摘要：In seismic interpretation, stacking velocity is mainly acquired by manual picking from velocity spectra, which is time-consuming and highly susceptible to human experience. To improve the picking efficiency, we develop an automatic velocity picking approach based on an adaptive threshold constrained unsupervised clustering. A sliding time window is applied on velocity spectra to seek adaptive thresholds, which contribute to identifying effective energy clusters and construct candidate set from the identified eligible points. The clusters in the candidate set are automatically assigned into different groups by the classical K-means clustering method. The ultimate centroids of each group are marked as the stacking velocities picked automatically. Based on the experience of manual interpretation, we introduce a brief post processing procedure to eliminate velocity outliers and obtain a smooth velocity field. Both synthetic and real data tests demonstrate that our proposed method can significantly relieve the burden of manual labor, improve the efficiency and in the meanwhile retain relative high accuracy.
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ISSN号：0001-5733

卷、期、页：v 64,n 3,p1048-1060

发表日期：2021-03-10

影响因子：0.811100

期刊分区（SCI为中科院分区）：三区

收录情况：SCIE（科学引文索引网络版）,北大中文核心期刊,EI（工程索引）

发表期刊名称：Acta Geophysica Sinica

参与作者：袁焕,曾华会

通讯作者：王迪

第一作者：袁三一,王尚旭

论文类型：期刊论文

论文概要：王迪,袁三一,袁焕,曾华会,王尚旭，Intelligent velocity picking based on unsupervised clustering with the adaptive threshold constraint，Acta Geophysica Sinica，2021，v 64,n 3,p1048-1060

论文题目：Intelligent velocity picking based on unsupervised clustering with the adaptive threshold constraint