AI 中文总结
提出基于Mamba的轻量级全局建模框架LMGM，利用线性复杂度、三方向扫描和双域感知块，在三维地震插值中实现高效高性能，并适用于噪声去除。
AI 中文摘要
在地震勘探中，地震波的传播自然会产生地震数据中的长程依赖关系。捕捉这种全局相关性可以显著提高地震信号处理、反演和解释的精度。因此，全局建模（GM）方法已成为地震勘探的一种有效范式，为利用地震数据内在的全局关系提供了有力手段。然而，主流的全局建模方法，尤其是Transformer，因其固有的二次复杂度而带来极高的计算成本，这限制了此类方法向更高维数据的可扩展性。为了实现三维（3D）地震勘探的高效全局建模，我们提出了一个通用框架，名为基于轻量级Mamba的全局建模（LMGM），以相对较低的计算成本建立长程依赖关系。具体而言，LMGM充分利用了Mamba的线性复杂度。设计了一个三方向扫描Mamba块，以利用三维地震数据沿三个维度的空间相关性。同时，进一步开发了一个双域感知块，以融合时域和频域特征，增强特征表示。此外，提出了一种两阶段即插即用的非线性归一化策略，以增强LMGM对不同地震记录的适应性，同时保留信号特征。我们以三维地震数据插值作为代表性任务来全面评估LMGM。结果表明，与基于U-Net和基于Transformer的方法相比，LMGM在显著降低计算成本的同时实现了更优的处理性能。此外，随机噪声去除实验证明了LMGM在插值之外的适用性，支持其作为通用全局建模框架的潜力。
英文摘要
In seismic exploration, the propagation of seismic waves naturally gives rise to long-range dependencies in seismic data. Capturing such global correlations can significantly improve the accuracies of seismic signal processing, inversion, and interpretation. Global modeling (GM) methods have therefore emerged as an effective paradigm for seismic exploration, offering a powerful means of exploiting the intrinsic global relationships within seismic data. However, the mainstream GM approaches, particularly Transformers, incur prohibitively high computational costs for their inherent quadratic complexity, which restricts the scalability of such methods to higher-dimensional data. To achieve efficient GM for three-dimensional (3D) seismic exploration, we propose a general framework, named lightweight Mamba-based global modeling (LMGM), to establish long-range dependencies at a relatively low computational cost. Specifically, LMGM fully leverages the linear complexity of Mamba. A three-directional scanning Mamba block is designed to exploit spatial correlations along the three dimensions of 3D seismic data. Meanwhile, a dual-domain-aware block is further developed to integrate time- and frequency-domain features for enhanced feature representation. In addition, a two-stage plug-and-play nonlinear normalization strategy is proposed to enhance the adaptability of LMGM to diverse seismic records while preserving signal characteristics. We employ 3D seismic data interpolation as a representative task to comprehensively evaluate LMGM. The results demonstrate that LMGM achieves superior processing performance while substantially reducing computational cost compared with U-Net-based and Transformer-based methods. Furthermore, experiments on random noise removal demonstrate the applicability of LMGM beyond interpolation, supporting its potential as a general GM framework.
