A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction

The use of different seismicity indicators as input for systems to predict earthquakes is becoming increasingly popular. Nevertheless, the values of these indicators have not been systematically obtained so far. This is mainly due to the gap of knowledge existing between seismologists and data mining experts. In this work, the effect of using different parameterizations for inputs in supervised learning algorithms has been thoroughly analyzed by means of a new methodology. Five different analyses have been conducted, mainly related to the shape of training and test sets, to the calculation of the b-value, and to the adjustment of most collected indicators. Outputs sensitivity has been determined when any of these factors is not properly taken into consideration. The methodology has been applied to four Chilean zones. Given its general-purpose design, it can be extended to any location. Similar conclusions have been drawn for all the cases: a proper selection of the sets length and a careful parameterization of certain indicators leads to significantly better results, in terms of prediction accuracy.