Siamese-Derived Attention Dense Network for Seismic Impedance Inversion

Abstract

Seismic impedance inversion is essential for providing high-resolution stratigraphic analysis. Therefore, improving the accuracy while ensuring the efficiency of the inversion model is crucial for practical implementation. Recently, deep learning-based approaches have proven superior in capturing complex relationships between different data domains. In this paper, a Siamese-derived attention-dense network (SADN) is proposed, which incorporates both prediction and Siamese modules. In the prediction module, DenseNet serves as the backbone, and a channel attention mechanism is integrated into DenseNet to improve the weight of factors highly correlated with seismic impedance inversion. A bottleneck structure is employed in DenseNet to reduce computational costs. In the Siamese module, a weight-shared DenseNet is employed to compute the distribution similarity between the predicted impedance and the actual impedance, effectively regularizing the distribution similarity between the inverted seismic impedance and the recorded ground truth. The qualitative and quantitative results demonstrate the advantage of the SADN over commonly used traditional networks for seismic impedance inversion.