Code Show version 1.0

Last week we released Code Show version 1.0. In a new experiment, we teamed up with Total and the European Association of Geoscientists and Engineers at the EAGE Annual Conference and Exhibition in Copenhagen. Our goal was to bring a little of the hackathon to as many conference delegates as possible. We succeeded in reaching a few hundred people over the three days, making a lot of new friends in the process. See the action in this Twitter Moment.

What was on the menu?

The augmented reality sandbox that Simon Virgo and his colleagues brought from the University of Aachen. The sandbox displayed both a geological map generated by the GemPy 3D implicit geological modeling tool, as well as a seismic wavefield animation generated by the Devito modeling and inversion project. Thanks to Yuriy Ivanov (NTNU) and others in his hackathon team for contributing the seismic modeling component.  

Demos from the Subsurface Hackathon. We were fortunate to have lots of hackathon participants make time for the Code Show. Graham Brew presented the uncertainty visualizer his team built; Jesper Dramsch and Lukas Mosser showed off their t-SNE experiments; Florian Smit and Steve Purves demoed their RGB explorations; and Paul Gabriel shared the GiGa Infosystems projects in AR and 3D web visualization. Many thanks to those folks and their teams.

AR and VR demos by the Total team. Dell EMC provided HTC Vive and Meta 2 kits, with Dell Precision workstations, for people to try. They were a lot of fun, provoking several cries of disbelief and causing at least one person to collapse in a heap on the floor.

Python demos by the Agile team. Dell EMC also kindly provided lots more Dell Precision workstations for general use. We hooked up some BBC micro:bit microcontrollers, Microsoft Azure IoT DevKits, and other bits and bobs, and showed anyone who would listen what you can do with a few lines of Python. Thank you to Carlos da Costa (University of Edinburgh) for helping out!

Tech demos by engineers from Intel and INT. Both companies are very active in visualization research and generously spent time showing visitors their technology. 

 The code show in full swing. 

The code show in full swing. 

v 2.0 next year... maybe?

The booth experience was new to us. Quite a few people came to find us, so it was nice to have a base, rather than cruising around as we usually do. I'd been hoping to get more people set up with Python on their own machines, but this may be too in-depth for most people in a trade show setting. Most were happy to see some new things and maybe tap out some Python on a keyboard.

Overall, I'd call it a successful experiment. If we do it next year in London, we have a very good idea of how to shape an even more engaging experience. I think most visitors enjoyed themselves this year though; If you were one of them, we'd love to hear from you!

Big open data... or is it?

Huge news for data scientists and educators. Equinor, the company formerly known as Statoil, has taken a bold step into the open data arena. On Thursday last week, it 'disclosed' all of its subsurface and production data for the Volve oil field, located in the North Sea. 

What's in the data package?

A lot! The 40,000-file package contains 5TB of data, that's 5,000GB!

volve_data.png

This collection is substantially larger, both deeper and broader, than any other open subsurface dataset I know of. Most excitingly, Equinor has released a broad range of data types, from reports to reservoir models: 3D and 4D seismic, well logs and real-time drilling records, and everything in between. The only slight problem is that the seismic data are bundled in very large files at the moment; we've asked for them to be split up.

Questions about usage rights

Regular readers of this blog will know that I like open data. One of the cornerstones of open data is access, and there's no doubt that Equinor have done something incredible here. It would be preferable not to have to register at all, but free access to this dataset — which I'm guessing cost more than USD500 million to acquire — is an absolutely amazing gift to the subsurface community.

Another cornerstone is the right to use the data for any purpose. This involves the owner granting certain privileges, such as the right to redistribute the data (say, for a class exercise) or to share derived products (say, in a paper). I'm almost certain that Equinor intends the data to be used this way, but I can't find anything actually granting those rights. Unfortunately, if they aren't explicitly granted, the only safe assumption is that you cannot share or adapt the data.

For reference, here's the language in the CC-BY 4.0 licence:

 

Subject to the terms and conditions of this Public License, the Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:

  1. reproduce and Share the Licensed Material, in whole or in part; and
  2. produce, reproduce, and Share Adapted Material.
 

You can dig further into the requirements for open data in the Open Data Handbook.

The last thing we need is yet another industry dataset with unclear terms, so I hope Equinor attaches a clear licence to this dataset soon. Or, better still, just uses a well-known licence such as CC-BY (this is what I'd recommend). This will clear up the matter and we can get on with making the most of this amazing resource.

More about Volve

The Volve field was discovered in 1993, but not developed until 15 years later. It produced oil and gas for 8.5 years, starting on 12 February 2008 and ending on 17 September 2016, though about half of that came in the first 2 years (see below). The facility was the Maersk Inspirer jack-up rig, standing in 80 m of water, with an oil storage vessel in attendance. Gas was piped to Sleipner A. In all, the field produced 10 million Sm³ (63 million barrels) of oil, so is small by most standards, with a peak rate of 56,000 barrels per day.

 Volve production over time in standard m³ (i.e. at 20°C). Multiply by 6.29 for barrels.

Volve production over time in standard m³ (i.e. at 20°C). Multiply by 6.29 for barrels.

The production was from the Jurassic Hugin Formation, a shallow-marine sandstone with good reservoir properties, at a depth of about 3000 m. The top reservoir depth map from the discovery report in the data package is shown here. (I joined Statoil in 1997, not long after this report was written, and the sight of this page brings back a lot of memories.)

 

The top reservoir depth map from the discovery report. The Volve field (my label) is the small closure directly north of Sleipner East, with 15/9-19 well on it.

 

Get the data

To explore the dataset, you must register in the 'data village', which Equinor has committed to maintaining for 2 years. It only takes a moment. You can get to it through this link.

Let us know in the comments what you think of this move, and do share what you get up to with the data!

Visualize this!

The Copenhagen edition of the Subsurface Hackathon is over! For three days during the warmest June in Denmark for over 100 years, 63 geoscientists and programmers cooked up hot code in the Rainmaking Loft, one of the coolest, and warmest, coworking spaces you've ever seen. As always, every one of the participants brought their A game, and the weekend flew by in a blur of creativity, coffee, and collaboration. And croissants.

 Pierre enjoying the  Meta AR headset  that DEll EMC provided.

Pierre enjoying the Meta AR headset that DEll EMC provided.

Our sponsors have always been unusually helpful and inspiring, pushing us to get more audacious, but this year they were exceptionally engaged and proactive. Dell EMC, in the form of David and Keith, provided some fantastic tech for the teams to explore; Total supported Agile throughout the organization phase, and Wintershall kindly arranged for the event to be captured on film — something I hope to be able to share soon. See below for the full credit roll!

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During th event, twelve teams dug into the theme of visualization and interaction. As in Houston last September, we started the event on Friday evening, after the Bootcamp (a full day of informal training). We have a bit of process to form the teams, and it usually takes a couple of hours. But with plenty of pizza and beer for fuel, the evening flew by. After that, it was two whole days of coding, followed by demos from all of the teams and a few prizes. Check out some of the pictures:

Thank you very much to everyone that helped make this event happen! Truly a cast of thousands:

  • David Holmes of Dell EMC for unparallelled awesomeness.
  • The whole Total team, but especially Frederic Broust, Sophie Segura, Yannick Pion, and Laurent Baduel...
  • ...and also Arnaud Rodde for helping with the judging.
  • The Wintershall team, especially Andreas Beha, who also acted as a judge.
  • Brendon Hall of Enthought for sponsoring the event.
  • Carlos Castro and Kim Saabye Pedersen of Amazon AWS.
  • Mathias Hummel and Mahendra Roopa of NVIDIA.
  • Eirik Larsen of Earth Science Analytics for sponsoring the event and helping with the judging.
  • Duncan Irving of Teradata for sponsoring, and sorting out the T-shirts.
  • Monica Beech of Ikon Science for participating in the judging.
  • Matthias Hartung of Target for acting as a judge again.
  • Oliver Ranneries, plus Nina and Eva of Rainmaking Loft.
  • Christopher Backholm for taking such great photographs.

