sixths of the GeoClusterFu...n team cluster around a laptop.

Four sixths of the GeoClusterFu...n team cluster around a laptop.


AR outcrop on your phone

Team SmARt_OGs: Brian Burnham (University of Aberdeen), Tala Maria Aabø (Natural History Museum of Denmark), Björn Wieczoreck, Georg Semmler and Johannes Camin (GiGa Infosystems).

Tech: ARKit/ARCore, WebAR, Firebase. GitLab repo. 

Project: Bjørn and his colleagues from GiGa Infosystems have been at all the European hackathons. This time, he knew he wanted to get virtual outcrops on mobiles phones. He found a willing team, and they got it done! Project page.

Three views from the SmartOGs's video.  See the full version.

Three views from the SmartOGs's video. See the full version.


Rock clusters in latent space

The Embedders: Lukas Mosser (Imperial College London), Jesper Dramsch (Technical University of Denmark), Ben Fischer (PricewaterhouseCoopers), Harry McHugh (DUG), Shubhodip Konar (Cairn India), Song Hou (CGG), Peter Bormann (ConocoPhillips).

Tech: Bokeh, scikit-learn, Multicore-TSNE. GitHub repo.

Project: There has been a lot of recent interest in the t-SNE algorithm as a way to reduce the dimensionality of complex data. The team explored its application to subsurface data, and found promising applications. Web page. Project page.

The Embeders built a web app to cluster the data in an LAS file. The clusters (top left) are generated by the t-SNE algorithm.

The Embeders built a web app to cluster the data in an LAS file. The clusters (top left) are generated by the t-SNE algorithm.


Fully mixed reality

Team Hands On GeoLabs: Will Sanger (Western Geco), Chance Sanger (Houston Museum of Fine Arts), Pierre Goutorbe (Total), Fernando Villanueva (Institut de Physique du Globe de Paris).

Project: Starting with the ambitious goal of combining the mixed reality of the Meta AR gear with the mixed reality of the Gempy sandbox, the team managed to display and interact with some seismic data in the AR headset, which  allows interaction with simple hand gestures. Project page.

The team demonstrate the Meta AR headset.

The team demonstrate the Meta AR headset.


Huge grids over the web

Team Grid Vizards: Fabian Kampe, Daniel Buse, Jonas Kopcsek, Paul Gabriel (all from GiGa Infosystems)

Tech: three.js. GitHub repo.

Project: Paul and his team wanted to visualize hundreds of millions or billions of grid cells — all in the browser. They ended up with about 20 million points working very smoothly, and impressed everyone. Project page.

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Interpreting RGB displays for spec decomp

Team: Florian Smit (Technical University of Denmark), Gijs Straathof (SGS), Thomas Gazzola (Total), Julien Capgras (Total), Steve Purves (Euclidity), Tom Sandison (Shell)

Tech: Python, react.js. GitHub repos: Client. Backend.

Project: Spectral decomposition is still a mostly quantitative tool, especially the interpretation of RGB-blended displays. This team set out to make intuitive, attractive forward models of the spectral response of wells. This should help interpret seismic data, and perhaps make more useful RGB displays too. Intriguing and promising work. Project page.

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That's it for another year! Twelve new geoscience visualization projects — ten of them open source. And another fun, creative weekend for 63 geoscientists — all of whom left with new connections and new skills. All this compressed into one weekend. If you haven't experienced a hackathon yet, I urge you to seek one out.

I will leave you with two videos — and an apology. We are so focused on creating a memorable experience for everyone in the room, that we tend to neglect the importance of capturing what's happening. Early hackathons only had the resulting blog post as the document of record, but lately we've been trying to livestream the demos at the end. Our success has been, er, mixed... but they were especially wonky this time because we didn't have livestream maestro Gram Ganssle there. So, these videos exist, and are part of the documentation of the event, but they barely begin to convey the awesomeness of the individuals, the teams, or their projects. Enjoy them, but next time — you should be there!

Visualization in Copenhagen, part 1

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It's finally here! The round-up of projects from the Subsurface Hacakthon in Copenhagen last month. This is the first of two posts presenting the teams and their efforts, in the same random order the teams presented them at the end of the event.


Subsurface data meets Pokemon Go

Team Geo Go: Karine Schmidt, Max Gribner, Hans Sturm (all from Wintershall), Stine Lærke Andersen (University of Copenhagen), Ole Johan Hornenes (University of Bergen), Per Fjellheim (Emerson), Arne Kjetil Andersen (Emerson), Keith Armstrong (Dell EMC). 

Project: With Pokemon Go as inspiration, the team set out to prototype a geoscience visualization app that placed interactive subsurface data elements into a realistic 3D environment.

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Visualizing blind spots in data

Team Blind Spots: Jo Bagguley (UK Oil & Gas Authority), Duncan Irving (Teradata), Laura Froelich (Teradata), Christian Hirsch (Aalborg University), Sean Walker (Campbell & Walker Geophysics).

Tech: Flask, Bokeh, AWS for hosting app. GitHub repo.

Project: Data management always comes up as an issue in conversations about geocomputing, but few are bold enough to tackle it head on. This team built components for checking the integrity of large amounts of raw data, before passing it to data science projects. Project page.

