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Authors: Mulargia, F.* 
Gaperini, P.* 
Lolli, B.* 
Stark, P. B.* 
Title: Purported Precursors: Poor Predictors
Issue Date: 2015
Series/Report no.: 2/56 (2015)
Keywords: seismic precursors, statistical analysis
Subject Classification05. General::05.01. Computational geophysics::05.01.04. Statistical analysis 
Abstract: The destructive 2009 L’Aquila and 2012 Emilia Romagna earthquakes led the Italian Dipartimento della Protezione Civile (DPC) to fund nine groups studying seismic precursors. Three of the groups produced testable predictions by the DPC deadline of 31 May 2013, using: (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 success to that of 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 modeling seismicity and thereby precludes concluding that predictions are “good” simply because the model for seismicity is bad. Neither precursor predicts significantly better than chance. ANN was a poor predictor of events large enough to affect public safety.
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