Machine learning meets seismic interpretation

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Agile has been reverberating inside the machine learning echo chamber this past week at EAGE. The hackathon's theme was machine learning, Monday's workshop was all about machine learning. And Matt was also supposed to be co-chairing the session on Applications of machine learning for seismic interpretation with Victor Aare of Schlumberger, but thanks to a power-cut and subsequent rescheduling, he found himself double-booked so, lucky me, he invited me to sit in his stead. Here are my highlights, from the best seat in the house.

Before I begin, I must mention the ambivalence I feel towards the fact that 5 of the 7 talks featured the open-access F3 dataset. A round of applause is certainly due to dGB Earth Sciences for their long time stewardship of open data. On the other hand, in the sardonic words of my co-chair Victor Aarre, it would have been quite valid if the session was renamed The F3 machine learning session. Is it really the only quality attribute research dataset our industry can muster? Let's do better.

Using seismic texture attributes for salt classification

Ghassan AlRegib ruled the stage throughout the session with not one, not two, but three great talks on behalf of himself and his grad students at Georgia Institute of Technology (rather than being a show of bravado, this was a result of problems with visas). He showed some exciting developments in shallow learning methods for predicting facies in seismic data. In addition to GLCM attributes, he also introduced a couple of new (to me anyway) attributes for salt classification. Namely, textural gradient and a thing he called seismic saliency, a metric modeled after the human visual system describing the 'reaction' between relative objects in a 3D scene. 

Twelve Seismic attributes used for multi-attribute salt-boundary classification. (a) is RMS Amplitude, (B) to (M) are TEXTURAL attributes. See abstract for details. This figure is copyright of Ghassan AlRegib and licensed CC-BY-SA by virtue of being generated from the F3 dataset of dGB and TNO.

Ghassan also won the speakers' lottery, in a way. Due to the previous day's power outage and subsequent reshuffle, the next speaker in the schedule was a no-show. As a result, Ghassan had an extra 20 minutes to answer questions. Now for most speakers that would be a public-speaking nightmare, but Ghassan hosted the onslaught of inquiring minds beautifully. If we hadn't had to move on to the next next talk, I'm sure he could have entertained questions all afternoon. I find it fascinating how unpredictable events like power outages can actually create the conditions for really effective engagement. 

Salt classification without using attributes (using deep learning)

Matt reported on Anders Waldeland's work a year ago, and it was interesting to see how his research has progressed, as he nears the completion of his thesis. 

Anders successfully demonstrated how convolutional neural networks (CNNs) can classify salt bodies in seismic datasets. So, is this a big deal? I think it is. Indeed, Anders's work seems like a breakthough in seismic interpretation, at least of salt bodies. To be clear, I don't think this means that it is time for seismic interpreters to pack up and go home. But maybe we can start looking forward to spending our time doing less tedious things than picking complex salt bodies.  

One slice of a 3d seismic volume with two CLASS LABELS: Salt (red) and Not SALT (GREEN). This is the training data. On the right: Extracted 3D salt body in the same dataset, coloured by elevation. Copyright of A Waldeland, used with permission.

One slice of a 3d seismic volume with two CLASS LABELS: Salt (red) and Not SALT (GREEN). This is the training data. On the right: Extracted 3D salt body in the same dataset, coloured by elevation. Copyright of A Waldeland, used with permission.

He trained a CNN on one manually labeled slice of a 3D cube and used the network to automatically classify the full 3D salt body (on the right in the figure). Conventional algorithms for salt picking, such as that used by AlRegib (see above), typically rely on seismic attributes to define a feature space. This requires professional insight and judgment, and is prone to error and bias. Nicolas Audebert mentioned the same shortcoming in his talk in the workshop Matt wrote about last week. In contrast, the CNN algorithm works directly on the seismic data, learning the most discriminative filters on its own, no attributes needed. 

Intuition training

Machine learning isn't just useful for computing in the inverse direction such as with inversion, seismic interpretation, and so on. Johannes Amtmann showed us how machine learning can be useful for ranking the performance of different clustering methods using forward models. It was exciting to see: we need to get back into the habit of forward modeling, each and every one of us. Interpreters build synthetics to hone their seismic intuition. It's time to get insanely good at building forward models for machines, to help them hone theirs. 

There were so many fascinating problems being worked on in this session. It was one of the best half-day sessions of technical content I've ever witnessed at a subsurface conference. Thanks and well done to everyone who presented.

Why don't people use viz rooms?