Finally, some statistics from the event:

  • 63 participants, including 8 women (still way too few, but 100% better than 4 out of 63 in Paris)
  • 15 students plus a handful of post-docs.
  • 19 people from petroleum companies.
  • 20 people from service and technology companies, including 7 from GiGa-infosystems!
  • 1 no-show, which I think is a new record.

I will write a summary of all the projects in a couple of weeks when I've caught my breath. In the meantime, you can read a bit about them on our new events portal. We'll be steadily improving this new tool over the coming weeks and months.

That's it for another year... except we'll be back in Europe before the end of the year. There's the FORCE Hackathon in Stavanger in September, then in November we'll be in Aberdeen and London running some events with the Oil and Gas Authority. If you want some machine learning fun, or are looking for a new challenge, please come along!

 Simon Virgo (centre) and his colleagues in Aachen built an augmented reality sandbox, powered by their research group's software,  Gempy . He brought it along and three teams attempted projects based on the technology. Above, some of the participants are having a scrum meeting to keep their project on track.

Simon Virgo (centre) and his colleagues in Aachen built an augmented reality sandbox, powered by their research group's software, Gempy. He brought it along and three teams attempted projects based on the technology. Above, some of the participants are having a scrum meeting to keep their project on track.

Looking forward to Copenhagen

We're in Copenhagen for the Subsurface Bootcamp and Hackathon, which start today, and the EAGE Annual Conference and Exhibition, which starts next week. Walking around the city yesterday, basking in warm sunshine and surrounded by sun-giddy Scandinavians, it became clear that Copenhagen is a pretty special place, where northern Europe and southern Europe seem to have equal influence.

The event this weekend promises to be the biggest hackathon yet. It's our 10th, so I think we have the format figured out. But it's only the third in Europe, the theme — Visualization and interaction — is new for us, and most of the participants are new to hackathons so there's still the thrill of the unknown! 

Many thanks to our sponsors for helping to make this latest event happen! Support these organizations: they know how to accelerate innovation in our industry.

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New events for UK

By the way, we just announced two new hackathons, one in London and one in Aberdeen, for the autumn. They are happening just before PETEX, the PESGB petroleum conference; find out more here. You can skill up for these events at some new courses, also just announced. The UK Oil and Gas Authority is offering our Intro to Geocomputing and Machine Learning class for free — apply here for a place. The courses are oversubscribed, so be sure to tell the OGA why you should get a place!

Code Show

There is a lot of other stuff happening at the EAGE exhibition this year — the HPC area, a new start-up area, and a digital transformation area which I hope is as bold as it sounds. Here's the complete schedule and some highlights:

There's lots of other stuff of course — EAGE has the most varied programme of any subsurface conference — but these are the sessions I'd be at if I had time to go to any sessions this year. But I won't because The hackathon is not all that's happening! Next week, starting on Tuesday, we're conducting a new experiment with the Code Show. In partnership with EAGE and Total, this is our attempt to bring some of the hackathon experience to everyone at EAGE. We'll be showing people the projects from the hackathon, talking to them about programming, and helping them get started on their own coding adventure. So if you're at EAGE, swing by Booth #1830 and say Hi.

Weekend worship in Salt Lake City

The Salt Lake City hackathon — only the second we've done with a strong geology theme — is a thing of history, but you can still access the event page to check out who showed up and who did what. (This events page is a new thing we launched in time for this hackathon; it will serve as a public document of what happens at our events, in addition to being a platform for people to register, sponsor, and connect around our events.) 

In true seat-of-the-pants hackathon style we managed to set up an array of webcams and microphones to record the finale. The demos are the icing on the cake. Teams were selected at random and were given 4 minutes to wow the crowd. Here is the video, followed by a summary of what each team got up to... 


