标题： 基于注意力的双分支双反演网络的声学阻抗预测 显示英文标题

标题： Acoustic Impedance Prediction Using an Attention-Based Dual-Branch Double-Inversion Network

摘要： 地震阻抗反演是一种广泛用于储层特征描述的技术。 准确的高分辨率地震阻抗数据是后续储层解释的基础。 深度学习方法在地震阻抗反演中展现了显著的潜力。 传统的单分支半监督网络，直接输入原始地震测井数据，难以捕捉高频弱信号。 这一限制导致反演结果分辨率低，准确性与稳定性不足。 此外，地震子波不确定性进一步限制了这些方法在实际地震数据中的应用。 为解决这些挑战，我们提出 ADDIN-I：一种基于注意力机制的双分支双反演网络用于阻抗预测。 ADDIN-I的双分支结构克服了单分支半监督网络的局限性，并提高了序列建模中高频弱信号特征的提取能力。 该网络结合了注意力机制以进一步增强其特征提取能力。 为了使该方法适应实际地震数据的应用，采用了一个深度学习正演算子来自适应地拟合子波。 ADDIN-I在合成数据和实际数据应用中均表现出色。 显示英文摘要

摘要： Seismic impedance inversion is a widely used technique for reservoir characterization. Accurate, high-resolution seismic impedance data form the foundation for subsequent reservoir interpretation. Deep learning methods have demonstrated significant potential in seismic impedance inversion. Traditional single semi-supervised networks, which directly input original seismic logging data, struggle to capture high-frequency weak signals. This limitation leads to low-resolution inversion results with inadequate accuracy and stability. Moreover, seismic wavelet uncertainty further constrains the application of these methods to real seismic data. To address these challenges, we propose ADDIN-I: an Attention-based Dual-branch Double-Inversion Network for Impedance prediction. ADDIN-I's dual-branch architecture overcomes the limitations of single-branch semi-supervised networks and improves the extraction of high-frequency weak signal features in sequence modeling. The network incorporates an attention mechanism to further enhance its feature extraction capabilities. To adapt the method for real seismic data applications, a deep learning forward operator is employed to fit the wavelet adaptively. ADDIN-I demonstrates excellent performance in both synthetic and real data applications.