Detecting the Bull’s-Eye Effect in Seismic Inversion Low-Frequency Models Using the Optimized YOLOv7 Model

Abstract

To detect bull’s-eye anomalies in low-frequency seismic inversion models, the study proposed an advanced method using an optimized you only look once version 7 (YOLOv7) model. This model is enhanced by integrating advanced modules, including the bidirectional feature pyramid network (BiFPN), weighted intersection-over-union (wise-IoU), efficient channel attention (ECA), and atrous spatial pyramid pooling (ASPP). BiFPN facilitates robust feature extraction by enabling bidirectional information flow across network scales, which enhances the ability of the model to capture complex patterns in seismic inversion models. Wise-IoU improves the precision and fineness of reservoir feature localization through its weighted approach to IoU. Meanwhile, ECA optimizes interactions between channels, which promotes effective information exchange and enhances the overall response of the model to subtle inversion details. Lastly, the ASPP module strategically addresses spatial dependencies at multiple scales, which further enhances the ability of the model to identify complex reservoir structures. By synergistically integrating these advanced modules, the proposed model not only demonstrates superior performance in detecting bull’s-eye anomalies but also marks a pioneering step in utilizing cutting-edge deep learning technologies to enhance the accuracy and reliability of seismic reservoir prediction in oil and gas exploration. The results meet scientific literature standards and provide new perspectives on methodology, which makes significant contributions to ongoing efforts to refine accurate and efficient prediction models for oil and gas exploration.