Unconformist.ai

Didi Ooi (University of Bristol), Karin Maria Eres Guardia (Shell), Alana Finlayson (UK OGA), Zoe Zhang (Chevron). The team used machine learning the automate the mapping of unconformities in subsurface data. One of the trickiest parts is building up a catalog of data-model pairs for GANs to train on. Instead of relying on thousand or hundreds of thousands of human-made seismic interpretations, the team generated training images by programmatically labelling pixels on synthetic data as being either above (white) or below (black) the unconformity. Project pageSlides.

unconformist.ai_preso

Outcrops Gee Whiz

Thomas Martin (soon Colorado School of Mines), Zane Jobe (Colorado School of Mines), Fabien Laugier (Chevron), and Ross Meyer (Colorado School of Mines). The team wrote some programs to evaluate facies variability along drone-derived digital outcrop models. They did this by processing UAV point cloud data in Python and classified different rock facies using using weather profiles, local cliff face morphology, and rock colour variations as attributes. This research will help in the development drone assisted 3D scanning to automate facies boundaries mapping and rock characterization. RepoSlides.

SLC_outcrop_geewhiz_blog.png

Jet Loggers

Eirik Larsen and Dimitrios Oikonomou (Earth Science Analytics), and Steve Purves (Euclidity). This team of European geoscientists, with their circadian clocks all out of whack, investigated if a language of stratigraphy can be extracted from the rock record and, if so, if it can be used as another tool for classifying rocks. They applied natural language processing (NLP) to an alphabetic encoding of well logs as a means to assist or augment the labour-intensive tasks of classifying stratigraphic units and picking tops. Slides.

 

 

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SLC_bookcliffs.png

Book Cliffs Bandits

Tom Creech (ExxonMobil) and Jesse Pisel (Wyoming State Geological Survey). The team started munging datasets in the Book Cliffs. Unfortunately, they really did not have the perfect, ready to go data, and by the time they pivoted to some workable open data from Alaska, their team name had already became a thing. The goal was build a tool to assist with lithology and stratigraphic correlation. They settled on change-point detection using Bayesian statistics, which they were using to build richer feature sets to test if it could produce more robust automatic stratigraphic interpretation. Repo, and presentation.

 

 

A channel runs through it

Nam Pham (UT Austin), Graham Brew (Dynamic Graphics), Nathan Suurmeyer (Shell). Because morphologically realistic 3D synthetic seismic data is scarce, this team wrote a Python program that can take seismic horizon interpretations from real data, then construct richer training data sets for building an AI that can automatically delineate geological entities in the subsurface. The pixels enclosed by any two horizons are labelled with ones, pixels outside this region are labelled with zeros. This work was in support of Nam's thesis research which is using the SegNet architecture, and aims to extract not only major channel boundaries in seismic data, but also the internal channel structure and variability – details that many seismic interpreters, armed even with state-of-the art attribute toolboxes, would be unable to resolve. Project page, and code.

GeoHacker

Malcolm Gall (UK OGA), Brendon Hall and Ben Lasscock (Enthought). Innovation happens when hackers have the ability to try things... but they also need data to try things out on. There is a massive shortage of geoscience datasets that have been staged and curated for machine learning research. Team Geohacker's project wasn't a project per se, but a platform aimed at the sharing, distribution, and long-term stewardship of geoscience data benchmarks. The subsurface realm is swimming with disparate data types across a dizzying range of length scales, and indeed community efforts may be the only way to prove machine-learning's usefulness and keep the hype in check. A place where we can take geoscience data, and put it online in a ready-to-use for for machine learning. It's not only about being open, online and accessible. Good datasets, like good software, need to be hosted by individuals, properly documented, enriched with tutorials and getting-started guides, not to mention properly funded. Website.

