Frontiers in Earth Science (Jan 2023)
Automatic velocity picking with restricted weighted k-means clustering using prior information
Abstract
Automatic picking of seismic velocity can be performed using k-means clustering. In simple k-means clustering, the number of clusters needs to be predetermined, while the picking result is affected by the initial value of each cluster center. In this study, we present an unsupervised weighted k-means clustering velocity-picking method that picks the centers of the energy clusters instead of the geometric centers of the clusters. This method works on the semblance velocity spectrum and requires an initial velocity function and three user-defined thresholds to limit the search area. The number of cluster centers and their initial times are obtained according to a rectangular signal resulting from the three thresholds, while the initial velocities of the cluster centers can be subsequently obtained using their initial times and the initial velocity function. Inaccurate selection of thresholds may merge two clusters wrongly, in which case only a stronger event is selected. In the weighted k-means clustering algorithm, weights are calculated by using the amplitudes of the velocity points. Meanwhile, points far from the center are gradually removed to ensure that each cluster center coincides with the respective energy cluster center. We also propose a method for ignoring non-primary velocities, such as multiples, by removing points that create sudden changes in the slope of the reference velocity beyond a user-defined limit. The processing of the model and real data show that the proposed seismic velocity-picking method has high efficiency and picking accuracy.
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