Anisotropic 3D seismic features for robust horizons correlation across faults

While 3D seismic data become widespread and the data-sets get larger, the demand for automation to speed up the seismic interpretation process is increasing as well. However, the development of intelligent tools which can do more to assist interpreters has been difficult due to low information content in seismic data. In this paper, we present an image processing method in which a-priori geological knowledge is incorporated to correlate horizons across faults. Our new method exploits anisotropic spatial correlation of horizons for robustness and aims at developing an interpreter friendly interactive environ- ment. The results of this method are compared with previously proposed methods. I. I NTRODUCTION Seismic images are pictures of underground structures. They are obtained by sending seismic waves from a surface and recording the reflected signals due to changes in acoustic impedance of underground layers. Three dimensional seismic data are a sequence of slices, which image cross-sectional layers at different depths (5). Interpretation of faults and horizons are the backbone of seismic data interpretation. Horizons are layered rocks which are created through a long time sedimentation process. A faulting process cuts and displaces horizons. Accordingly, faults are identified in seismic data as lateral discontinuities of horizons. Unless erosion occurred, the faulted horizons usually have their corresponding part on the other side of the fault. The correspondence analysis between horizons across a fault is important for describing a fault throw function, which influences exploration decision. Thus, correlation of horizons across faults is an indispensable task of seismic interpretation. Seismic data interpreters locate faults as lines from hori- zon discontinuities on seismic slices (3). Horizon are then connected across faults on the basis of reflection character and geological reasoning. Interpreters evaluate their correlation decision on 2-d slices (see fig. 1). The 3-d nature of the data set can be appreciated only if a sequence of slices is displayed. This is done frequently as the information from a single slice is often inconclusive. Identification of the geological features in seismic sections by an interpreter is time consuming and subjective. The main focus of our research is developing a computer- based methodology for correlation of horizons across faults. Expected outcomes are to reduce the time-consuming manual task, and to avoid the uncertainties associated with fault