Provisional Graph

I’ve now located the minimum data sources needed to start putting together the neural network for this system. I now need to consider how to sample & shape this data. To this end I’ve roughed out a graph – it’s short on details and will undoubtedly change, but should be enough to decide on how to handle the inputs.

To reiterate the aim, I want to take ELF/VLF (and historical seismic) signals and use them to predict future seismic events.

As an overall development strategy, I’m starting with a target of the simplest thing that could possibly work, and iteratively moving towards something with a better chance of working.

Data Sources

I’ve not yet had a proper look at what’s available as archived data, but I’m pretty sure what’s needed will be available.  The kind of anomalies that precede earthquakes will be relatively rare, so special case signals will be important in training the network. However, the bulk of training data and runtime data will come come from live online sources.

Seismic Data

Prior work (eg OPERA) suggests that clear radio precursors are usually only associated with fairly extreme events, and even those are only detectable using traditional means for geographically close earthquakes. The main hypothesis of this project is that Deep Learning techniques may pick up more subtle indicators, but all the same it makes sense to focus initially on more local, more significant events.

The Istituto Nazionale di Geofisica e Vulcanologia (INGV) provides heaps of data, local to Italy and worldwide. A recent event list can be found here. Of what they offer I found it easiest to code against their Atom feed which gives weekly event summaries. (No surprise I found it easiest, I had a hand in the development of RFC4287 🙂

I’ve put together some basic code for GETting and parsing this feed.

Radio Data

The go-to site for natural ELF/VLF radio information is vlf.it and it’s maintainer Renato Romero has a station located in northern Italy. The audio from this is streamed online (along with other channels) by Paul Nicholson. Reception, logging and some processing of this data is possible using Paul’s VLF Receiver Software Toolkit. I found it straightforward to get a simple spectrogram from Renato’s transmissions using these tools. I’ve not set up a script for logging yet, but I’ll probably get that done later today.

It will be desirable to visualise the VLF signal to look for interesting patterns and the best way of doing this is through spectrograms. Conveniently, this makes the problem of recognising anomalies essentially a visual recognition task – the kind of thing the Deep Learning literature is full of.

The Provisional Graph

Here we go –

provisional-nn-2017-07-03

CNN – convolutional neural network subsystem
RNN – recurrent neural network subsystem (probably LSTMs)
FCN – fully connected network (old-school backprop ANN)

This is what I’m picturing for the full training/runtime system. But I’m planning to set up pre-training sessions. Imagine RNN 3 and its connections removed. On the left will be a VLF subsystem and on the right a seismic subsystem.

Pre-Training

In this phase, data from VLF logs will be presented as a set of labeled spectrograms to a multi-layer convolutional network CNN. VLF signals contain a variety of known patterns, which include:

  • Man-made noise – the big one is 50Hz mains hum (and its harmonics), but other sources include things like industrial machinery, submarine radio transmissions.
  • Sferics – atmospherics, the radio waves caused by lightning strikes in a direct path to the receiver. These appear as a random crackle of impulses.
  • Tweeks – these again are caused by lightning strikes but the impulses are stretched out through bouncing between the earth and the ionosphere. They sound like brief high-pitched pings.
  • Whistlers – the impulse of a lightning strike can find its way into the magnetosphere and follow a path to opposite side of the planet, possibly bouncing back repeatedly. These sound like descending slide whistles.
  • Choruses – these are caused by the solar wind hitting the magnetosphere and sound like a chorus of birds or frogs.
  • Other anomalous patterns – planet Earth and it’s environs are a very complex system and there are many other sources of signals. Amongst these (it is assumed here) will be earthquake precursors caused by geoelectric activity.

Sample audio recordings of the various signals can be found at vlf.it and Natural Radio Lab. They can be quite bizarre. The key reference on these is Renato Romero’s book Radio Nature – strongly recommended to anyone with any interest in this field. It’s available in English and Italian (I got my copy from Amazon).

So…with the RNN 3 path out of the picture, it should be feasible to set up the VLF subsystem as a straightforward image classifier.

On the right hand side, the seismic section, I imagine the pre-training phase being a series of stages, at least with: seismic data->RNN 1; seismic data->RNN 1->RNN 2. If you’ve read The Unreasonable Effectiveness of Recurrent Neural Networks (better still, played with the code – I got it to write a Semantic Web “specification”) you will be aware of how good LSTMs can be at picking up patterns in series. But it’s pretty clear that the underlying system behind geological events will be a lot more complex than the rules of English grammar & syntax. But I’m (reasonably) assuming that sequences of events, ie predictable patterns do occur in geological systems. While I’m pretty certain that this alone won’t allow useful prediction with today’s technology, it should add information to the system as a whole in the form of probabilistic ‘shapes’. Work already done elsewhere would seem to bear this out (eg see A Deep Neural Network to identify foreshocks in real time).

Training & Prediction

Once the two subsystems have been pre-trained for what seems a reasonable length of time, I’ll glue them together, retaining the learnt weights. The VLF spectrograms will now be presented as a temporal sequence, and I strongly suspect the time dimension will have significance in this data, hence the insertion of extra memory in the form of RNN 3.

At this point I currently envisage training the system in real time using live data feeds.  (So the seismic sequence on the right will be time now, and the inputs on the left will be now-n). I’m not entirely sure yet how best to flip between training and predicting, worst case periodically cloning the whole system and copying weights across.

