She doesn’t have a science/technology/coding background, but asked me. But she does understand purple prose, mailed her this:
This is my view : it has been observed that many earthquakes have been preceded by unusual electrical activity in the atmosphere, and this has been made evident in the Extremely Low Frequency/Very LF radio bands.
These happen to be the frequency bands that audio works at, so a lot of existing technologies can be applied to its analysis (although the signals are radio rather than acoustic). There are plausible scientific explanations for these phenomena – if virtually any kind of rock is heavily compressed or stretched it generates electricity, at least some of which is expressed as radio waves. Rocks get squeezed and pulled before a split. Research to date has generally been inconclusive, even negative on this having potential for predicting earthquakes.
The biggest problem in my opinion is that there is a massive amount of unrelated noise in the system – things like lightning strikes are very loud in the wavebands in question, let alone solar flares. But recent developments in machine learning, aka Deep Learning, have been very good at pulling out features of interest from a very confused situation.
My hypothesis is that modern neural nets will be able to detect patterns well enough (beyond what traditional techniques can find) to provide an early warning system. Don’t get me wrong, I’m only half convinced myself, but if there’s a 10% of the idea making sense, 10% chance of it being codable (that bit’s 100%, but hypothetically) and a 10% chance of being able to exploit it, I’ll be out in the garden in a tent and will live to talk bollocks again. Or come back in shame-faced – but still alive.