SLC_petrodict.png

Petrodict

Mark Mlella (Univ. Louisiana, Lafayette), Matthew Bauer (Anchutz Exploration), Charley Le (Shell), Thomas Nguyen (Devon). Petrodict is a machine-learning driven, cloud-based lithology prediction tool that takes petrophysics measurements (well logs) and gives back lithology. Users upload a triple combo log to the app, and the app returns that same log with with volumetric fractions for it's various lithologic or mineralogical constituents. For training, the team selected several dozen wells that had elemental capture spectroscopy (ECS) logs – a premium tool that is run only in a small fraction of wells – as well as triple combo measurements to build a model for predicting lithology. Repo.

Seismizor

George Hinkel, Vivek Patel, and Alex Waumann (all from University of Texas at Arlington). Earthquakes are hard. This team of computer science undergraduate students drove in from Texas to spend their weekend with all the other geo-enthusiasts. What problem in subsurface oil and gas did they identify as being important, interesting, and worthy of their relatively unvested attention? They took on the problem of induced seismicity. To test whether machine learning and analytics can be used to predict the likelihood that injected waste water from fracking will cause an earthquake like the ones that have been making news in Oklahoma. The majority of this team's time was spent doing what all good scientists do –understanding the physical system they were trying to investigate – unabashedly pulling a number of the more geomechanically inclined hackers from neighbouring teams and peppering them with questions. Induced seismicity is indeed a complex phenomenon, but George's realization that, "we massively overestimated the availability of data", struck a chord, I think, with the judges and the audience. Another systemic problem. The dynamic earth – incredible in its complexity and forces – coupled with the fascinating and politically charged technologies we use for drilling and fracking might be one of the hardest problems for machine learning to attack in the subsurface. 


AAPG next year is in San Antonio. If it runs, the hackathon will be 18–19 of May. Mark your calendar and stay tuned!

Productive chaos

Wednesday was a good day.

Over 150 participants came to Room 251 for all or part of the first 'unsession' at the AAPG Annual Conference and Exhibition in Salt Lake City. I was one of the hosts of the event, and emceed the afternoon.

In a nutshell, it was awesome. I have facilitated unsessions before, but this event was on a new scale. Twelve tables of 8–10 seats — covered in sticky notes, stickers, coloured pens, and large sheets of paper — quickly filled up. Together, we burned about 10 person-weeks of human productivity, raising the temperature in the room by several degrees in the process.

Diversity means good conversation

On the way in, people self-identified as mostly software (blue name tags) or mostly soft rocks (red), as a non-serious way to get a handle on how many data scientists we had vs how many people are focused on the rocks themselves — without, I hope, any kind of value judgment. The ratio was about 1:2.

As people continued to drift in, we counted people identifying with various categories, to get a very rough idea of who was in the room. The results are shown here. In addition, I counted 24 women present at the start. Part of the point here is to introduce participants to each other, but there's another purpose too. AAPG, like many scientific organizations, is grappling with diversity today. Like others, it needs to do much better. A small part of the solution is, I think, to name it and measure how we're doing at every opportunity. It's one way to pay more attention.

Harder to capture is the profound level of job diversity. People responsible for billion-dollar budgets sat with graduate students, AAPG medal winners with SEC executives. We even had a venture capitalist and a physician.

Look at all these lovely people:

Tangible and intangible output

At the start of the session, I told the room I wanted to fill the walls with things we made — with data. We easily achieved this, producing a survey of the skills geoscientists will need in the future, hundreds of high-value machine learning tasks in geoscience, a ranked list of the most interesting of these, and even some problem analysis of some of them. None of this was definitive, but I hope it will provide grist for the mill of future conversations about machine learning in geoscience.

As well as these tangible products, each person in the room walked away with new connections and new ideas — about machine learning, about collaboration, and about what scientific meetings can be like.

Acknowledgments

A lot of people contributed to making this event happen.

My unsession co-chairs, Brendon Hall and Yan Zaretskiy of Enthought — spent several hours on the phone with me over the last few weeks, shaping the content and flow of an event that was a bit, er, fuzzy.

