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Seismic velocity spectrum picking is an important task in seismic exploration. With the development of geological exploration technology, the data of seismic velocity spectrum has experienced explosive growth. Manual picking of seismic velocity spectrum alone gradually cannot meet the needs of practical production. In order to improve efficiency and reduce time costs, there is an urgent need to find a fast and effective method to achieve an automated velocity spectrum picking process. This paper proposes an intelligent picking method based on neural networks. The method abstracts the task of seismic velocity spectrum picking into a visual task, processes it, and outputs candidate boxes through model training. The center points of the candidate boxes are used as the "time-velocity" sequences of seismic velocity spectrum images. To improve the accuracy of model training, a YOLO-based improved YOLO-DCANet network is proposed. In the backbone stage, deformable convolutional modules are introduced, and a Coordinate Attention (CA) mechanism is integrated at different stages of the process. Meanwhile, to learn the features of energy clusters at different levels in the image, a multi-scale model is introduced to match energy cluster information at different levels. The experiments show that the mAP accuracy of the proposed YOLO-DCANET is improved by about 8% compared with the traditional two-stage model, and it’s also improved by about 5% compared with the original YOLO series model. It shows that the seismic velocity spectrum picking algorithm proposed in this paper is significantly improved compared with the traditional deep learning method. In addition, the generalization test on other working areas proves that the proposed method has strong generalization ability and robustness.
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