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Matteo Niccoli asked me why I thought the use of immersive viz rooms had declined. Certainly, most big companies were building them in about 1998 to 2002, but it's rare to see them today. My stock answer was always "Linux workstations", but of course there's more to it than that.

What exactly is a viz room?

I am not talking about 'collaboration rooms', which are really just meeting rooms with a workstation and a video conference phone, a lot of wires, and wireless mice with low batteries. These were one of the collaboration technologies that replaced viz rooms, and they seem to be ubiquitous (and also under-used).

The Viz Lab at Wisconsin–Madison. Thanks to Harold Tobin for permission.A 'viz room', for our purposes here, is a dark room with a large screen, at least 3 m wide, probably projected from behind. There's a Crestron controller with greasy fingerprints on it. There's a week-old coffee cup because not even the cleaners go in there anymore. There's probably a weird-looking 3D mouse and some clunky stereo glasses. There might be some dusty haptic equipment that would work if you still had an SGI.

Why did people stop using them?

OK, let's be honest... why didn't most people use them in the first place?

  1. The rise of the inexpensive Linux workstation. The Sun UltraSPARC workstations of the late 1990s couldn't render 3D graphics quickly enough for spinning views or volume-rendered displays, so viz rooms were needed for volume interpretation and well-planning. But fast machines with up to 16GB of RAM and high-end nVidia or AMD graphics cards came along in about 2002. A full dual-headed set-up cost 'only' about $20k, compared to about 50 times that for an SGI with similar capabilities (for practical purposes). By about 2005, everyone had power and pixels on the desktop, so why bother with a viz room?
  2. People never liked the active stereo glasses. They were certainly clunky and ugly, and some people complained of headaches. It took some skill to drive the software, and to avoid nauseating spinning around, so the experience was generally poor. But the real problem was that nobody cared much for the immersive experience, preferring the illusion of 3D that comes from motion. You can interactively spin a view on a fast Linux PC, and this provides just enough immersion for most purposes. (As soon as the motion stops, the illusion is lost, and this is why 3D views are so poor for print reproduction.)
  3. They were expensive. Early adoption was throttled by expense  (as with most new technology). The room renovation might cost $250k, the SGI Onyx double that, and the projectors were $100k each. But  even if the capex was affordable, everyone forgot to include operating costs — all this gear was hard to maintain. The pre-DLP cathode-ray-tube projectors needed daily calibration, and even DLP bulbs cost thousands. All this came at a time when companies were letting techs go and curtailing IT functions, so lots of people had a bad experience with machines crashing, or equipment failing.
  4. Intimidation and inconvenience. The rooms, and the volume interpretation workflow generally, had an aura of 'advanced'. People tended to think their project wasn't 'worth' the viz room. It didn't help that lots of companies made the rooms almost completely inaccessible, with a locked door and onerous booking system, perhaps with a gatekeeper admin deciding who got to use it.
  5. Our culture of PowerPoint. Most of the 'collaboration' action these rooms saw was PowerPoint, because presenting with live data in interpretation tools is a scary prospect and takes practice.
  6. Volume interpretation is hard and mostly a solitary activity. When it comes down to it, most interpreters want to interpret on their own, so you might as well be at your desk. But you can interpret on your own in a viz room too. I remember Richard Beare, then at Landmark, sitting in the viz room at Statoil, music blaring, EarthCube buzzing. I carried on this tradition when I was at Landmark as I prepared demos for people, and spent many happy hours at ConocoPhillips interpreting 3D seismic on the largest display in Canada.  

What are viz rooms good for?

Don't get me wrong. Viz rooms are awesome. I think they are indispensable for some workflows: 

  • Well planning. If you haven't experienced planning wells with geoscientists, drillers, and reservoir engineers, all looking at an integrated subsurface dataset, you've been missing out. It's always worth the effort, and I'm convinced these sessions will always plan a better well than passing plans around by email. 
  • Team brainstorming. Cracking open a new 3D with your colleagues, reviewing a well program, or planning the next year's research projects, are great ways to spend a day in a viz room. The broader the audience, as long as it's no more than about a dozen people, the better. 
  • Presentations. Despite my dislike of PowerPoint, I admit that viz rooms are awesome for presentations. You will blow people away with a bit of live data. My top tip: make PowerPoint slides with an aspect ratio to fit the entire screen: even PowerPoint haters will enjoy 10-metre-wide slides.

What do you think? Are there still viz rooms where you work? Are there 'collaboration rooms'? Do people use them? Do you?