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Researchers from the Universities of California at Berkeley and Santa Cruz, and the Technical University of Munich have released a paper describing a new model that delivers deep learning to earthquake forecasting.
“There’s a whole field of research that explores how to improve ETAS,” says Dacher-Cousineau, a postdoctoral researcher at UC Berkeley. “It’s an immensely useful model that has been used a lot, but it’s been frustratingly hard to improve on it.”The promise of Recast is that its model flexibility, self-learning capability and ability to scale will enable it to interpret larger datasets and make better predictions during earthquake sequences, he says.
The Recast model architecture builds on developments in neural temporal point processes, which are probabilistic generative models for continuous time event sequences. In a nutshell, the model has an encoder-decoder neural network architecture used for predicting the timing of a next event based on a history of past events.
Enhanced data is meanwhile helping to fill the void. With the labeled data in earthquake catalogues, machine learning engineers are revisiting these sources of raw data and building enhanced catalogs to get 10x to 100x the number of earthquakes for training data and categories.
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