Spatial Sequential Pattern Mining for Seismic Data
Riccardo Campisano, Fabio Andre Machado Porto, Esther Pacitti, Florent Masséglia, Eduardo Ogasawara · 2016
A myriad of applications from different domains collects time series data for further analysis. In many of them, such as seismic datasets, the observed data is also associated to a space dimension, which corresponds, in fact, to spatial-time series. The analysis of these datasets is difficult due to both the continuous nature of the observed data and the relationship between spatial and time dimensions. Meanwhile, sequential patterns mining techniques have been successfully used in large volume of transactional databases to obtain insights from data. In this work, we start exploring the discovery of frequent sequential patterns in seismic datasets. For that, we discretize continuous values into symbols and adapt well known sequential algorithm to mine spatial-time dataset. To better understand the quality of the identified patterns, we visualize them over the original seismic traces images. Our preliminary results indicate that the study of sequence mining in seismic datasets is promising.