利用优化 YOLOv7 模型检测地震反演低频模型中的牛眼效应

IF 0.7 4区 地球科学 Q4 GEOCHEMISTRY & GEOPHYSICS
Jun Li, Jia-bing Meng, Pan Li
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引用次数: 0

摘要

为了检测低频地震反演模型中的靶心异常,该研究提出了一种先进的方法,使用优化的 "你只看一次 "第 7 版(YOLOv7)模型。该模型通过集成双向特征金字塔网络(BiFPN)、加权交叉联合(wise-IoU)、高效通道关注(ECA)和无道空间金字塔池化(ASPP)等先进模块得到了增强。BiFPN 通过实现跨网络尺度的双向信息流,促进了稳健的特征提取,从而增强了模型捕捉地震反演模型中复杂模式的能力。Wise-IoU 通过加权 IoU 方法提高了储层特征定位的精度和精细度。同时,ECA 优化了道间的相互作用,促进了有效的信息交换,增强了模型对微妙反演细节的整体响应。最后,ASPP 模块战略性地解决了多尺度空间依赖性问题,进一步增强了模型识别复杂储层结构的能力。通过对这些先进模块的协同整合,所提出的模型不仅在探测牛眼异常方面表现出卓越的性能,而且在利用前沿深度学习技术提高油气勘探中地震储层预测的准确性和可靠性方面迈出了开创性的一步。该成果符合科学文献标准,并提供了新的方法论视角,为不断完善准确高效的油气勘探预测模型做出了重要贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting the Bull’s-Eye Effect in Seismic Inversion Low-Frequency Models Using the Optimized YOLOv7 Model

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.

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来源期刊
Applied Geophysics
Applied Geophysics 地学-地球化学与地球物理
CiteScore
1.50
自引率
14.30%
发文量
912
审稿时长
2 months
期刊介绍: The journal is designed to provide an academic realm for a broad blend of academic and industry papers to promote rapid communication and exchange of ideas between Chinese and world-wide geophysicists. The publication covers the applications of geoscience, geophysics, and related disciplines in the fields of energy, resources, environment, disaster, engineering, information, military, and surveying.
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