Automatic fault interpretation based on point cloud fitting and segmentation

Faults generated by seismic motion and stratigraphic lithology changes are essential research objects for seismic motion and hydrocarbon prospecting. This paper emphatically concentrates on the fault reconstruction from the existing fault probability volume. The core idea is to transform the separation of different fault sticks into a fitting and segmentation problem of point cloud data. First, we utilize the point cloud filtering algorithm to preprocess the probability volume and then complete the coarse segmentation of the fault sticks by the region growth algorithm. For the intersecting faults, we employ an enhanced random sample consensus methodology with the constraints of fault orientation and effective inliers to accomplish the detailed segmentation of different fault sticks. Finally, we take the faults identified by the region growth and the random sample consensus method as a priori to construct a random forest model to predict the fault sticks of additional data. By examining and comparing the proposed method with some other approaches with both synthetic and field data, the experimental results manifest that the novel method achieves better segmentation results than others. Moreover, the proposed method is efficient based on the fact that it can handle billions of voxels within a few minutes.