A more difficult unknown for me right now is how best to handle the latency between (assumed) precursors and events.  The precursors may appear hours, days, weeks or more before the earthquakes. While I’m working on the input sections I think I need to read up a lot more on Deep Learning & cross-correlation.

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Reading online VLF

For the core of the VLF handling section of the neural nets, my current idea couldn’t be much more straightforward. Take periodic spectrograms of the signal(s) and use them as input to a CNN-based visual recognition system. There are loads of setups for these available online. The ‘labeling’ part will (somehow) come from the seismic data handling section (probably based around an RNN). This is the kind of pattern that hopefully the network will be able to recognise (the blobby bits around 5kHz):

Screenshot from 2017-07-01 18-07-52

“Spectrogramme of the signal recorded on September 10, 2003 and concerning the earthquake with magnitude 5.2 that occurred in the Tosco Emiliano Apennines, at a distance of about 270 km from the station, on September 14, 2003.” . From Nardi & Caputo, A perspective electric earthquake precursor observed in the Apennines

It’ll be a while yet before I’ll have my own VLF receiver set up, but in the meantime various VLF receiver stations have live data online, available through vlf.it. This can be listened to in a browser, e.g. Renato Romero’s feed from near Turin at http://78.46.38.217:80/vlf15 (have a listen!).

So how to receive the data and generate spectrograms? Like a fool I jumped right in without reading around enough. I wasted a lot of time taking the data over HTTP from the link above into Python and trying to get it into a usable form from there. That data is transmitted using Icecast, specifically using an Ogg Vorbis stream. But the docs are thin on the ground so decoding the stream became an issue. It appears that an Ogg header is sent once, then a continuous stream. But there I got stuck, couldn’t make sense of the encoding, leading me to look back at the docs around how the transmission was done. Ouch! I really had made a rod for my own back.

Reading around Paul Nicholson’s pages on the server setup, it turns out that the data is much more readily available with the aid of Paul’s VLF Receiver Software Toolkit. This is a bunch of Unixy modules. I’ve still a way to go in putting together suitable shell scripts, definitely not my forte. But it shouldn’t be too difficult, within half an hour I was able to get the following image:

img

First I installed vlfrx-tools, (a straightforward source configure/make install, though note that in latest Ubuntu in the prerequisites it’s libpng-dev not libpng12-dev). Then ran the following:

vtvorbis -dn 78.46.38.217,4415 @vlf15

– this takes Renato’s stream and decodes it into buffer @vlf15.

With that running, in another terminal ran:

vtcat -E30 @vlf15 | vtsgram -p200 -b300 -s '-z60 -Z-30' > img.png

– which pulls out 30 seconds from the buffer and pipes it to a script wrapping the Sox audio utility to generate the spectrogram.

 

 

 

Loops, Oops!

While I’m very familiar with handling audio-frequency signals in electronics and have a basic understanding of how radio circuits work, there are huge gaps in my knowledge around radio waves, their propagation and reception, and rather a key part of radio system design: aerials.

My planned aerial design for VLF/ELF reception was three air-cored coils positioned in orthogonal directions, like this :

xyz-circles

(see also Hardware Issues)

Unlike, say FM aerials, these small loop antennas will pick up the magnetic component of the electromagnetic signal (as do the coils in typical AM radios). I assumed that the signal received on the N-S axis would be different from E-W and Up-Down. This seemed like the way to capture directional information, assuming the Up-Down direction would also be useful, ie. overall collecting as much information as possible.

Well, I’d had the Wikipedia page on Loop Antennas bookmarked for weeks, yesterday I finally read it, and got a surprise.  As Wikipedia puts it:

Small loop antennas are much less than a wavelength in size, and are mainly (but not always) used as receiving antennas at lower frequencies…

…Surprisingly, the radiation and receiving pattern of a small loop is quite opposite that of a large loop (whose circumference is close to one wavelength). Since the loop is much smaller than a wavelength, the current at any one moment is nearly constant round the circumference. By symmetry it can be seen that the voltages induced along the flat sides of the loop will cancel each other when a signal arrives along the loop axis. Therefore, there is a null in that direction. Instead, the radiation pattern peaks in directions lying in the plane of the loop, because signals received from sources in that plane do not quite cancel owing to the phase difference between the arrival of the wave at the near side and far side of the loop. Increasing that phase difference by increasing the size of the loop has a large impact in increasing the radiation resistance and the resulting antenna efficiency.

So my planned N-S coil would actually have nulls in those directions, similarly for E-W, and the Up-Down coil would in effect be omnidirectional NSEW. D’oh!

There is a lot to electromagnetic radiation! (eg. near vs. far field reception is something I need to read up on). It’s weird stuff.

220px-Electromagneticwave3D

Reading around the topic a little more, the null positions are the key to radio direction finding (RDF). A coil will have two nulls, 180° apart, which RDF gets around by adding a sense antenna, which may be a simple vertical whip aerial. This will be omnidirectional and (if I understand correctly) when summed at the right levels with the coil signal will effectively remove one of the nulls.

Luckily, hardware-wise I’m still at the planning/experimentation stage, so haven’t wasted too much time winding coils. Looks like I’ll have to change the design, provisionally having N-S and E-W coils plus a sense antenna, a la RDF. There are lots of designs for all kinds of aerials and receivers at VLF.it (I’ve just started assembling a list of reference links, that site is at the top of the list).