标题： 基于强空间特征约束的AVO反演半监督学习 显示英文标题

标题： Semi-Supervised Learning for AVO Inversion with Strong Spatial Feature Constraints

摘要： 一维卷积是叠前振幅随偏移距变化（AVO）反演中广泛使用的一种深度学习技术；然而，它缺乏横向连续性。虽然二维卷积提高了横向连续性，但由于测井数据的稀疏性，模型仅能学习到微弱的空间特征，并未能充分探索地震数据中的空间相关性。为克服这些挑战，我们提出了一种基于带有强空间特征约束的半监督学习的新AVO反演方法（SSFC-SSL）。首先，通过反演网络获得二维预测值，并使用测井标签对测井位置的预测值进行稀疏表示。随后，引入了一个标签湮灭算子，使非测井位置的预测值能够通过神经网络学习测井位置的空间特征。最终，实现了非测井位置与测井位置之间的双向强空间特征映射。此外，为了减少对测井标签的依赖，我们将半监督学习策略与低频模型相结合，进一步增强了该方法的鲁棒性。在合成示例和实际数据上的实验结果表明，与一维和二维深度学习技术相比，所提出的方法显著提高了横向连续性和反演精度。 显示英文摘要

摘要： One-dimensional convolution is a widely used deep learning technique in prestack amplitude variation with offset (AVO) inversion; however, it lacks lateral continuity. Although two-dimensional convolution improves lateral continuity, due to the sparsity of well-log data, the model only learns weak spatial features and fails to explore the spatial correlations in seismic data fully. To overcome these challenges, we propose a novel AVO inversion method based on semi-supervised learning with strong spatial feature constraints (SSFC-SSL). First, two-dimensional predicted values are obtained through the inversion network, and the predicted values at well locations are sparsely represented using well-log labels. Subsequently, a label-annihilation operator is introduced, enabling the predicted values at non-well locations to learn the spatial features of well locations through the neural network. Ultimately, a two-way strong spatial feature mapping between non-well locations and well locations is achieved. Additionally, to reduce the dependence on well-log labels, we combine the semi-supervised learning strategy with a low-frequency model, further enhancing the robustness of the method. Experimental results on both synthetic example and field data demonstrate that the proposed method significantly improves lateral continuity and inversion accuracy compared to one- and two-dimensional deep learning techniques.