We seeded the tables with some of the Software Underground crowd who were in town for the hackathon and AAPG. This ensures that there's no failure case: twelve people are definitely coming. And in the unlikely event that 100 people come, there are twelve allies to manage some of the chaos. Heartfelt thanks to the table hosts:

  • Didi Ooi of the University of Bristol
  • Graham Ganssle of Expero
  • Lisa Stright of Colorado State University
  • Thomas Martin of Colorado School of Mines
  • Tom Creech of ExxonMobil
  • David Holmes of Dell EMC
  • Steve Purves of Euclidity
  • Diego Castaneda of Agile
  • Evan Bianco of Agile

Jenny Cole of SEG came along to observe the session and I appreciated her enthusiastic help as it became clear we were in for more than the usual amount of entropy in the room. Theresa Curry of AAPG did an amazing job getting the venue set up, providing refreshments, and ensuring the photographers were there to capture some of the action. The ACE 2018 organizing committee, especially Zane Jobe and Lauren Birgenheier, did their part by agreeing to supprt including such a weird-sounding thing in the program.

Finally, thank you to the 100+ scientists that came to the event, not knowing at all what to expect. It was a privilege to receive your enthusiastic participation and thoughtful contributions. Let's do it again some time!


We will digitize the ideas and products of the unsession over the coming weeks. They will be released under an open license. Watch this space for updates.

If you're interested in the methodology we use for these events, check out Proceedings of an unsession in CSEG Recorder, November 2013. If you'd like help running an event like this, get in touch.

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:

word_cloud_menti_SLC.png

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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An invitation to start something

Most sessions at your average conference are about results — the conclusions and insights from completed research projects. But at AAPG this year, there's another kind of session, about beginnings. The 'Unsession' is back!

   Machine Learning Unsession
   Room 251 B/C, 1:15 pm, Wednesday 23 May

The topic is machine learning in geoscience. My hope is that there's a lot of emphasis on geological problems, especially in stratigraphy. But we don't know exactly where it will go, because the participants themselves will determine the topic and direction of the session.

Importantly, most of the session will not involve technical discussion. It's not a computational geology session. It's a session for everyone — we absolutely need input from anyone who's interested in how computers can help us do geoscience.

What to expect

Echoing our previous unconference-style sessions, here's the vibe my co-hosts (Brendon Hall and Yan Zaretskiy of Enthought) and I are going for:

  • Conferences are too one-way, too passive. We want more action, more tangible outcomes.
  • We want open, honest, inclusive conversations about our science, and our technical challenges. Bring your most courageous, opinionated, candid self. The stuff you’re scared to mention, or you’d normally only talk about over a beer? Bring that.
  • Listen with an open mind. The minute you think you’re right, you’ve checked out of the conversation.
  • Whoever shows up — they are the right people. (This is a rule of Open Space Technology.)
  • What happens is the only thing that could have happened. (This is a rule of Open Space Technology.)
  • There is no finish line; when it's over, it's over.
  • What we are doing is not definitive. It's just a thing that we're doing.

The session is an experiment. Failure is most definitely an option, just the least desirable one. Conversely, perfection is the least likely outcome.

If you're going to AAPG this year, I hope you'll come along to this conversation. Bring a friend!


Here's a reminder of the very first Unsession that Evan and I facilitated, way back in 2013. Argh, that's 5 years ago...

The geospatial sport

An orienteer leaving a control site. 

If you love studying maps or solving puzzles, and you love being outside, then orienteering — the thinking runner's sport — might be the sport you've been looking for.

There are many, many flavours of orienteering (on foot, on skis, in kayaks, etc), but here's how it generally works:

  • Competitors make their way to an event, perhaps on a weekday evening, maybe a weekend morning.
  • Several courses are offered, varying in length (usually 2 to 12 km) and difficulty (from walk-in-the-park to he's-still-not-back-call-search-and-rescue).
  • A course consists of about 20 or so 'controls', which must be visited in order. Visits are recorded on an electronic 'dibber' carried by the orienteer, or by shapes punched on a card.
  • Each person chooses a course , and is allotted a start time.
  • You can't see your course — or the map — until you start. You have 0 seconds to prepare.
  • You walk or run or ski or bike around the controls, at various speeds and in various (occasionally incorrect) directions.
  • After making it to the finish, everyone engages in at least 30 minutes of analysis and dissection of route choices and split times, while eating everything in sight.

