Linear Pattern Detection of Geological Faults via a Topology and Shape Optimization Method

Geological faults comprise an interesting and of high-importance subject of study in geosciences, since they are responsible for seismic activity in regions where plate boundaries (divergent, convergent, transform) are present. In this paper, we propose a framework for automatic enhancement and identification of the linear patterns of geological fault structures. According to the proposed method, first, we compute the Slope and Aspect images, as well as their derivatives. Then, a rotation- and scale-invariant filter and a pixel-labeling method provide an enhancement of the geological faults and the detection of points that probably belong to them, respectively. The enhancement and detection results are efficiently combined to construct a weighted graph that represents the possible fault points and the connections between them taking into account the shape and topology of the faults. Thus, the detection of the linear fault patterns, which is the main goal of this framework, is reduced to the problem of suitable subset selection, corresponding to the graph edges of the most representative faults. Concerning the experimental results, the proposed method has been tested on several onshore and offshore real topographic images yielding high-performance results.