After watching the interview with Susan Murphy and Brendan Meade, I was most surprised to learn that the current tools we have with numerical simulations are not enough to predict earthquakes. I thought that there have been enough earthquakes throughout history to for there to be sufficient data to calibrate simulation models, but it seems that we do not have enough data either, especially for larger magnitude earthquakes, which are certainly ones that we would like to predict. It was interesting to hear that we may be at a limit of being able to comfortably make predictions, which I had not thought to be possible given how much the field of deep learning has accelerated in recent years. However, I do recognize that uncertainty, as this has been the core of discussion with regard to predictions, is extremely difficult to tune, especially when attempting to predict rare events like earthquakes.
One question I would like to ask both professors, but more so to Professor Meade, is how to communicate with the people who are likely to be affected by a predicted earthquake. For the Sendai earthquake that he mentioned (and semi-predicted, which I was also surprised about), I know that the impacted communities would not have left their homes even if they were told beforehand that they would be hit, because many of them were so deeply connected to the community there and cherished the familial histories rooted in their hometowns. How can we have them prepare for an earthquake of such large scale? What if they are not cooperative? What authorities do we have as forecasters to protect the communities while respecting their decisions?
Victoria, I really like your post and your additional question about how we can effectively communicate risk to communities in spite of their possible (but reasonable) unwillingness to respond to these forecasts. For my final project, I am researching hurricane prediction, where citizens' response to warnings, or lack thereof, is a large problem. I can see how this process would only be made more difficult in a setting where we have even less confidence in the precise predictions made by the models at hand. Like the current state of earthquake prediction, as little as 80 years ago it was extremely difficult to know when and where a hurricane would hit. Hopefully, as computational and algorithmic tools continue to develop, we can one day reach the same levels of accuracy with earthquake prediction that we have with hurricane forecasting.