AI 中文总结
研究旨在解决地震数据中面波模式分离难题，提出物理感知无监督深度学习框架，利用f-v域自适应高斯掩码在时空域分离不同模式分量，通过物理约束学习逆映射，实验证明该框架能实现可靠模式分离及准确反演。
AI 中文摘要
面波（SW）色散分析在近地表地球物理学和地震学中广泛用于通过测量地震数据中的SW几何色散来确定剪切波速度结构。现有方法中，多通道面波分析（MASW）和双台站方法常用于提取SW反演的色散信息。但地震数据中基模和高阶模共存给这些方法带来挑战。为此提出一种物理感知无监督深度学习框架，它作为深度逆算子，利用在频率-相速度（f-v）域构建的自适应高斯掩码在时空域直接分离基模和高阶模分量。通过在目标掩码内最大化能量集中度并抑制其外的泄漏将物理约束纳入损失函数。通过反向传播，网络学习从f-v域的物理约束到时空域波场分离的逆映射，无需标记训练数据。合成数据和现场数据的数值实验表明，该框架为SW模式分离提供了强大且自动化的解决方案，有助于更可靠地拾取色散曲线并提高后续SW反演的准确性。
英文摘要
Surface-wave (SW) dispersion analysis is widely used in near-surface geophysics and seismology to determine shear-wave velocity structures by measuring SW geometric dispersion in seismic data. Among the available approaches, multichannel analysis of surface waves (MASW) and two-station methods are commonly employed to extract dispersion information for SW inversion. However, the coexistence of fundamental and higher modes in seismic data poses challenges for these methods, particularly for two-station analysis. To separate the different mode components, we propose a physics-aware unsupervised deep-learning framework. The method acts as a deep inverse operator that directly separates fundamental- and higher-mode components in the time-space domain using an adaptive Gaussian mask constructed in the frequency-phase-velocity (f-v) domain. Physical constraints are incorporated into the loss function by maximizing energy concentration within the target mask while suppressing leakage outside it. Through backpropagation, the network learns the inverse mapping from physical constraints in the f-v domain to wavefield separation in the time-space domain without requiring labeled training data. Numerical experiments on both synthetic and field data show that the framework provides a robust and automated solution for SW mode separation, facilitating more reliable dispersion-curve picking and improving the accuracy of subsequent SW inversion.
