AI 中文总结
该研究提出适配叠前地震数据的GeoFormer，通过三种几何注入机制实现道级分词，在四个野外数据集的初至波拾取任务中，其性能优于视觉Transformer及特定任务基线，且强噪声下精度更稳健。
AI 中文摘要
我们提出GeoFormer，一种专为叠前地震数据设计的几何感知Transformer架构。与从二维图像块提取token并主要编码视觉模式的视觉Transformer不同，GeoFormer通过明确纳入采集几何信息来适配叠前地震数据。每条地震道由包含波形和四个源-接收器坐标的5D单元表示，从中可导出两个几何属性：偏移距和相对高程，其中相对高程为接收器高程减去源高程。因此，GeoFormer执行道级分词，每个token结合波形与这些几何属性。为利用这些几何属性，GeoFormer在Transformer流程的不同层级引入三种几何注入机制：在token层级，GeomMLP用由偏移距和高程导出的逐道几何表示替代分类token；在归一化层级，GeomAdaLN用几何条件特征调制替代统一层归一化；在注意力层级，GeomAttnBias注入无参数物理先验，即几何上邻近的道应相互更多关注。我们在初至波拾取这一严重依赖采集几何的典型地震处理任务上验证GeoFormer。对四个野外数据集的实验表明，GeoFormer的性能优于视觉Transformer和两个特定任务基线。至关重要的是，由于其道级token编码与波形质量无关的物理先验几何属性，GeoFormer在强噪声下仍能保持稳健的拾取精度。 ablation研究证实，GeomMLP、GeomAdaLN和GeomAttnBias各自对GeoFormer的拾取精度有贡献。
英文摘要
We propose GeoFormer, a Geometry-Aware Transformer architecture specifically designed for prestack seismic data. Unlike Vision Transformer, whose tokens are extracted from 2D patches and primarily encode visual patterns, GeoFormer is designed for prestack seismic data by explicitly incorporating acquisition geometry. Each seismic trace is represented by a 5D unit consisting of the waveform and four source-receiver coordinates, from which two geometric attributes are derived: offset and relative elevation, where relative elevation is the receiver elevation minus the source elevation. GeoFormer therefore performs trace-level tokenization, where each token combines the waveform with these geometric attributes. To exploit these geometric attributes, GeoFormer introduces three geometry injection mechanisms operating at different levels of the Transformer pipeline. At the token level, GeomMLP replaces the classification token with a per-trace geometric representation derived from offset and elevation. At the normalization level, GeomAdaLN replaces uniform layer normalization with geometry-conditioned feature modulation. At the attention level, GeomAttnBias injects a parameter-free physical prior that geometrically proximate traces should attend more to each other. We validate GeoFormer on first-arrival picking, a representative seismic processing task that relies heavily on acquisition geometry. Experiments on four field datasets demonstrate that GeoFormer outperforms Vision Transformer and two task-specific baselines. Crucially, GeoFormer maintains robust picking accuracy under strong noise because its trace-level tokens encode geometric attributes that provide a physical prior independent of waveform quality. Ablation studies confirm that GeomMLP, GeomAdaLN, and GeomAttnBias each contribute to GeoFormer's picking accuracy.
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