Sean, Laura, and Christian. Jo and Duncan were out doing research. Note the kanban board in the background — agile all the way!

Sean, Laura, and Christian. Jo and Duncan were out doing research. Note the kanban board in the background — agile all the way!


Volume uncertainties visualization

Team Fortuna: Natalia Shchukina (Total), Behrooz Bashokooh (Shell), Tobias Staal (University of Tasmania), Robert Leckenby (now Agile!), Graham Brew (Dynamic Graphics), Marco van Veen (RWTH Aachen). 

Tech: Flask, Bokeh, Altair, Holoviews. GitHub repo.

Project: Natalia brought some data with her: lots of surface grids. The team built a web app to compute uncertainty sections and maps, then display them dynamically and interactively — eliciting audible gasps from the room. Project page.

The Fortuna app: Probability of being the the zone (left) and entropy (right). Cross-sections are shown at the top, maps on the bottom.


Differences and similarities with RGB blends

Team RGBlend: Melanie Plainchault and Jonathan Gallon (Total), Per Olav Svendsen, Jørgen Kvalsvik and Max Schuberth (Equinor).

Tech: Python, Bokeh. GitHub repo.

Project: One of the more intriguing ideas of the hackathon was not just so much a fancy visualization technique, as a novel way of producing a visualization — differencing 3 images and visualizing the differences in RGB space. It reminded me of an old blog post about the spot the difference game. Project page.

The differences (lower right) between three time-lapse seismic amplitude maps.

The differences (lower right) between three time-lapse seismic amplitude maps.


Augmented reality geological maps

Team AR Sandbox: Simon Virgo (RWTH Aachen), Miguel de la Varga (RWTH Aachen), Fabian Antonio Stamm (RWTH Aachen), Alexander Schaaf (University of Aberdeen).

Tech: Gempy. GitHub repo.

Project: I don't have favourite projects, but if I did, this would be it. The GemPy group had already built their sandbox when they arrived, but they extended it during the hackathon. Wonderful stuff. Project page.

magic box of sand: Sculpting a landscape (left), and the projected map (right). You can't even imagine how much fun it was to play with.


Augmented reality seismic wavefields

Team Sandbox Seismics: Yuriy Ivanov (NTNU Trondheim), Ana Lim (NTNU Trondheim), Anton Kühl (University of Copenhagen), Jean Philippe Montel (Total).

Tech: GemPy, Devito. GitHub repo.

Project: This team worked closely with Team AR Sandbox, but took it in a different direction. They instead read the velocity from the surface of the sand, then used devito to simulate a seismic wavefield propagating across the model, and projected that wavefield onto the sand. See it in action in my recent Code Show post. Project page.

Yuriy Ivanov demoing the seismic wavefield moving across the sandbox.


Pretty cool, right? As usual, all of these projects were built during the hackathon weekend, almost exclusively by teams that formed spontaneously at the event itself (I think one team was self-contained from the start). If you didn't notice the affiliations of the participants — go back and check them out; I think this might have been an unprecedented level of collaboration!

Next time we'll look at the other six projects. [UPDATE: Next post is here.]

Before you go, check out this awesome video Wintershall made about the event. A massive thank you to them for supporting the event and for recording this beautiful footage — and for agreeing to share it under a CC-BY license. Amazing stuff!

Lots of news!

I can't believe it's been a month since my last post! But I've now recovered from the craziness of the spring — with its two hackathons, two conferences, two new experiments, as well as the usual courses and client projects — and am ready to start getting back to normal. My goal with this post is to tell you all the exciting stuff that's happened in the last few weeks.

Meet our newest team member

There's a new Agilist! Robert Leckenby is a British–Swiss geologist with technology tendencies. Rob has a PhD in Dynamic characterisation and fluid flow modelling of fractured reservoirs, and has worked in various geoscience roles in large and small oil & gas companies. We're stoked to have him in the team!

Rob lives near Geneva, Switzerland, and speaks French and several other human languages, as well as Python and JavaScript. He'll be helping us develop and teach our famous Geocomputing course, among other things. Reach him at robert@agilescientific.com.

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Geocomputing Summer School

We have trained over 120 geoscientists in Python so far this year, but most of our training is in private classes. We wanted to fix that, and offer the Geocomputing class back for anyone to take. Well, anyone in the Houston area :) It's called Summer School, it's happening the week of 13 August, and it's a 5-day crash course in scientific Python and the rudiments of machine learning. It's designed to get you a long way up the learning curve. Read more and enroll. 


A new kind of event

We have several more events happening this year, including hackathons in Norway and in the UK. But the event in Anaheim, right before the SEG Annual Meeting, is going to be a bit different. Instead of the usual Geophysics Hackathon, we're going to try a sprint around open source projects in geophysics. The event is called the Open Geophysics Sprint, and you can find out more here on events.agilescientific.com.

That site — events.agilescientific.com — is our new events portal, and our attempt to stay on top of the community events we are running. Soon, you'll be able to sign up for events on there too (right now, most of them are still handled through Eventbrite), but for now it's at least a place to see everything that's going on. Thanks to Diego for putting it together!