Accelerating Seismic Dip Estimation With Deep Learning

The seismic volumetric dip is a crucial seismic geometric attribute, which can provide useful information for assisting subsequent processing and interpretation. Waveform similarity scanning-based dip estimation (WSSB) delivers reliable dip estimation but encounters problems of expensive computation. To improve computing efficiency, we use multitask deep learning to simultaneously estimate the inline dip and crossline dip directly from a 3-D field seismic dataset. Our method considers dip estimation as a regression problem and trains a multilayer convolutional neural network with dual-channel output. It aims to output continuous values of seismic apparent dip from two directions simultaneously. To train the network, we propose an effective and efficient workflow to create a training sample dataset, which consists of field seismic cubes and the corresponding dip labels estimated by WSSB. After training, the network automatically learns how to extract rich and proper features that are important for dip estimation. By sliding the extraction window within the full 3-D seismic data, the network can output many overlapping dip cubes that are stacked to get two complete 3-D volumes of seismic dip. The final results of dip estimation by our method are similar to those by WSSB. We further demonstrate the accuracy of our approach by comparing the structural curvature. However, the computation time of our method is much less than that of WSSB. The proposed method can accurately estimate seismic volumetric dips with high computational efficiency.