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 reviewed the literature of artificial neural network (ANN) applications for earthquake prediction (77 articles, 1994-2019 period) and found two emerging trends: an increasing interest in this domain over time and a complexification of ANN models toward deep learning. Despite the relatively positive results claimed in those studies, we verified that far simpler (and traditional) models seem to offer similar predictive powers, if not better ones. Those include an exponential law for magnitude prediction and a power law (approximated by a logistic regression or one artificial neuron) for aftershock prediction in space. Because of the structured, tabulated nature of earthquake catalogs, and the limited number of features so far considered, simpler and more transparent machine-learning models than ANNs 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 (e.g., the Gutenberg-Richter law), which are already known to have minimal abilities to predict large earthquakes.

Neural network applications in earthquake prediction (1994-2019): Meta-analytic and statistical insights on their limitations / Mignan, A.; Broccardo, M.. - In: SEISMOLOGICAL RESEARCH LETTERS. - ISSN 0895-0695. - 91:4(2020), pp. 2330-2342. [10.1785/0220200021]

Neural network applications in earthquake prediction (1994-2019): Meta-analytic and statistical insights on their limitations

Broccardo M.
2020-01-01

Abstract

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 reviewed the literature of artificial neural network (ANN) applications for earthquake prediction (77 articles, 1994-2019 period) and found two emerging trends: an increasing interest in this domain over time and a complexification of ANN models toward deep learning. Despite the relatively positive results claimed in those studies, we verified that far simpler (and traditional) models seem to offer similar predictive powers, if not better ones. Those include an exponential law for magnitude prediction and a power law (approximated by a logistic regression or one artificial neuron) for aftershock prediction in space. Because of the structured, tabulated nature of earthquake catalogs, and the limited number of features so far considered, simpler and more transparent machine-learning models than ANNs 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 (e.g., the Gutenberg-Richter law), which are already known to have minimal abilities to predict large earthquakes.
2020
4
Mignan, A.; Broccardo, M.
Neural network applications in earthquake prediction (1994-2019): Meta-analytic and statistical insights on their limitations / Mignan, A.; Broccardo, M.. - In: SEISMOLOGICAL RESEARCH LETTERS. - ISSN 0895-0695. - 91:4(2020), pp. 2330-2342. [10.1785/0220200021]
File in questo prodotto:
File Dimensione Formato  
J17_2020_SRL.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.78 MB
Formato Adobe PDF
2.78 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/276272
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 68
  • ???jsp.display-item.citation.isi??? 53
social impact