Abstract
Salt bodies identification is one of the key problems in seismic interpretation, which has important research significance. The traditional salt bodies extraction problem is affected by the noise of seismic data and the level of processing personnel, and the extraction efficiency and results are not very perfect. With the development of deep learning, Convolutional Neural Networks (CNNs) is widely used in geological exploration and seismic data interpretation. However, seismic data is often lack of corresponding label data, and there are still some difficulties in processing it by using deep learning method. To solve this problem, we propose an interactive salt bodies interpretation method based on CNN and graph cut algorithm, and verified its effectiveness on the data set provided by TGS salt identification challenge on the kaggle platform. Field experiments show that the proposed method can interactively detect salt bodies.
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