Bluntly, no. There hasn't been an improvement. At all. We've been using machine learning in geology for far longer than it's been called that. Hell, we inven

Bluntly, no. There hasn't been an improvement. At all. We've been using machine learning in geology for far longer than it's been called that. Hell, we invented half the damn methods (seriously). Inverse theory is nothing new. Gaussian processes have been standard for 60 years. Markov models for stratigraphic sequences are commonly applied but again, have been for decades. What hasn't changed at all is interpretation. Seimsic inversion is _very_ different from interpretation. Sure, we can run larger inverse problems, so seismic inversion has definitely improved, but that has no relationship at all to interpretation. Put another way, to do seismic inversion you have to already have both the interpretation _and_ ground truth (i.e. the well and a model of the subsurface). At that point, you're in a data rich environment. It's a very different ball game than trying to actually develop the initial model of the subsurface with limited seismic data (usually 2d) and lots of "messier" regional datasets (e.g. gravity and magnetics).