The "Bull's-Eye" effect in low-frequency seismic inversion models severely impacts the accuracy and reliability of inversion results, particularly under conditions of low contrast, blurred boundaries, and complex geological backgrounds, where precise identification becomes challenging. To address this issue, this paper proposes a lightweight and highly robust object detection model, MWLB-YOLOv11. Based on YOLOv11, the model integrates four key modules to enhance detection performance. By adopting MobileNetV4 as the backbone network, the model benefits from a deeper architecture and parameter optimization to improve feature extraction capabilities. To expand the receptive field and capture low-frequency structural information, the C3k2-WTConv module is introduced, while avoiding excessive parameterization. Additionally, a Large Separable Kernel Attention (LSKAttention) mechanism is incorporated to further enhance spatial context modeling while maintaining computational efficiency. The BiFPN feature fusion structure facilitates information exchange across multiple scales, improving performance in small object detection. The model is optimized specifically for detecting ring-shaped "Bull's-Eye" anomalies in seismic images. Experimental results demonstrate that MWLB-YOLOv11 outperforms traditional methods in terms of accuracy, efficiency, and robustness, enabling more precise detection and localization of Bull's-Eye regions. This model offers an effective solution for seismic inversion analysis and other anomaly detection tasks, showcasing broad application potential.
Key words
Seismic Inversion /
Bull's-Eye Effect /
YOLOv11 /
MobileNetV4 /
WTConv /
LSKAttention /
BiFPN
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