Seismic Lithology Prediction via a Lightweight Network With Attention Mechanism

Lithology prediction is essential for the characterization and exploration of oil and gas reservoirs. Recent studies have demonstrated that integrating time‐frequency analysis with deep learning models can enhance lithology prediction performance. However, existing approaches are often constrained by large parameter volumes and high computational complexity, limiting their applicability to large‐scale seismic datasets. Furthermore, lithology samples typically exhibit class imbalance, which leads to insufficient feature learning for minority classes. To address these problems, this paper proposes LightLPNet‐CA, a lightweight network for seismic lithology prediction. The method first applies a time‐frequency transform to generate time‐frequency spectral maps from post‐stack seismic traces, which are used as input to the network. Subsequently, a channel attention mechanism is integrated to enhance discriminative feature representation, along with a class‐balancing strategy to mitigate sample imbalance. With only about 0.1M parameters, the network reduces computational overhead while maintaining predictive performance through its lightweight architecture. Experimental results show that the proposed method achieves higher lithology identification accuracy, particularly excelling in the prediction of minority classes.