Seismic inception

A month ago, some engineers at Google blogged about how they had turned a deep learning network in on itself and produced some fascinating and/or disturbing images:

One of the images produced by the team at Google. Click to see a larger version. Read more. CC-BY.

The basic recipe, which Google later open sourced, involves training a deep learning network (basically a multi-layer neural network) on some labeled images, animals maybe, then searching for matching patterns in a target image, like these clouds. If it finds something, it emphasizes it — given the data, it tries to construct an animal. Then do it again.

Or, here's how a Google programmer puts it (one of my favourite sentences ever)...

Making the "dream" images is very simple. Essentially it is just a gradient ascent process that tries to maximize the L2 norm of activations of a particular DNN layer. 

That's all! Anyway, the point is that you get utter weirdness:

OK, cool... what happens if you feed it seismic?

That was my first thought, I'm sure it was yours too. The second thing I thought, and the third, and the fourth, was: wow, this software is hard to compile. I spent an unreasonable amount of time getting caffe, the Berkeley Vision & Learning Centre's deep learning software, working. But on Friday I cracked it, so today I got to satisfy my curiosity.

The short answer is: reptiles. These weirdos were 8 levels down, which takes about 20 minutes to reach on my iMac.

Seismic data from the Virtual Seismic Atlas, courtesy of Fugro. 


Er, right... what's the point in all this?

That's a good question. It's just a bit of fun really. But it makes you wonder:

  • What if we train the network on seismic facies? I think this could be very interesting.
  • Better yet, what if we train it on geology? Probably spurious: seismic is not geology.
  • Does this mean learning networks are just dumb machines, or can they see more than us? Tough one — human vision is highly fallible. There are endless illusions to prove this. But computers only do what we tell them, at least for now. I think if we're careful what we ask for, we can use these highly non-linear data-crunching algorithms for good.
  • Are we out of a job? Definitely not. How do you think machines will know what to learn? The challenge here is to make this work, and then figure out how it can help change, or at least accelerate, our understanding of the subsurface.

This deep learning stuff — of which the University of Toronto was a major pioneer during its emergence in about 2010 — is part of the machine learning revolution that you are, like it or not, experiencing. It will take time, and it will make awful mistakes, but the indications are that machine learning will eat every analytical method for breakfast. Customer behaviour prediction, computer vision, natural language processing, all this stuff is reeling from the relatively sudden and widespread availability of inexpensive computer intelligence. 

So what are we going to do with that?

           Okay, one more. from  Paige Bailey's Twitter feed .

           Okay, one more. from Paige Bailey's Twitter feed.