Data Sources

Two data sources are key to this project: natural VLF/ULF radio signals and seismic vibration. Other environmental signals have been examined in relation to earthquake prediction, several are described on Wikipedia. Three are of note here:

Prior seismic events – in the long term, patterns are recognisable in the progress of earthquake systems. In the short term, major events are often associated with numerous lower magnitude events, after the fact these get classified into foreshocks, mainshocks or aftershocks. Given that seismic signals will be captured by the system proposed here, no extra effort is needed to include these factors into analysis.

Radon gas emissions – while still the subject of ongoing research, spikes in geological radon emissions have been noted preceding earthquakes. Measuring the typical level of radon at a given location is relatively straightforward, by accumulating particulate matter at the location over weeks or months and then measuring its radioactivity, exposing the contribution of radon decay products. But continuous monitoring is much more involved given the low levels of radioactivity involved. Given this difficulty, the radon approach isn’t a high priority in this project. Maybe later.

Animal Behaviour – there is anecdotal evidence that animals exhibit unusual activity prior to an earthquake. This will be left out of scope here due to the difficulty in exploiting such a phenomenon (if it exists). Also, I have two dogs and they displayed absolutely nothing out of the ordinary prior to the last tremor we felt here, they remained asleep. During and after the tremor, they were perturbed…

PS. See Biological Anomalies around the 2009 L’Aquila Earthquake – a lot were reported.

There are additional environmental signals that may have little or no correlation with seismic events but might still improve prediction.

Earth’s magnetic field – the geomagnetic field changes over time and is linked to variations in geology, especially in the Earth’s core. The proposed VLF/ELF receivers will pick up rapid variations in this field, additionally given that it is relatively straightforward to detect ultra-low frequency changes, it will probably be useful to gather this data.

Static electrical field – similar to the magnetic field, the ‘static’ field at a given location can be seen as the ultra-low frequency aspect of signals that the VLF/ELF receivers will pick up. ‘Spherics’, the radio signals generated by lightning strikes are a major feature of VLF/ELF radio signals, and nearby thunderstorms strongly modify the local static electrical field. Again, such data may well be a useful contribution to the system, and measuring this field is relatively straightforward.

Solar and lunar cycles – these bodies exert significant gravitational forces over the Earth and hence are likely to be a factor in geological changes. It should be straightforward to include such data in analysis.

Sunspot activity – whether or not solar discharges have an influence on the occurance of earthquakes (it seems unlikely), they have a major impact on radio signals. It should be feasible to find an online source of related data and include it in the analysis.

Ambient temperature – the reception of radio waves is influenced by the weather and temperature is a key aspect. Also any other measurement circuitry will, to some extent, suffer from temperature drift. Being able to effectively subtract this factor from other signals may help improve results. Temperature sensors are relatively easy to set up, so measuring this seems worthwhile.

Acoustic noise – ie. ambient audio. As with temperature, changes here are is likely to be picked up to some extent by other sensors, especially the seismic subsystem. For example, tractors quite often pass by here, causing a deep rumble that is likely to be detected as a seismic signal. It will be useful to have the corresponding sound signal to help discount such artificial signals. Recording audio is simple so again this seems worthwhile.

[[TODO – move this paragraph to an overview page]] It should be noted that as no completely reliable prediction method has yet been discovered, the general attitude surrounding any approach is that of scepticism. This mood is amplified by the political aspects of prediction – action on a prediction (eg. mass evacuation) is liable to be expensive, so false positives will be costly. Additionally the lack of prediction advice when a major event has occurred can have significant fallout. A notable example being the charging of six Italian scientists with manslaughter following the devastating 2009 l’Aquila earthquake. They were subsequently acquitted (though a government official’s charges were upheld). That such charges were even considered shows the sensitivity in this field.


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 :


(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.


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 (I’ve just started assembling a list of reference links, that site is at the top of the list).



Hardware Issues

[work in progress 2017-04-15]

Hardware Overview

(I initially made a significant mistake in my radio reception plans, see Loops, Oops!)

I had hoped by now to have prototypes for a radio receiver and seismograph ready in hardware. What I have got on that front is an increased awareness of how tricky they are likely to be. I still think I’ve got a reasonable design for the sensor part of the seismograph.

Noise Annoys

On the radio side, the experiments I’ve done so far have only really shown that the signal/noise ratio I need to deal with is even worse than I imagined. This is largely due to my location. Although this is a very small village in the hills, everywhere you look are overhead mains power lines. To get a signal that won’t be swamped with noise I anticipate having to filter out at least mains hum at 50Hz and 100Hz (possibly even higher harmonics) in analog signal conditioning. A standard circuit that can do this job is the twin-T notch filter. By wrapping it around a couple of op-amps it can be bootstrapped, boosting the Q, hence the depth & narrowness of the notch can be increased. The big problem here is component tolerance. Metal film resistors are pretty good, having 1% accuracy, but capacitors are another story – anything better than 10% is quite specialist (and that’s not even taking into account effects of thermal variation). Some level of manual tweaking seems unavoidable, worst case 4 trim pots per filter (3 in the T, one for the feedback = Q).

