The destructive 2009 L’Aquila and 2012 Emilia-Romagna earthquakes led the Italian Dipartimento della Protezione Civile (DPC) to fund nine research groups to investigate seismic precursors. Three research groups produced testable predictions by the DPC deadline of May 31, 2013, based on: 1) radon in a well in Friuli, 2) temperature, flow, CO2 flux, and other variables measured in wells in Emilia-Romagna, and 3) an Artificial Neural Network (ANN) algorithm applied to seismicity. We evaluated the geochemical precursors by comparing their predictions to an equal number of predictions at the same locations and with the same individual and total durations as the actual predictions, but at random times. This approach avoids modelling the seismicity, so the accuracy of the predictions is not influenced by the accuracy of any seismicity model. Neither set of geochemical precursors succeeds significantly better than the random predictions. ANN, on the other hand, did not predict any events large enough to affect public safety.

Purported precursors: poor predictors

MULARGIA, FRANCESCO;GASPERINI, PAOLO;LOLLI, BARBARA;
2015

Abstract

The destructive 2009 L’Aquila and 2012 Emilia-Romagna earthquakes led the Italian Dipartimento della Protezione Civile (DPC) to fund nine research groups to investigate seismic precursors. Three research groups produced testable predictions by the DPC deadline of May 31, 2013, based on: 1) radon in a well in Friuli, 2) temperature, flow, CO2 flux, and other variables measured in wells in Emilia-Romagna, and 3) an Artificial Neural Network (ANN) algorithm applied to seismicity. We evaluated the geochemical precursors by comparing their predictions to an equal number of predictions at the same locations and with the same individual and total durations as the actual predictions, but at random times. This approach avoids modelling the seismicity, so the accuracy of the predictions is not influenced by the accuracy of any seismicity model. Neither set of geochemical precursors succeeds significantly better than the random predictions. ANN, on the other hand, did not predict any events large enough to affect public safety.
2015
Mulargia, F.; Gasperini, P.; Lolli, B.; Stark, P. B.
File in questo prodotto:
Eventuali allegati, non sono esposti

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/11585/545171
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact