Dip angle extraction and application based on dip gathers and deep learning

Various emerging seismic data processing and interpretating techniques require dip angle of the strata as a priori information. Most traditional dip angle estimation methods rely on imaging profiles, which are inevitably affected by imaging quality. Moreover, the three-dimensional estimation of spatial dip angle relys on the image cube formed by multiple imaging sections. The 2D dip gathers can be used to estimate the dip because the position of the elliptical reflection is indicative of the 3D dip, which not only avoids the influence of low-quality imaging sections, but also realizes dip estimation on a single imaging line. However, this method relies on personal experience and consumes excessive manpower and time. Dip gathers in a seismic prospecting can provide sufficient sample support for data-driven deep learning algorithms to extract complex feature. Therefore, by introducing deep learning algorithm on the basis of 2D dip gathers. So by introducing deep learning algorithm, we come up with a dip angle automatic pickup method on the basis of 2D dip angle gathers and VGG network, and then apply the result in the estimation of Fresnel-zone-related aperture. Finally, the accuracy of dip angle estimation and the generalization performance of the network are verified by the practical data from an oil field, and the calculated aperture is then applied in stationary phase migration to verify the effectiveness of the application of the predicted dip angle in actual data.