标题： DispFormer：用于从全球合成到区域应用的灵活色散曲线反演的预训练变换器 显示英文标题

标题： DispFormer: Pretrained Transformer for Flexible Dispersion Curve Inversion from Global Synthesis to Regional Applications

摘要： 表面波频散曲线反演对于估算地下剪切波速度（$v_s$）至关重要，但传统方法常常难以在计算效率和反演精度之间取得平衡。 虽然深度学习方法显示出潜力，但以往的研究通常需要大量的标记数据，并且在处理具有不同周期范围、缺失数据和低信噪比的真实数据集时存在困难。 本研究提出了DispFormer，一种基于变压器的神经网络，用于从瑞利波相位和群频散曲线反演$v_s$剖面。 DispFormer独立处理每个周期的频散数据，从而使其能够处理不同长度的数据，而无需修改网络或在训练和测试数据之间进行对齐。 通过在全局合成数据集上进行预训练，并使用零样本和少量样本策略在两个区域合成数据集上进行测试，展示了其性能。 结果表明，即使没有任何标记数据，零样本DispFormer生成的反演剖面也能很好地与真实值匹配，为传统方法提供了一个可部署的初始模型生成器。 当有标记数据时，少量样本的DispFormer仅需少量标记即可优于传统方法。 此外，真实数据测试表明，DispFormer能够有效处理不同长度的数据，并且产生的数据残差低于参考模型。 这些发现表明，DispFormer为频散曲线反演提供了稳健的基础模型，并且是更广泛应用的有前景的方法。 显示英文摘要

摘要： Surface wave dispersion curve inversion is essential for estimating subsurface Shear-wave velocity ($v_s$), yet traditional methods often struggle to balance computational efficiency with inversion accuracy. While deep learning approaches show promise, previous studies typically require large amounts of labeled data and struggle with real-world datasets that have varying period ranges, missing data, and low signal-to-noise ratios. This study proposes DispFormer, a transformer-based neural network for inverting the $v_s$ profile from Rayleigh-wave phase and group dispersion curves. DispFormer processes dispersion data at each period independently, thereby allowing it to handle data of varying lengths without requiring network modifications or alignment between training and testing data. The performance is demonstrated by pre-training it on a global synthetic dataset and testing it on two regional synthetic datasets using zero-shot and few-shot strategies. Results indicate that zero-shot DispFormer, even without any labeled data, produces inversion profiles that match well with the ground truth, providing a deployable initial model generator to assist traditional methods. When labeled data is available, few-shot DispFormer outperforms traditional methods with only a small number of labels. Furthermore, real-world tests indicate that DispFormer effectively handles varying length data, and yields lower data residuals than reference models. These findings demonstrate that DispFormer provides a robust foundation model for dispersion curve inversion and is a promising approach for broader applications.