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

Applied Geophysics 2024年第4期21卷 766-776,880,881页

作者： Jun Li Jia-bing Meng Pan Li Institute of Disaster Prevention Sanhe 065201China Hebei Province University Smart Emergency Application Technology Research and Development Center Sanhe 065201China

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 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 fl ow across network scales,which enhances the ability of the model to capture complex patterns in seismic inversion ***-IoU improves the precision and fi neness of reservoir feature localization through its weighted approach to ***,ECA optimizes interactions between channels,which promotes eff ective information exchange and enhances the overall response of the model to subtle inversion ***,the ASPP module strategically addresses spatial dependencies at multiple scales,which further enhances the ability of the model to identify complex reservoir *** 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 *** results meet scientifi c literature standards and provide new perspectives on methodology,which makes signifi cant contributions to ongoing eff orts to refi ne accurate and effi cient prediction models for oil and gas exploration.

关键词： bull’s-eye YOLO bidirectional feature pyramid network weighted intersection-over-union atrous spatial pyramid pooling

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