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actual paper? https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/doi/10.1785/0120230031/627949/Earthquake-Forecasting-Using-Big-Data-and?redirectedFrom=

actual paper? https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/doi/10.1785/0120230031/627949/Earthquake-Forecasting-Using-Big-Data-and?redirectedFrom=fulltext https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/d... the abstract: > The proposed algorithm is trained using the available data from 2016 to 2020 and evaluated using real‐time data during 2021. As a result, the testing accuracy reaches 70%, whereas the precision, recall, and F1‐score are 63.63%, 93.33%, and 75.66%, respectively.

jldugger 3y ago
from UT's press office, https://news.utexas.edu/2023/10/05/ai-driven-earthquake-forecasting-shows-promise-in-trials/ https://news.utexas.edu/2023/10/05/ai-driven-earthquake-fore... > The researchers said that their method had succeeded by following a relatively simple machine learning approach. The AI was given a set of statistical features based on the team’s knowledge of earthquake physics, then told to train itself on a five-year database of seismic recordings. One common mistake with timeseries forecasting is leaking the evaluation set. It would be relatively easy to accidentally record the future into a DNN.