Volcanic Anomalies Detection Through Recursive Density Estimation

The volcanic conditions of Latin America and the Caribbean propitiate the occurrence of natural disaster in these areas. The volcanic-related disasters alter the living conditions of the populations compromised by their activity. We propose to use Recursive Density Estimation (RDE) method to detect volcanic anomalies. The different data used for the design and evaluation of this method are obtained from Purace volcano of two surveillance volcanic areas: Geochemistry and Deformation. The proposed method learns quickly from data streams in real time and the different volcanic anomalies can be detected taking into account all the previous data of the volcano. RDE achieves good performance in the outliers detection; 82% of precision for geochemestry data, while 77% of precision in geodesy data.