Seismic Volumetric Dip Estimation via Multichannel Deep Learning Model

Abstract

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 utilize seismic dips calculated by 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 multi-channel 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 by using several traditional methods based on 3D real seismic data as the training labels, which are used to pre-train SM and PM. Then, we propose a workflow to create synthetic seismic data and ground truth dip labels, which are utilized 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 the MCDL by pre-training with the estimated dip volumes from real data. To demonstrate its validity and availability, we apply the MCDL to synthetic data and two 3D real seismic volumes. The qualitative and quantitative comparisons illustrate the superiority of the proposed model over the traditional methods.