Given the swath of sensors that Japan has, and the long history of a lot of them. I do wonder what the result of training off their datasets would be.

This is interesting. Can you share the model&#x2F;github?

So when&#x2F;where is the next big one coming?

So I&#x27;ve been working on this seismic intelligence system (GSIN) and I think I accidentally made data centers kind of obsolete for this type of work. Let me explain what happened.
The Problem:
Earthquake forecasting sucks. The standard models are all statistical bullshit from the 80s. They don&#x27;t understand physics, they just pattern match on historical data. And the few ML attempts that exist? They need massive compute clusters or AWS bills that would bankrupt a small country.
I&#x27;m talking researchers spending $50k on cloud GPUs to train models that still don&#x27;t work that well. Universities need approval from like 5 committees to get cluster time. It&#x27;s gatekept as hell.
What I Built:
I took 728,442 seismic events from USGS and built a 3D neural network that actually understands how stress propagates through rock. Not just pattern matching - it learns the actual physics of how earthquakes trigger other earthquakes.
The architecture is a 3D U-Net that takes earthquake sequences and outputs probability grids showing where aftershocks are likely. It&#x27;s trained on real data spanning decades of global seismic activity.
Here&#x27;s the crazy part:
The entire training pipeline runs on a single RTX 5080. $1000 GPU. Not a cluster. Not AWS. Just one consumer card.Pre-loads all 15GB of training data into RAM at startup
Zero disk reads during training (that&#x27;s the bottleneck everyone hits)
Uses only 0.2GB of VRAM somehow
Trains 40 epochs in under 3 hours
Best validation Brier score: 0.0175For context, traditional seismic models get Brier scores around 0.05-0.15. Lower is better.

I just trained a physics-based earthquake forecasting model on a $1000 GPU