I don't know anything about seismology, and I am going to put aside the money and focus on the math. > The features you're interested in are almost never what'

I don't know anything about seismology, and I am going to put aside the money and focus on the math. > The features you're interested in are almost never what's clearly imaged -- instead, you're predicting what's in that unimaged, "mushy" area over there through fundamental laws of physics like conservation of mass and understanding of the larger regional context. Those are deeply difficult to incorporate in machine learning in practice. I was part of a university research lab over 15 years ago that was doing exactly this [1], with just regular old statistics (no AI/ML required). By modeling the variability of the stuff that you could see easily, you could produce priors strong enough to eek out the little bit of signal from the mush (which is basically what the actual radiologists do, which we know because they told us). It isn't a turn-key, black box solution like deep learning pretends to be. It takes a long time, it is highly dependent on getting good data sets, and years of labor goes into a basically bespoke solution for a single radiology problem, but the results agree with a human as closely as humans agree with each other. You also get the added bonus of understanding the relationships you are modeling when you are done. From university lab to clinically proven diagnosis tool is of course a longer road, and I have not been involved in these projects for a long time, but my point is that the math problem on its own is tractable. [1] http://midag.cs.unc.edu/ http://midag.cs.unc.edu/