Seismic attributes play a crucial role in fault interpretation and mapping fracture density. Conventionally, seismic attributes derived from migrated reflections are used for this purpose. The attributes derived from the other counterparts of the recorded wavefield are often ignored and excluded from the categorization. We have performed categorization of the attributes derived from the diffracted part of the wavefield and combine them into a new seismic attribute class, which we call diffractivity attributes. The extraction of diffractivity attributes is based on the 3D Kirchhoff time migration operator that includes a dynamic muting. We distinguish three major classes in the diffractivity attributes, which describe geometric and amplitude properties of the seismic diffractions. We assign point and edge diffraction focusing as well as the azimuth to the geometric class. The amplitudes of the isolated seismic diffractions are used to extract the instantaneous attributes based on the complex-trace approach. The instantaneous amplitudes, phase, frequency, and sweetness build up the instantaneous attribute class. We perform a spectral decomposition of the isolated diffractions into the isofrequencies using the wavelet approach. The isofrequencies compose the spectral-decomposition class. We also link the new diffractivity class to the conventional seismic reflection attributes. We use a deep learning approach based on convolutional neural networks for classifying and correlating the diffractivity attributes.

中文翻译：

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使用自动编码器网络将衍射属性与地震反射属性进行分类和关联

地震属性在断层解释和测绘裂缝密度中起着至关重要的作用。传统上，从迁移的反射中得出的地震属性用于此目的。从记录的波场的其他对应项派生的属性通常会被忽略，并从分类中排除。我们已经对源自波场衍射部分的属性进行了分类，并将它们组合成新的地震属性类，我们将其称为衍射属性。衍射属性的提取基于包含动态静音的3D Kirchhoff时间迁移算子。我们在衍射属性中区分了三个主要类别，它们描述了地震衍射的几何和振幅特性。我们将点和边缘衍射聚焦以及方位角分配给几何类别。孤立的地震衍射的振幅用于基于复迹法提取瞬时属性。瞬时幅度，相位，频率和甜度构成了瞬时属性类。我们使用小波方法将孤立的衍射光谱分解为等频率。等频率组成频谱分解类别。我们还将新的衍射等级与常规地震反射属性关联起来。我们使用基于卷积神经网络的深度学习方法对衍射属性进行分类和关联。孤立的地震衍射的振幅用于基于复迹法提取瞬时属性。瞬时幅度，相位，频率和甜度构成了瞬时属性类。我们使用小波方法将孤立衍射的光谱分解成等频率。等频率组成频谱分解类别。我们还将新的衍射等级与常规地震反射属性关联起来。我们使用基于卷积神经网络的深度学习方法对衍射属性进行分类和关联。孤立的地震衍射的振幅用于基于复迹法提取瞬时属性。瞬时幅度，相位，频率和甜度构成了瞬时属性类。我们使用小波方法将孤立衍射的光谱分解成等频率。等频率组成频谱分解类别。我们还将新的衍射等级与常规地震反射属性关联起来。我们使用基于卷积神经网络的深度学习方法对衍射属性进行分类和关联。我们使用小波方法将孤立衍射的光谱分解成等频率。等频率组成频谱分解类别。我们还将新的衍射等级与常规地震反射属性关联起来。我们使用基于卷积神经网络的深度学习方法对衍射属性进行分类和关联。我们使用小波方法将孤立衍射的光谱分解成等频率。等频率组成频谱分解类别。我们还将新的衍射等级与常规地震反射属性关联起来。我们使用基于卷积神经网络的深度学习方法对衍射属性进行分类和关联。