The catch is that your navigation system is entirely analog: you are only allowed a paper map and an analog compass, plus a whistle for safety. The only digital components are the timing system and the map-making process — which starts with LiDAR and ends in a software package like OCAD or OOM.

Orienteering maps are especially awesome. They are usually made especially for the sport, typically at 1:5000 or 1:7500, with a 2.5 m or 5 m contour interval. Many small features are mapped, for example walls and fences, small pits and mounds, and even individual trees and boulders.

 The sample orienteering map from the  Open Orienteering Mapper  software, licensed GNU GPL. White areas correspond to open, runnable (high velocity) woodland, with darker shades of green indicating slower running. Yellow areas are open. Olive green areas are out of bounds.

The sample orienteering map from the Open Orienteering Mapper software, licensed GNU GPL. White areas correspond to open, runnable (high velocity) woodland, with darker shades of green indicating slower running. Yellow areas are open. Olive green areas are out of bounds.

Other than the contours and paths, the most salient feature is usually the vegetation, which is always carefully mapped. Geophysicists will like this: the colours correspond more to the speed with which you can run than to the type of vegetation. Orienteering maps are velocity maps!

Here's part of another map, this one from Debert, Nova Scotia:

bluenose-map-debert.png

So, sporty cartophile friends, I urge you to get out and give it a try. My family loves it because it's something we can do together — we all get to compete on our own terms, with our own peers, and there's a course for everyone. I'm coming up on 26 years in the sport, and every event is still a new adventure!


World Orienteering Day — really a whole week — is in the last week in May. It's a great time to give orienteering a try. There are events all over the world, but especially in Europe. If you can't find one near you, track down your national organization and check for events near you.

It's Dynamic Range Day!

OK signal processing nerds, which side are you on in the Loudness War?

If you haven't heard of the Loudness War, you have some catching up to do! This little video by Matt Mayfield is kinda low-res but it's the shortest and best explanation I've been able to find. Watch it, then choose sides >>>>

There's a similar-but-slightly-different war going on in photography: high-dynamic-range or HDR photography is, according to some purists, an existential threat to photography. I'm not going to say any more about it today, but these HDR disasters speak volumes.

True amplitudes

The ideology at the heart of the Loudness War is that music production should be 'pure'. It's analogous to the notion that amplitudes in seismic images should be 'true', and just as nuanced. For some, the idea could be to get as close as possible to a live performance, for others it might be to create a completely synthetic auditory experience; for a record company the main point is to be noticed and then purchased (or at least searched for on Spotify). It reminds me a bit of the aesthetically

For a couple of decades, mainstream producers succumbed to the misconception that driving up the loudness — by increasing the mean amplitude, in turn by reducing the peaks and boosting the quiet passages — was the solution. But this seems to be changing. Through his tireless dedication to the cause, engineer Ian Shepherd has been a key figure in unpeeling this idée fixe. As part of his campaigning, he instituted Dynamic Range Day, and tomorrow is the 8th edition. 

If you want to hear examples of well-produced, dynamic music, check out the previous winners and runners up of the Dynamic Range Day Award — including tunes by Daft Punk, The XX, Kendrick Lamar, and at the risk of dating myself, Orbital.

The end is in sight

I'll warn you right now — this Loudness War thing is a bit of a YouTube rabbithole. But if you still haven't had enough, it's worth listening to the legendary Bob Katz talking about the weapons of war.

My takeaway: the war is not over, but battles are being won. For example, Spotify last year reduced its target output levels, encouraging producers to make more dynamic records. Katz ends his video with "2020 will be like 1980" — which is a good thing, in terms of audio engineering — and most people seem to think the Loudness War will be over.