Neural Network Applications in Earthquake Prediction (1994-2019): Meta-Analytic Insight on their Limitations
This meta-analysis suggests that incremental improvements in neural network complexity may not be justified for earthquake prediction, given current data limitations.
The paper reviewed 77 neural network studies for earthquake prediction from 1994 to 2019, finding that simpler models often match or outperform complex deep learning approaches in this domain.
In the last few years, deep learning has solved seemingly intractable problems, boosting the hope to find approximate solutions to problems that now are considered unsolvable. Earthquake prediction, the Grail of Seismology, is, in this context of continuous exciting discoveries, an obvious choice for deep learning exploration. We review the entire literature of artificial neural network (ANN) applications for earthquake prediction (77 articles, 1994-2019 period) and find two emerging trends: an increasing interest in this domain, and a complexification of ANN models over time, towards deep learning. Despite apparent positive results observed in this corpus, we demonstrate that simpler models seem to offer similar predictive powers, if not better ones. Due to the structured, tabulated nature of earthquake catalogues, and the limited number of features so far considered, simpler and more transparent machine learning models seem preferable at the present stage of research. Those baseline models follow first physical principles and are consistent with the known empirical laws of Statistical Seismology, which have minimal abilities to predict large earthquakes.