A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new
methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the
original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning
clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search
strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus
discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been
applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a
remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false
positives has decreased and the positive predictive values increased, both of them in a very remarkable manner.Ministerio de Ciencia y Tecnología TIN2011-28956-C00Junta de Andalucía P12-TIC-1728Instituto Ramón y Cajal (RYC) RYC-2012-1198
