Majority of the deep learning techniques in seismic image analysis focus on solving one task at a time and ignore the richness of presence of many other structures in the vicinity and their correlation with the task of interest at hand. These approaches work best in solving the identification of simple structures in the shallow areas of the survey where the signal-to-noise ratio is high and struggle in deeper areas as the signal becomes weaker. In addition, it is a challenge to acquire the right data and quality labels to train the deep learning models for some of the fundamental challenges in geoscience. In this paper, we present two recent applications of deep learning in seismic processing that are targeted to reduce the cycle time of seismic processing projects. In the first example, we present the concept of learning multiple related tasks and demonstrate on a use case that the multi-task learning learns common representations of related tasks (salt body and salt boundary) and improves the accuracy of identifying and segmenting salt boundary structures even in low signal-to-noise areas. In the second example, we reconstruct the regularly and irregularly missing in narrow-azimuth data using an encoder-decoder style convolutional neural network.


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