The goal for this project is to find geological structures or objects that indicate the presence of reservior of valuable natural resources like oil and gas. The geological structures we are interested in include faults, channels, domes,etc. Because of the complexity and variablity of these structures, it usually takes huge amount of time for the geologists to interpret the seimic data to find them.
In this project, we develop a framework to automatically detect the geological structures, using PCA based/mahalanobis distance based outlier/novelty detection methods. These interesting structures are detected as abnormalises which can be separated from the normal horizons or strata that consist of the backgound.
On top of that, we propose a hierarchical clustering-based pipeline for geological structure recognition, which gives a stastical models for the target structures in terms of configuration of codes/labels, and then using the models to detect new objects of the same category in seismic data.