结合SKNet 与U-Net 的盐体识别方法
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程国建(1964—),男,陕西扶风人,教授,博士,从事计算智能、机器学习、人工智能与模式识别、图像处理等工作。E-mail:gjcheng@xsyu.edu.cn。

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国家自然科学基金项目“基于遥感大数据的汾渭平原空气质量时空特征及其驱动力研究与模拟”(62002286),国家自然科学基金青年科学基金项目“基于多核学习的高分辨率光学遥感图像固定结构人造目标检测方法研究”(41301480)。


Salt body recognition method combining SKNet with U-Net
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    摘要:

    地下盐体与油气藏的关系密不可分,盐体的准确识别对油气藏勘探和钻探路径规划具有重要意义。以往的深度学习方法使用固定大小的感受野,不能根据地震图像中盐体的大小动态地调整卷积核来捕捉特征,从而忽略了部分全局信息,导致在盐体边界或狭长处识别效果较差。针对上述问题,在U-Net基础上进行改进,使用SKNet 作为编码器提取盐体特征,其具有动态选择机制,根据输入信息的多个尺度自适应地调整感受野的大小,并结合位置与通道自注意力机制以及超柱体方法进行特征融合。采用改进的U-Net方法对TGS盐体数据集进行评估,取得交并比为85.66%、像素准确率为96.1%的识别效果。

    Abstract:

    The relationship between underground salt bodies and oil and gas reservoirs is inseparable. The accurate recognition of salt bodies is of great significance to the oil and gas reservoir exploration and drilling path planning. In existing deep learning methods,the size of the receptive field is unchanged,and the convolution kernel cannot be dynamically adjusted for the feature capture according to the size of the salt body in a seismic image. As a result,the part of the global information is ignored,which results in poor recognition at the boundaries or narrow areas of the salt bodies. In response to the above problems,this paper proposes a new method based on U-Net with SKNet as the encoder to extract salt body features.It has a dynamic selection mechanism that allows the sizes of the receptive fields to be adjusted adaptively according to multiple scales of the input information. In addition,it combines the position and channel self-attention mechanism and the hyper column method for feature fusion. The improved U-Net method is used to evaluate the TGS salt body data set. The recognition results have an intersection over union(IoU)of 85.66% and a pixel accuracy of 96.1%.

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程国建,刘宁,万晓龙,姚卫华,魏新善.结合SKNet 与U-Net 的盐体识别方法[J].油气地质与采收率,2022,29(1):62~68

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  • 在线发布日期: 2022-03-30