Abstract：The artificial interpretation of seismic facies requires a lot of time and energy and is highly subjective and uncertain, which directly affects the accuracy of seismic data interpretation. Although deep learning algorithms have been widely used in seismic facies classification, due to the diversity of patterns and spatial scales of seismic facies, it remains a challenging task to improve computational efficiency while ensuring high resolution and precision. Therefore, an automatic seismic facies classification method based on LinkNet is proposed. The weighted linear combination of the multi-class cross entropy and Tversky is used as the loss function of network training. Tversky improves the description accuracy of the boundaries of minority classes of seismic facies in the unbalanced data by adjusting the parameters to balance the false positive and false negative classes and improving the recall rate and other indicators. LinkNet's decoding layer shares the learning characteristics of the encoding layer, which makes its structure simpler and greatly raises computational efficiency. The tests on the F3 block of the North Sea in the Netherlands show that the accuracy of the proposed method for describing seismic facies is higher than that of U-Net + PPM, and when faced with unbalanced data, it pays more attention to the minority classes and has better boundary characterization ability. LinkNet has fast computing speed and can run on devices with lower configurations, which is more practical than U-Net + PPM.
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