Automatic Fresnel zone picking in the dip-angle domain using deep neural networks

Although successful applications of deep neural networks (DNNs) have been shown in many research fields, their application to Fresnel zone picking for Kirchhoff-type migration has not been explored. We investigate the application of DNNs for identifying the left and right boundaries of the Fresnel zone in the dip-angle domain automatically, which leads to an optimal summation for imaging. We use a pair of 1D dip-angle gathers in both inline and crossline directions as the input data to train the DNNs in a supervised way. The goal is to minimize the cost as a function of weights and biases. The trained DNNs can be utilized to automatically extract a set of marks regarding different dip-angle values from a large number of sub-images in dip-angle gathers. Through experiment, we show that a four-layer DNN is enough to extract the features of our training data and meanwhile avoid overfitting in estimating the Fresnel zone range. Finally, we adopt two field data examples, Dashen and Xudong oil field in China, to demonstrate the effectiveness and the generalization ability of the DNNs-based Fresnel zone picking on dip-angle gathers. The results illustrate that the proposed DNN is an efficient automatic data-driven picker needing little human participation.