Seismic Fault Detection Based on a Curvilinear Support

In this paper, we present a new approach for seismic fault detection. Our goal is to increase the detection accuracy by computing some classical attributes on a support founded on an a priori knowledge about the faults. Two forms of support are proposed: one approximating the fault by a set of linear sub-segments of fixed length, the other founded on a more complex curved support which aims to describe the whole fault system. In the second case, computing all the possible configurations to detect the real location of the faults is illusory; then, we propose a fault detection algorithm based on a stochastic approach. One interest of this approach is the possibility of using a common support for different fault detection operators. Then a whole detection framework can be proposed which acts like a decision fusion process.