Comment by nikhil-shankar

I suspect (possibly incorrectly) that earthquakes are a chaotic phenomenon resulting from a multilayered complex system, a lot like a lottery ball picker.

Essentially random outputs from deterministic systems are unfortunately not rare in nature…. And I suspect that because of the relatively higher granularity of geology vs the semicohesive fluid dynamics of weather, geology will be many orders of magnitude more difficult to predict.

That said, it might be possible to make useful forecasts in the 1 minute to 1 hour range (under the assumption that major earthquakes often have a dynamic change in precursor events), and if accuracy was reasonable in that range, it would still be very useful for major events.

Looking at the outputs of chaotic systems like geolocated historical seismographic data might not be any more useful than 4-10 orders of magnitude better than looking at previous lottery ball selections in predicting the next ones…. Which is to say that the predictive power might still not be useful even though there is some pattern in the noise.

Generative AI needs a large and diverse training set to avoid overfitting problems. Something like high resolution underground electrostatic distribution might potentially be much more predictive than past outputs alone, but I don’t know of any such efforts to map geologic stress at a scale that would provide a useful training corpus.

They’re empiricists — the only ~~real~~ conclusive way to answer that question is to try it, IMO!

The old ML maxim was “don’t expect models to do anything a human expert couldn’t do with access to the same data”, but that’s clearly going to way of Moore’s Law… I don’t think a meteorologist could predict 11km^2 of weather 10 days out very accurately, and I know for sure that a neuroscientists couldn’t recreate someone’s visual field based on fMRI data!