My plan to make a receiver consisting of 3 orthogonal coils will have to remain on hold until I have some funds available (it sounds crazy, but wire looks like it’s going to be the greatest expense). I do have the bits to play with a single channel. And that’s also simpler.

Data Acquisition

I haven’t written it up yet, but once I’ve got usable analog signals from the radio, seismo and other* sensors, I want to use dedicated hardware to get them into the digital domain. Not only that, ideally I want this part of the system to make the raw data available on the Web. I think it makes sense to use a subsystem to collect the data independently of processing, to keep things modular. That way the subsystems can be developed in isolation and additionally the processing workload can be more distributed. So essentially I will have analog-to-digital converters (ADCs) attached to one or more small computers. The physical location of sensors is a consideration – away from sources of interference for the radio receivers, solidly attached to the ground for the seismic sensors. It is necessary in each case to have some of the signal conditioning circuitry local to the sensor (at least pre-amplification).

I then need to get the preconditioned signal from the sensors through the ADCs into the dedicated processing subsystems and on again to the part that will do the processing. The most elegant approach I can think of is also having the ADCs & dedicated computers local to the sensors, with the data being delivered to subsequent processing over WiFi. (Care will be needed in shielding the digital electronics to avoid interference).  An existing standard transfer protocol will be desirable (probably TCP or UDP), and from there it’s only a small step to proxying it onto the Web.

Given this setup, and the considerations of location, modularity and performance, it will make sense to use one ADC+computer subsystem for the radio receivers and one for the seismic sensors.

*  for reasons described below, I plan to eventually detect signals in 3D, ie. both the radio and seismic units having a N-S, E-W and vertical element. But most ADC boards have their number of channels as multiples of 2. Hence I intend to use 2 x 4 channel ADCs, and use the 2 spare channels for acquiring other environmental signals – provisionally acoustic noise (audio) and ambient temperature (again, there’s rationale below).

Two small species of small, inexpensive, general-purpose computers stand out from the marketplace: Raspberry Pi and Arduino. Now I’ve spent a lot of time around digital audio and rather naively assumed that suitable ADCs would be ten a penny. Not so.

For the radio signals, ideally I want to capture something roughly equivalent to fair quality audio, ie. say a bandwidth of 20kHz with 16 bit precision. ADCs that can do this are available of-the-shelf in the recording part of regular soundcards. Unfortunately, there doesn’t appear to be anything available that’s inexpensive and interfaces easily with either computer board (and I have read of issues with data transfer bottlenecks when using USB devices). However I have found a card that appears to fit the bill, for the Arduino : the Mayhew Labs Extended ADC Shield.

Progress (of sorts)

[work in progress 2017-04-15 – material was getting long so splitting off to separate pages]

General Status

In the last month or so I’ve done a little hardware experimentation on the radio side of things, along with quite a lot of research all around the subjects. I also had a small setback. About 3 months ago I ordered a pile of components, they never arrived, a screw-up by the distributor (I wasn’t charged). Unfortunately I don’t have the funds right now to re-order things, so although I do have the resources for some limited experimentation, I can’t go full speed on the hardware. (Once I’ve got a little further along, I reckon I’ll add a ‘Donate’ button to this site – worth a try!)

This might be a blessing in disguise. Being constrained in what I can do has forced me to re-evaluate the plans. In short, in the near future I’m going to have to rely mostly on existing online data sources and simplify wherever possible. This is really following the motto of Keep It Simple, Stupid, something I should maybe have been considering from the start.


Next Steps

Neural Network Strategies

[work in progress 2017-04-15]


Provisional Architecture



Provisional Network Architecture
A Critical Issue

If we assume that significant seismic events are indeed preceded by distinctive radio emissions generated by geological stresses, and that these patterns may be detected reasonably reliably, there remains at least one significant issue in applying them to the prediction problem. This the time delay between the radio event and the seismic event.

Reports in the literature describe a latencies anywhere between hours and weeks, and it seems likely that this may confound the association between earthquakes and precursors. There could be any number of unknown variables in the physical environment affecting the generation and propagation of the radio signal. For all practical purposes these are effectively random, and so probably best considered another form of noise – in this case in the temporal dimension.

It may be that the radio signal patterns of precursors have recognisable correlation with not only the ensuing seismic events but the length of time before those events. Another (optimistic) possibility is that the neural networks applied will learn this correlation.

At this stage, the best bet seems experimentation…