发表机构
Institute of Geophysics, China Earthquake Administration; Laboratory of Seismology and Physics of Earth’s Interior, School of Earth and Space Sciences, University of Science and Technology of China; Institute of Advanced Technology, University of Science and Technology of China; University of Chinese Academy of Sciences（中国地震局地球物理研究所; 中国科学技术大学地球和空间科学学院地震学与地球内部物理实验室; 中国科学技术大学先进技术研究院; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过相位拾取和频散任务检验高信噪比筛选是否提升地震深度学习训练效果，发现收益取决于任务、训练计划和测试域，但保留全部有效数据可降低3.0%误差，高SNR并非可靠的数据筛选标准。
AI 中文摘要
硬信噪比（SNR）筛选会移除微弱的地震记录，但保留更清晰的波形是否足以弥补被丢弃数据所带来的学习提升？我们在相位拾取和环境噪声频散两个任务中检验了这一问题，首先匹配训练数量，然后在固定计算量下恢复全部符合条件的频散数据池。相位拾取的筛选在微调或随机初始化下均未带来一致的未过滤测试集上的收益，包括一个在距离和相位组成上匹配的对照组。频散筛选在原始的五个训练轮次终点增加了误差，但在更长时间训练后，相较于同等规模的未筛选数据集有所改进。保留全部29,788条符合条件的拟合路径，而非随机采样的9,929条路径，在相同的更新预算下，三个随机种子的未过滤测试误差降低了3.0%。全可用数据训练与严格SNR训练的平均误差相近，分别为0.0405和0.0406 km/s，但种子配对排序结果混合；严格训练在SNR选择的测试集上保持了9.9%的优势。在为期两天的监测中，SNR调节了拾取流，但事件恢复取决于其测量定义。高SNR并不能可靠地识别出更好的训练数据：收益取决于任务、训练计划和测试领域，而固定数量的比较忽略了保留额外有效样本的益处。
英文摘要
Hard signal-to-noise ratio (SNR) screening removes weak seismic records, but does retaining clearer waveforms improve learning enough to compensate for the discarded data? We test this question in phase picking and ambient-noise dispersion, first matching training counts and then restoring the full eligible dispersion pool at fixed compute. Phase-picking screening gave no consistent unfiltered-test benefit under fine-tuning or random initialization, including a control matched on distance and phase composition. Dispersion screening increased error at the original five-epoch endpoint but improved on an equally sized unscreened set after longer training. Retaining all 29,788 eligible fit paths instead of 9,929 randomly sampled paths reduced unfiltered-test error by 3.0% across three seeds at the same update budget. Full-available and strict-SNR training then had similar mean errors, 0.0405 and 0.0406 km s$^{-1}$, with mixed seed-paired ordering; strict training retained a 9.9% advantage on the SNR-selected test. In two-day monitoring, SNR regulated pick streams but event recovery depended on its measurement definition. High SNR did not reliably identify better training data: gains depended on the task, training schedule, and test domain, and fixed-count comparisons omitted the benefit of retaining additional valid examples.
Comments32 pages, including supplementary material
