发表机构
Peking University; Harbin Institute of Technology（北京大学; 哈尔滨工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对勘探地震处理中学习方法难以比较的问题，提出多任务基准SPBench，覆盖六任务、24种方法、10个数据集，并引入SCoRE和RC_norm评估，揭示合成与野外排名差异及退化影响。
AI 中文摘要
勘探地震处理是地下成像和资源勘探的基础，但基于学习的方法在研究间仍难以比较。我们对368篇论文的调查发现，广泛依赖私有或难以复现的数据集，仅25篇提供公开代码。这掩盖了所报告的性能提升究竟源于模型设计还是实验设置。我们提出了地震处理基准（SPBench），涵盖六项任务：随机噪声衰减、道插值、地滚波压制、多次波压制、解混和初至拾取。我们在10个数据集上、43种标准化设置下复现了24种监督方法，并发布了数据集、实现、配置、评估脚本和结果。为补充全局分数和逐道拾取误差，我们引入了面向重建的信号分量解析评估（SCoRE）以及用于初至拾取的无参考脊曲率分数（RC_norm）。我们的分析表明，合成数据上的排名无法可靠预测野外数据上的排名，当模型在各设置内训练时，任务间存在依任务而定的一致性。随着退化增强，在相干地滚波下的排名重排比在类随机干扰下更为显著。脊曲率分数与基于MAE的模型排名在所评估设置中一致，三个野外测区的平均Kendall相关系数为0.881，而SCoRE揭示了全局分数所隐藏的与频率和能量相关的差异。SPBench为比较基于学习的地震处理方法提供了可复现的基础，并刻画了其相对优势如何随数据设置、退化强度和评估标准而变化。
英文摘要
Exploration seismic processing underpins subsurface imaging and resource exploration, but learning-based methods remain difficult to compare across studies. Our survey of 368 papers finds widespread reliance on private or difficult-to-reproduce datasets, with only 25 providing public code. This obscures whether reported gains arise from model design or experimental settings. We introduce the Seismic Processing Benchmark (SPBench), covering six tasks: random noise attenuation, trace interpolation, ground-roll suppression, multiple suppression, deblending, and first-arrival picking. We reproduce 24 supervised methods on 10 datasets under 43 standardized settings and release datasets, implementations, configurations, evaluation scripts, and results. To complement global scores and per-trace pick errors, we introduce signal-component-resolved evaluation (SCoRE) for reconstruction and a reference-free ridge-curvature score (RC_norm) for first-arrival picking. Our analyses show that synthetic rankings do not reliably predict field rankings, with task-dependent agreement when models train within each setting. As degradation strengthens, rankings reorder more under coherent ground roll than under random-like interference. The ridge score agrees with MAE-based model rankings in the evaluated settings, with a mean Kendall correlation of 0.881 across three field surveys, while SCoRE reveals frequency- and energy-dependent differences hidden by global scores. SPBench provides a reproducible basis for comparing learning-based seismic processing methods and characterizes how their relative advantages vary across data settings, degradation strengths, and evaluation criteria.
