Seismic Volumetric Dip Estimation via Multichannel Deep Learning Model

Although there are plenty of approaches proposed for addressing seismic volumetric dip estimation, it still suffers from several limitations, for example, the expensive computation cost, the perturbations from sequence stratigraphic anomalies, and the difficulty for handling the complicated geologic structures. Recently, deep learning (DL)-based models have been proposed for seismic dip estimation, which use seismic dips calculated using the traditional methods as the training labels. Apparently, these DL-based models can effectively improve the computational efficiency; however, it still subjects to the limitations of the traditional algorithms. We propose a multichannel deep learning (MCDL) model for implementing seismic volumetric dip estimation, mainly including share module (SM), particular module (PM), and fused module (FM). First, we calculate seismic dips using several traditional methods based on 3-D real seismic data as the training labels, which are used to pretrain SM and PM. Then, we propose a workflow to create synthetic seismic data and ground-truth dip labels, which are used to fine-tune SM/PM and train FM. In this way, we can obtain a DL model by considering both the features of synthetic ground-truth dips and the calculated dips from real data. Moreover, we can effectively enhance the generalization ability of MCDL by pretraining with the estimated dip volumes from real data. To demonstrate its validity and availability, we apply MCDL to synthetic data and two 3-D real seismic volumes. The qualitative and quantitative comparisons illustrate the superiority of the proposed model over the traditional methods.