I nearly had kittens when danbri pointed this challenge out to me. I’ve thought for a long time earthquake prediction was well in scope for machine learning and have been dismayed at how little uptake there has been. (Surprised too, given the co-location of many tech people and the San Andreas fault…). Hopefully this competition will change things. Deadline is in 3 months, pretty significant $ prizes.
My second reaction was: “Great! I can work on ELFQuake and maybe win a prize!”. But that isn’t the whole picture. The competition is based around data generated in the lab, essentially by squashing a rock and recording its fractures. Apparently a reasonable approximation of geological effects.
I forget offhand where, but I’ve also seen a project aiming to use machine learning on real data. But that project, as with this competition, I feel is missing a trick. My gut says that although, sure, algorithms can almost certainly be useful in predicting seismic events, using the seismic data alone for training is a blinkered approach. These events, in the real world, occur as the result of the behaviour of a massively complex system. There are practical limitations on what can be modeled, but I’d suggest that it’s possible to creep a little further into the real world by bringing in data from other natural sources. For example, does the position of the moon influence the timing of events? Does seem at least credible, given that its gravity is enough to pull the oceans around.
The data source I reckon looks most promising is natural radio, signals that have been shown to sometimes contain artifacts associated with subsequent seismic events. This is the hypothesis of this project ELFQuake (ELF for Extremely Low Frequency, it’s in this frequency range and that of VLF, Very Low Frequency that earthquake precursors seem to occur).
For me, the Kaggle challenge has acted as a nudge, to get me moving on ELFQuake software again. What’s more, material has already appeared for the basic setup needed to process this data – data that has a lot in common with the ELFQuake targets. This is very convenient for me, as although I’m now getting somewhat familiar with the principles and algorithms of Deep Learning, my practical experience is virtually non-existent. So I’ve been given a great foot-up. Here’s some material using sklearn : video, github. The toolkit that seems to me the most appealing option is Keras on TensorFlow (on Anaconda), but a lot of the pre/post Python wrangling will be the same.
I’ve put my name down for the competition, it’s a bit of extra motivation – potential $$$s! – to work on this stuff, and also what I get together for it can be used as a placeholder in the end-to-end system I’m aiming for (seismic & radio sensors -> data acquisition -> [magic] -> Twitter notifications).