Intelligent velocity picking considering an expert experience based on the Chan–Vese model and mean-shift clustering - Wang, Li-De; Wu, Jie; Xu, Xing-Rong; Zeng, Hua-Hui; Gao, Yang; Liu, Wen-Qing (Frontiers in Earth Science, 2023)
The velocity of seismic data can be initially established by identifying energy clusters on velocity spectra at different moments, which is crucial to the migration imaging and the stacking of common midpoint (CMP) gathers in the seismic data processing. However, the identification of energy clusters currently relies on manual work, with low efficiency and different standards. With the increasing application of wide-frequency, wide-azimuth and high-density seismic exploration technology, the amount of seismic data has increased significantly, greatly increasing the cost of manual labor and time. In this paper, an intelligent velocity picking method based on Chan-Vese (CV) model and Mean-Shift Clustering algorithm was proposed, which improved the accuracy and efficiency of the velocity picking. Firstly, a velocity trend band is set up on the velocity spectrum by experts to avoid multiples and other noise. Then, the velocity trend band is applied to the CV model as the initial time condition to segment velocity spectrum and obtain the velocity candidate region. Finally, the Mean-Shift Clustering is adopted to cluster the useful energy clusters retained in the candidate region derived from CV. When implementing the Mean-Shift Clustering algorithm, the Gaussian kernel function and the energy of velocity spectrum are utilized to weigh the efficiency and accuracy of the cluster. Furthermore, compared with K-means and manual picking, the tests of model and real data prove that the proposed method can dramatically improve the accuracy and efficiency in velocity picking.